diff --git a/.gitignore b/.gitignore
index b33e085fdf..9e075536c3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,8 @@
 # pkg and bin directories currently contain build artifacts
 # only so we exclude them.
 bin/
+!pkg/data_cache/src/head/bin/
+!pkg/data_cache/src/worker/bin/
 manifests/external-crds/
 
 # IDEs
@@ -20,9 +22,17 @@ artifacts
 __pycache__/
 *.egg-info/
 
+# Environment variables
+.env
+
 # Coverage
 cover.out
 
 # Helm
 charts/kubeflow-trainer/charts/
 charts/kubeflow-trainer/Chart.lock
+
+# data_cache
+pkg/data_cache/target
+pkg/data_cache/src/client/target/
+hack/data_cache/*.log
diff --git a/cmd/data_cache/Dockerfile b/cmd/data_cache/Dockerfile
new file mode 100644
index 0000000000..7229371e07
--- /dev/null
+++ b/cmd/data_cache/Dockerfile
@@ -0,0 +1,50 @@
+FROM rust:1.85-bullseye AS builder
+
+WORKDIR /workspace
+
+ENV RUST_LOG=info
+
+# Install system dependencies
+RUN apt-get update && \
+    apt-get -y install libssl-dev openssl zlib1g zlib1g-dev libpq-dev cmake protobuf-compiler netcat curl && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install cargo-chef for better caching
+RUN rustup update && cargo install cargo-chef --version 0.1.62
+
+# Copy manifests for dependency caching
+COPY pkg/data_cache/Cargo.toml pkg/data_cache/Cargo.lock ./
+
+# Cache dependencies
+RUN cargo fetch
+
+# Copy source code
+COPY pkg/data_cache/ .
+
+# Run tests
+RUN cargo test --tests
+
+# Build binaries in release mode
+RUN cargo build --release --bin head --bin worker
+
+# Stage 2: Create a minimal runtime image
+FROM debian:bookworm-slim AS runtime
+
+# Install runtime dependencies
+RUN apt-get update && \
+    apt-get -y install ca-certificates && \
+    rm -rf /var/lib/apt/lists/*
+
+# Copy binaries from builder stage
+COPY --from=builder /workspace/target/release/head /usr/local/bin/head
+COPY --from=builder /workspace/target/release/worker /usr/local/bin/worker
+
+# Create non-root user
+RUN groupadd -r cache_user && useradd -r -g cache_user cache_user
+
+# Change ownership and switch to non-root user
+RUN chown -R cache_user:cache_user /usr/local/bin/
+USER cache_user
+
+# Set default command
+CMD ["head"]
diff --git a/hack/data_cache/run_with_remote_table.sh b/hack/data_cache/run_with_remote_table.sh
new file mode 100755
index 0000000000..a3acca0877
--- /dev/null
+++ b/hack/data_cache/run_with_remote_table.sh
@@ -0,0 +1,88 @@
+#!/bin/bash
+
+# Check for required arguments
+if [ $# -lt 5 ]; then
+    echo "Usage: $0 <iam-role-arn> <metadata-loc> <table-name> <schema-name> <aws-profile> [environment]"
+    exit 1
+fi
+
+IAM_ROLE_ARN="$1"
+METADATA_LOC="$2"
+TABLE_NAME="$3"
+SCHEMA_NAME="$4"
+AWS_PROFILE="$5"
+ENVIRONMENT="${6:-LOCAL}"
+
+echo "Using IAM Role: $IAM_ROLE_ARN"
+echo "Metadata Location: $METADATA_LOC"
+echo "Table Name: $TABLE_NAME"
+echo "Schema Name: $SCHEMA_NAME"
+echo "Environment: $ENVIRONMENT"
+echo "AWS Profile: $AWS_PROFILE"
+
+role_output=$(aws sts assume-role --role-arn "$IAM_ROLE_ARN" --role-session-name "RoleSession1" --profile "$AWS_PROFILE")
+
+# Parse the JSON output using jq
+export AWS_ACCESS_KEY_ID=$(echo $role_output | jq -r '.Credentials.AccessKeyId')
+export AWS_SECRET_ACCESS_KEY=$(echo $role_output | jq -r '.Credentials.SecretAccessKey')
+export AWS_SESSION_TOKEN=$(echo $role_output | jq -r '.Credentials.SessionToken')
+export AWS_REGION=us-west-2
+export AWS_EC2_METADATA_DISABLED=true
+
+# Set required environment variables for testing
+export METADATA_LOC="$METADATA_LOC"
+export TABLE_NAME="$TABLE_NAME"
+export SCHEMA_NAME="$SCHEMA_NAME"
+export RUNTIME_ENV="$ENVIRONMENT"
+
+# Function to cleanup processes on exit
+cleanup() {
+    echo ""
+    echo "Stopping services..."
+    kill -9 $WORKER1_PID $WORKER2_PID $HEAD_PID 2>/dev/null || true
+    wait $WORKER1_PID $WORKER2_PID $HEAD_PID 2>/dev/null || true
+    rm -rf /tmp/test_metadata
+    exit 0
+}
+
+# Set up signal handlers for graceful shutdown
+trap cleanup SIGINT SIGTERM
+
+# Function to check if a service is ready
+check_service_ready() {
+    local host=$1
+    local port=$2
+    local service_name=$3
+    
+    echo "Waiting for $service_name to be ready on $host:$port..."
+    while ! nc -z "$host" "$port" 2>/dev/null; do
+        echo "  $service_name not ready yet, waiting 2 seconds..."
+        sleep 2
+    done
+    echo "  $service_name is ready!"
+}
+
+echo "Starting worker node 1..."
+cargo run --bin worker -- 0.0.0.0 50052 > worker1.log 2>&1 &
+WORKER1_PID=$!
+
+echo "Starting worker node 2..."
+cargo run --bin worker -- 0.0.0.0 50053 > worker2.log 2>&1 &
+WORKER2_PID=$!
+
+# Wait for both workers to be ready
+check_service_ready localhost 50052 "worker1"
+check_service_ready localhost 50053 "worker2"
+
+echo "Both workers are ready, starting head node..."
+cargo run --bin head -- 0.0.0.0 50051 > head.log 2>&1 &
+HEAD_PID=$!
+
+check_service_ready localhost 50051 "head"
+
+echo "All services are running. Press Ctrl+C to stop all services."
+wait
+
+#echo "Running client test..."
+#cd src/client && cargo run 2>&1
+#CLIENT_EXIT_CODE=$?
\ No newline at end of file
diff --git a/pkg/data_cache/Cargo.lock b/pkg/data_cache/Cargo.lock
new file mode 100644
index 0000000000..042cc6c0d4
--- /dev/null
+++ b/pkg/data_cache/Cargo.lock
@@ -0,0 +1,5541 @@
+# This file is automatically @generated by Cargo.
+# It is not intended for manual editing.
+version = 4
+
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diff --git a/pkg/data_cache/Cargo.toml b/pkg/data_cache/Cargo.toml
new file mode 100644
index 0000000000..02affb4c22
--- /dev/null
+++ b/pkg/data_cache/Cargo.toml
@@ -0,0 +1,37 @@
+[package]
+name = "arrow_cache"
+version = "0.1.0"
+edition = "2024"
+
+[dependencies]
+tracing = "0.1.41"
+tracing-subscriber = "0.3"
+tokio = "1.41.1"
+datafusion = "47.0.0"
+arrow = "55.0.0"
+arrow-schema = "55.0.0"
+serde = { version = "1.0.188", features = ["derive"] }
+serde_json = "1.0"
+async-trait = "0.1.86"
+iceberg-datafusion = "0.6.0"
+iceberg = "0.6.0"
+futures = "0.3.31"
+arrow-flight = "55.0.0"
+tonic = "0.12.3"
+bytes = "1.9.0"
+bincode = "1.3"
+trust-dns-resolver = "*"
+object_store = { version = "0.12.1", features = ["aws"] }
+url = "2.5.4"
+fastrand = "2.1.1"
+
+[dev-dependencies]
+tokio = { version = "1.41.1", features = ["full"] }
+
+[[bin]]
+name = "head"
+path = "src/head/bin/main.rs"
+
+[[bin]]
+name = "worker"
+path = "src/worker/bin/main.rs"
diff --git a/pkg/data_cache/OWNERS b/pkg/data_cache/OWNERS
new file mode 100644
index 0000000000..4761918f0f
--- /dev/null
+++ b/pkg/data_cache/OWNERS
@@ -0,0 +1,2 @@
+approvers:
+  - akshaychitneni
diff --git a/pkg/data_cache/README.md b/pkg/data_cache/README.md
new file mode 100644
index 0000000000..852b6877c5
--- /dev/null
+++ b/pkg/data_cache/README.md
@@ -0,0 +1,89 @@
+# Kubeflow Data Cache
+
+## Development Setup
+
+### Build the project
+```bash
+cargo build
+```
+
+### Build in release mode
+```bash
+cargo build --release
+```
+
+## Docker Build Instructions
+
+### Build the Docker image
+```bash
+docker build -f cmd/data_cache/Dockerfile -t arrow_cache .
+```
+
+### Run the head service
+```bash
+docker run -p 50051:50051 arrow_cache head
+```
+
+### Run the worker service
+```bash
+docker run -p 50052:50052 arrow_cache worker
+```
+
+## Running the System
+
+### Option 1: Remote Table Testing
+
+Run the system with remote table configuration using IAM roles:
+
+```bash
+../../hack/data_cache/run_with_remote_table.sh <iam-role-arn> <metadata-loc> <table-name> <schema-name> <aws-profile> [environment]
+```
+
+**Example:**
+```bash
+../../hack/data_cache/run_with_remote_table.sh \
+  arn:aws:iam::<account_id>:role/<role_name> \
+  s3a://<metadata_file_path> \
+  <table_name> \
+  <schema_name> \
+  <account_id> \
+  LOCAL
+```
+
+**Parameters:**
+- `iam-role-arn` (required): IAM role ARN for AWS access
+- `metadata-loc` (required): S3 location of the metadata file
+- `table-name` (required): Name of the table
+- `schema-name` (required): Name of the schema
+- `aws-profile` (required): AWS profile name
+- `environment` (optional): Runtime environment (defaults to "LOCAL")
+
+This script will:
+1. Assume the specified IAM role
+2. Set up AWS credentials and environment variables
+3. Start two worker nodes (ports 50052, 50053)
+4. Start the head node (port 50051)
+5. Wait for all services to be ready
+
+Press `Ctrl+C` to stop all services.
+
+## Testing
+
+### Run Client Test
+```bash
+cd src/client
+cargo run --bin client -- --endpoint http://localhost:50051 --local-rank 2 --world-size 4
+```
+
+## Environment Configuration
+
+The system supports two runtime environments:
+- **Local Development**: Set `RUNTIME_ENV=LOCAL` to use localhost workers on ports 50052/50053
+- **Kubernetes/LWS**: Uses `LWS_LEADER_ADDRESS` and `LWS_GROUP_SIZE` for service discovery
+
+## Prerequisites
+
+- Rust and Cargo
+- AWS CLI configured with appropriate credentials
+- `jq` for JSON parsing
+- `nc` (netcat) for service health checks
diff --git a/pkg/data_cache/src/client/Cargo.lock b/pkg/data_cache/src/client/Cargo.lock
new file mode 100644
index 0000000000..bf26e2f88d
--- /dev/null
+++ b/pkg/data_cache/src/client/Cargo.lock
@@ -0,0 +1,1844 @@
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+]
+
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+]
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+ "windows_aarch64_gnullvm 0.52.6",
+ "windows_aarch64_msvc 0.52.6",
+ "windows_i686_gnu 0.52.6",
+ "windows_i686_gnullvm 0.52.6",
+ "windows_i686_msvc 0.52.6",
+ "windows_x86_64_gnu 0.52.6",
+ "windows_x86_64_gnullvm 0.52.6",
+ "windows_x86_64_msvc 0.52.6",
+]
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+ "windows-link",
+ "windows_aarch64_gnullvm 0.53.0",
+ "windows_aarch64_msvc 0.53.0",
+ "windows_i686_gnu 0.53.0",
+ "windows_i686_gnullvm 0.53.0",
+ "windows_i686_msvc 0.53.0",
+ "windows_x86_64_gnu 0.53.0",
+ "windows_x86_64_gnullvm 0.53.0",
+ "windows_x86_64_msvc 0.53.0",
+]
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+
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+[[package]]
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+
+[[package]]
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+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1b9b4fd18abc82b8136838da5d50bae7bdea537c574d8dc1a34ed098d6c166f0"
+dependencies = [
+ "byteorder",
+ "zerocopy-derive",
+]
+
+[[package]]
+name = "zerocopy-derive"
+version = "0.7.35"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn",
+]
diff --git a/pkg/data_cache/src/client/Cargo.toml b/pkg/data_cache/src/client/Cargo.toml
new file mode 100644
index 0000000000..a97487c0e9
--- /dev/null
+++ b/pkg/data_cache/src/client/Cargo.toml
@@ -0,0 +1,21 @@
+[package]
+name = "client"
+version = "0.1.0"
+edition = "2024"
+
+[dependencies]
+futures = "0.3.31"
+arrow-flight = "55.0.0"
+tokio = { version = "1.41.1", features = ["rt-multi-thread", "macros"] }
+tonic = "0.12.3"
+bytes = "1.9.0"
+bincode = "1.3"
+serde = { version = "1.0.215", features = ["derive"] }
+tracing = "0.1.41"
+tracing-subscriber = "0.3.19"
+clap = { version = "4.0", features = ["derive", "env"] }
+
+
+[[bin]]
+name = "client"
+path = "src/main.rs"
diff --git a/pkg/data_cache/src/client/src/main.rs b/pkg/data_cache/src/client/src/main.rs
new file mode 100644
index 0000000000..9e4f4f809e
--- /dev/null
+++ b/pkg/data_cache/src/client/src/main.rs
@@ -0,0 +1,108 @@
+//! Sample client for validating access to cached datasets in the Arrow-based caching system.
+//!
+//! This client demonstrates how to connect to the head node and stream cached data
+//! from worker nodes using Apache Arrow Flight protocol. It serves as a reference
+//! implementation for accessing and consuming distributed cached datasets.
+
+use arrow_flight::decode::FlightRecordBatchStream;
+use futures::stream::StreamExt;
+use arrow_flight::flight_service_client::FlightServiceClient;
+use tonic::transport::Channel;
+use arrow_flight::FlightDescriptor;
+use futures::TryStreamExt;
+use serde::{Deserialize, Serialize};
+use tracing::{info, error};
+use tracing_subscriber;
+use clap::Parser;
+
+/// Index pair structure for dataset partitioning
+#[derive(Serialize, Deserialize, Debug)]
+struct IndexPair {
+    start: u64,
+    end: u64,
+}
+
+/// Command line arguments for the data cache client
+#[derive(Parser, Debug)]
+#[command(name = "data-cache-client")]
+#[command(about = "Sample client for accessing cached datasets via Arrow Flight protocol")]
+struct Args {
+    /// Head node endpoint URL
+    #[arg(long, env = "HEAD_NODE_ENDPOINT", default_value = "http://localhost:50051")]
+    endpoint: String,
+
+    /// Local rank for this client instance
+    #[arg(long, env = "LOCAL_RANK", default_value = "1")]
+    local_rank: String,
+
+    /// Total number of ranks in the distributed setup
+    #[arg(long, env = "WORLD_SIZE", default_value = "3")]
+    world_size: String,
+}
+/// Sample client main function that demonstrates accessing cached datasets.
+///
+/// This function:
+/// 1. Connects to the configurable head node endpoint
+/// 2. Requests flight information for a specific rank/partition
+/// 3. Streams data from worker nodes using Arrow Flight protocol
+/// 4. Validates that cached data can be successfully accessed and consumed
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    tracing_subscriber::fmt().init();
+
+    let args = Args::parse();
+
+    let channel = Channel::from_shared(args.endpoint.clone())?.connect().await?;
+    let mut client = FlightServiceClient::new(channel);
+
+    let descriptor = FlightDescriptor {
+        r#type: 1,
+        cmd: Default::default(),
+        path: vec![args.local_rank.clone(), args.world_size.clone()],
+    };
+
+    let response = client.get_flight_info(descriptor).await?;
+    let flight_info = response.into_inner();
+
+    for endpoint in flight_info.endpoint {
+        for location in endpoint.location {
+            let mut client = connect_to_host(&location.uri).await?;
+            let request = tonic::Request::new(endpoint.ticket.clone().ok_or("No ticket found in endpoint")?);
+            let response = client.do_get(request).await?.into_inner();
+
+            let mut record_batch_stream = FlightRecordBatchStream::new_from_flight_data(
+                response.map_err(|e| e.into())
+            );
+
+            while let Some(batch) = record_batch_stream.next().await {
+                match batch {
+                    Ok(record_batch) => {
+                        info!("Read batch with {} rows", record_batch.num_rows());
+                       // println!("{:?}", record_batch);
+                    },
+                    Err(e) => {
+                        error!("error: {}", e)
+                    }
+                }
+            }
+        }
+    }
+
+    Ok(())
+}
+
+/// Connects to a worker node endpoint for streaming cached dataset data.
+///
+/// This helper function establishes a connection to a worker node and returns
+/// a Flight service client for data streaming operations.
+///
+/// # Arguments
+/// * `endpoint` - The worker node endpoint URI
+///
+/// # Returns
+/// * `Result<FlightServiceClient<Channel>, Box<dyn std::error::Error>>` - Connected client or error
+async fn connect_to_host(endpoint: &str) -> Result<FlightServiceClient<Channel>, Box<dyn std::error::Error>> {
+    let address = format!("{}", endpoint);
+    info!("{}", address);
+    Ok(FlightServiceClient::connect(address).await?)
+}
diff --git a/pkg/data_cache/src/config/config.rs b/pkg/data_cache/src/config/config.rs
new file mode 100644
index 0000000000..8b5041a2b4
--- /dev/null
+++ b/pkg/data_cache/src/config/config.rs
@@ -0,0 +1,78 @@
+use std::env;
+use std::sync::Arc;
+use std::time::Duration;
+
+/// Configuration for dataset metadata and table information.
+///
+/// **Important**: The `schema_name` here refers to the **Iceberg schema namespace**,
+/// not Arrow schemas. The distributed caching system uses two separate Arrow schemas:
+///
+/// 1. **Metadata Schema**: Created by head node for worker coordination
+/// 2. **Data Schema**: Converted from Iceberg schema by worker nodes
+///
+/// This config provides the Iceberg table coordinates that workers use to
+/// retrieve the original data schema and convert it to Arrow format.
+#[derive(Debug, Clone)]
+pub struct DatasetConfig {
+    /// Location of Iceberg table metadata (e.g., S3 path to metadata.json)
+    pub metadata_loc: String,
+    /// Iceberg schema namespace (NOT Arrow schema - used for table identification)
+    pub schema_name: String,
+    /// Iceberg table name within the schema namespace
+    pub table_name: String,
+}
+
+/// Comprehensive configuration for the data cache system
+/// Consolidates all environment variables used across the application
+#[derive(Debug, Clone)]
+#[allow(dead_code)]
+pub struct CacheConfig {
+    pub dataset: DatasetConfig,
+    pub connect_timeout: Duration,
+}
+
+impl DatasetConfig {
+    pub fn from_env() -> Result<Self, Box<dyn std::error::Error>> {
+        let metadata_loc = env::var("METADATA_LOC")?;
+        let schema_name = env::var("SCHEMA_NAME")?;
+        let table_name = env::var("TABLE_NAME")?;
+        Ok(DatasetConfig {
+            metadata_loc,
+            schema_name,
+            table_name,
+        })
+    }
+}
+
+#[allow(dead_code)]
+impl CacheConfig {
+    pub fn from_env() -> Result<Self, Box<dyn std::error::Error>> {
+        let dataset = DatasetConfig::from_env()?;
+
+        // Connection timeout with default of 20 seconds
+        let connect_timeout = env::var("CONNECT_TIMEOUT_SECS")
+            .ok()
+            .and_then(|s| s.parse::<u64>().ok())
+            .map(Duration::from_secs)
+            .unwrap_or(Duration::from_secs(20));
+
+        Ok(CacheConfig {
+            dataset,
+            connect_timeout,
+        })
+    }
+
+    /// Create shared configuration from environment variables
+    /// Returns Arc<CacheConfig> for efficient sharing across components
+    #[allow(dead_code)]
+    pub fn shared_from_env() -> Result<Arc<Self>, Box<dyn std::error::Error>> {
+        Ok(Arc::new(Self::from_env()?))
+    }
+
+    /// Create a new configuration with custom timeout
+    #[allow(dead_code)]
+    pub fn with_timeout(mut self, timeout: Duration) -> Self {
+        self.connect_timeout = timeout;
+        self
+    }
+}
diff --git a/pkg/data_cache/src/config/mod.rs b/pkg/data_cache/src/config/mod.rs
new file mode 100644
index 0000000000..ef68c36943
--- /dev/null
+++ b/pkg/data_cache/src/config/mod.rs
@@ -0,0 +1 @@
+pub mod config;
diff --git a/pkg/data_cache/src/head/bin/main.rs b/pkg/data_cache/src/head/bin/main.rs
new file mode 100644
index 0000000000..664e12f96a
--- /dev/null
+++ b/pkg/data_cache/src/head/bin/main.rs
@@ -0,0 +1,144 @@
+use std::env;
+use std::fs;
+use std::time::Duration;
+use tokio::time::sleep;
+use tracing::{info, warn, error, debug};
+use tracing_subscriber;
+use trust_dns_resolver::TokioAsyncResolver;
+use trust_dns_resolver::config::*;
+use serde::{Deserialize, Serialize};
+use serde_json;
+
+#[path = "../mod.rs"]
+mod head;
+
+/// Worker configuration structure for parsing worker-mapping.json
+#[derive(Debug, Clone, Serialize, Deserialize)]
+struct WorkerConfig {
+    host: String,
+    port: u16,
+}
+
+/// Root structure for worker-mapping.json
+#[derive(Debug, Clone, Serialize, Deserialize)]
+struct WorkerMapping {
+    workers: Vec<WorkerConfig>,
+}
+
+/// Load worker configuration from /etc/arrow-cache/worker-mapping.json if it exists
+/// Returns None if file doesn't exist or can't be parsed
+fn load_worker_config() -> Option<Vec<String>> {
+    let config_path = "/etc/arrow-cache/worker-mapping.json";
+
+    info!("🔍 Checking for worker config file at: {}", config_path);
+
+    match fs::read_to_string(config_path) {
+        Ok(content) => {
+            info!("✅ Successfully read worker config file");
+            debug!("📄 Worker config file content: {}", content);
+
+            match serde_json::from_str::<WorkerMapping>(&content) {
+                Ok(mapping) => {
+                    let workers: Vec<String> = mapping.workers
+                        .into_iter()
+                        .map(|w| format!("http://{}:{}", w.host, w.port))
+                        .collect();
+
+                    info!("🎯 Loaded {} workers from config file: {:?}", workers.len(), workers);
+                    Some(workers)
+                }
+                Err(e) => {
+                    error!("❌ Failed to parse worker config JSON: {}", e);
+                    warn!("📝 Config file content was: {}", content);
+                    None
+                }
+            }
+        }
+        Err(e) => {
+            info!("ℹ️  Worker config file not found or not readable: {}", e);
+            info!("🔄 Will fall back to environment variable construction");
+            None
+        }
+    }
+}
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    tracing_subscriber::fmt().init();
+
+    let args: Vec<String> = std::env::args().collect();
+    info!("🚀 Arguments passed to head: {:?}", &args[1..]);
+
+    // Print all relevant environment variables for debugging
+    info!("🔍 Environment Variables:");
+    if let Ok(runtime_env) = env::var("RUNTIME_ENV") {
+        info!("  RUNTIME_ENV = {}", runtime_env);
+    } else {
+        info!("  RUNTIME_ENV = <not set>");
+    }
+
+    if let Ok(lws_leader) = env::var("LWS_LEADER_ADDRESS") {
+        info!("  LWS_LEADER_ADDRESS = {}", lws_leader);
+    }
+
+    if let Ok(lws_size) = env::var("LWS_GROUP_SIZE") {
+        info!("  LWS_GROUP_SIZE = {}", lws_size);
+    }
+
+    if let Ok(worker_service) = env::var("WORKER_SERVICE_NAME") {
+        info!("  WORKER_SERVICE_NAME = {}", worker_service);
+    }
+
+    let mut rpc_hosts = Vec::new();
+
+    // First, try to load worker configuration from config file
+    if let Some(config_workers) = load_worker_config() {
+        info!("✅ Using worker configuration from config file");
+        rpc_hosts = config_workers;
+    } else if env::var("RUNTIME_ENV").is_ok() {
+        info!("🏠 Using localhost configuration (RUNTIME_ENV is set)");
+        rpc_hosts.push(format!("{}:{}", "localhost", "50052"));
+        rpc_hosts.push(format!("{}:{}", "localhost", "50053"));
+    } else {
+        info!("🔧 Using LWS environment variable construction");
+
+        let lws_leader_address = env::var("LWS_LEADER_ADDRESS")?;
+        let lws_size: i32 = env::var("LWS_GROUP_SIZE")?.parse()?;
+        let rpc_port = 50051;
+
+        info!("🎯 LWS Configuration:");
+        info!("  Leader Address: {}", lws_leader_address);
+        info!("  Group Size: {}", lws_size);
+        info!("  RPC Port: {}", rpc_port);
+
+        let service_tokens: Vec<&str> = lws_leader_address.split('.').collect();
+        info!("  Service Tokens: {:?}", service_tokens);
+
+        let _resolver =
+            TokioAsyncResolver::tokio(ResolverConfig::default(), ResolverOpts::default());
+
+        for i in 1..lws_size {
+            let host = format!(
+                "{}-{}.{}",
+                service_tokens[0],
+                i,
+                service_tokens[1..].join(".")
+            );
+
+            info!("🔗 Constructing worker host {}: {}", i, host);
+
+            sleep(Duration::from_secs(10)).await;
+
+            let worker_url = format!("http://{}:{}", host, rpc_port);
+            info!("➕ Adding worker: {}", worker_url);
+            rpc_hosts.push(worker_url);
+        }
+    }
+
+    info!("🎯 Final RPC Hosts: {:?}", rpc_hosts);
+    let host = args.get(1).ok_or("Missing host argument")?;
+    let port = args.get(2).ok_or("Missing port argument")?;
+
+    info!("🌐 Starting head service on {}:{}", host, port);
+    head::head_service::run(host, port, rpc_hosts).await
+}
diff --git a/pkg/data_cache/src/head/head.rs b/pkg/data_cache/src/head/head.rs
new file mode 100644
index 0000000000..c269133a15
--- /dev/null
+++ b/pkg/data_cache/src/head/head.rs
@@ -0,0 +1,296 @@
+use super::config::config::CacheConfig;
+use crate::head::provider::DataFileTableProvider;
+use crate::head::writer::DistributedWriterExec;
+use arrow::array::UInt64Array;
+use arrow_schema::SchemaRef;
+use datafusion::datasource::MemTable;
+use datafusion::error::{DataFusionError, Result};
+use datafusion::physical_expr::Partitioning;
+use datafusion::physical_plan::execute_stream;
+use datafusion::physical_plan::repartition::RepartitionExec;
+use datafusion::prelude::SessionContext;
+use datafusion::sql::TableReference;
+use futures::StreamExt;
+use std::collections::HashMap;
+use std::sync::Arc;
+use std::time::Duration;
+use tokio::time::interval;
+use tracing::{error, info, warn};
+
+pub struct Distributor {
+    ctx: Arc<SessionContext>,
+    num_workers: usize,
+    data_file_provider: Arc<DataFileTableProvider>,
+    mem_table_name: String,
+    worker_map: Arc<HashMap<String, String>>,
+    metadata_schema: SchemaRef,
+    pub(crate) total_row_count: i64,
+    config: Arc<CacheConfig>,
+    retry_task_handle: Option<tokio::task::JoinHandle<()>>,
+}
+
+impl Distributor {
+    pub fn new(
+        ctx: Arc<SessionContext>,
+        num_workers: usize,
+        data_file_provider: Arc<DataFileTableProvider>,
+        mem_table_name: String,
+        worker_map: Arc<HashMap<String, String>>,
+        metadata_schema: SchemaRef,
+        config: Arc<CacheConfig>,
+    ) -> Self {
+        Self {
+            ctx,
+            num_workers,
+            data_file_provider,
+            mem_table_name,
+            worker_map,
+            metadata_schema,
+            total_row_count: 0,
+            config,
+            retry_task_handle: None,
+        }
+    }
+
+    pub fn context(&self) -> Arc<SessionContext> {
+        self.ctx.clone()
+    }
+
+    pub async fn init(&mut self) -> Result<()> {
+        let _ = self.fetch_data_files().await;
+        let df = self
+            .ctx
+            .sql("select * from memtable")
+            .await?
+            .collect()
+            .await?;
+        let _ = arrow::util::pretty::print_batches(&df);
+
+        let df = self
+            .ctx
+            .sql("SELECT MAX(row_end_indexes) AS max FROM memtable")
+            .await?;
+        let results = df.collect().await?;
+
+        if let Some(batch) = results.first() {
+            let column = batch.column(0);
+            let max_value = column
+                .as_any()
+                .downcast_ref::<UInt64Array>()
+                .ok_or_else(|| {
+                    DataFusionError::Execution("Failed to downcast to UInt64Array".to_string())
+                })?
+                .value(0);
+            self.total_row_count = (max_value + 1) as i64;
+            info!("Total num of rows: {}", self.total_row_count);
+        }
+
+        self.distribute_data_files().await?;
+
+        // Start periodic retry task if configured
+        self.start_periodic_retry_task().await;
+
+        Ok(())
+    }
+
+    pub async fn get_workers_to_connect(&self, start: u64, end: u64) -> Result<Vec<String>> {
+        info!("start: {}, end: {}", start, end);
+        let df = self.ctx.sql(format!("SELECT worker_ids FROM memtable WHERE row_start_indexes <= {} AND row_end_indexes >= {}", end, start).as_str()).await?;
+        let results = df.collect().await?;
+        let _ = arrow::util::pretty::print_batches(&results);
+
+        let mut string_results: Vec<String> = Vec::new();
+
+        for batch in results {
+            for row in 0..batch.num_rows() {
+                let mut row_string = String::new();
+                let array = batch.column(0);
+                let value = array
+                    .as_any()
+                    .downcast_ref::<UInt64Array>()
+                    .ok_or_else(|| {
+                        DataFusionError::Execution("Failed to downcast to UInt64Array".to_string())
+                    })?
+                    .value(row);
+                let url = self.worker_map.get(&value.to_string()).ok_or_else(|| {
+                    DataFusionError::Execution(format!("Worker {} not found in worker map", value))
+                })?;
+                row_string.push_str(url);
+                string_results.push(row_string);
+            }
+        }
+        Ok(string_results)
+    }
+
+    pub async fn fetch_data_files(&mut self) -> Result<()> {
+        let memtable = MemTable::load(
+            self.data_file_provider.clone(),
+            Some(self.num_workers),
+            &self.ctx.state(),
+        )
+        .await
+        .map_err(|err: DataFusionError| {
+            error!("Error loading table: {}", err);
+            err
+        })?;
+        self.ctx
+            .register_table(self.mem_table_name.clone(), Arc::new(memtable))
+            .map_err(|err: DataFusionError| {
+                error!("Failed to register table: {}", err);
+                err
+            })?;
+        Ok(())
+    }
+
+    pub async fn distribute_and_setup(&mut self) -> Result<()> {
+        // Calculate total row count first
+        let df = self
+            .ctx
+            .sql("SELECT MAX(row_end_indexes) AS max FROM memtable")
+            .await?;
+        let results = df.collect().await?;
+
+        if let Some(batch) = results.first() {
+            let column = batch.column(0);
+            let max_value = column
+                .as_any()
+                .downcast_ref::<UInt64Array>()
+                .ok_or_else(|| {
+                    DataFusionError::Execution("Failed to downcast to UInt64Array".to_string())
+                })?
+                .value(0);
+            self.total_row_count = (max_value + 1) as i64;
+            info!("Total num of rows: {}", self.total_row_count);
+        }
+
+        // Distribute data to workers
+        self.distribute_data_files().await?;
+
+        // Start periodic retry task if configured
+        self.start_periodic_retry_task().await;
+
+        Ok(())
+    }
+
+    async fn distribute_data_files(&self) -> Result<()> {
+        let table = self
+            .ctx
+            .table_provider(TableReference::parse_str(&self.mem_table_name))
+            .await
+            .map_err(|err: DataFusionError| {
+                error!("Error retrieving table: {}", err);
+                err
+            })?;
+        let plan = table.scan(&self.ctx.state(), None, &[], None).await?;
+        let plan = RepartitionExec::try_new(plan, Partitioning::RoundRobinBatch(self.num_workers))?;
+        let plan = DistributedWriterExec::new(
+            Arc::new(plan),
+            self.worker_map.clone(),
+            self.metadata_schema.clone(),
+            self.num_workers,
+            self.config.clone(),
+        );
+        let _ = execute_stream(Arc::new(plan), self.ctx.task_ctx())?
+            .collect::<Vec<_>>()
+            .await;
+        Ok(())
+    }
+
+    /// Start a background task that periodically retries data distribution to any workers
+    /// that may have failed during initial distribution or restarted
+    async fn start_periodic_retry_task(&mut self) {
+        let retry_interval_seconds: u64 =
+            std::env::var("ARROW_CACHE_PERIODIC_RETRY_INTERVAL_SECONDS")
+                .unwrap_or_else(|_| "30".to_string()) // Default: retry every 30 seconds
+                .parse()
+                .unwrap_or(30);
+
+        if retry_interval_seconds == 0 {
+            info!("Periodic retry disabled (ARROW_CACHE_PERIODIC_RETRY_INTERVAL_SECONDS=0)");
+            return;
+        }
+
+        info!(
+            "Starting periodic retry task (interval: {}s)",
+            retry_interval_seconds
+        );
+
+        let ctx = self.ctx.clone();
+        let num_workers = self.num_workers;
+        let mem_table_name = self.mem_table_name.clone();
+        let worker_map = self.worker_map.clone();
+        let metadata_schema = self.metadata_schema.clone();
+        let config = self.config.clone();
+
+        let handle = tokio::spawn(async move {
+            let mut retry_interval = interval(Duration::from_secs(retry_interval_seconds));
+            retry_interval.set_missed_tick_behavior(tokio::time::MissedTickBehavior::Skip);
+
+            loop {
+                retry_interval.tick().await;
+
+                info!("Attempting periodic data redistribution to workers");
+
+                match Self::periodic_distribute_data_files(
+                    &ctx,
+                    num_workers,
+                    &mem_table_name,
+                    worker_map.clone(),
+                    metadata_schema.clone(),
+                    config.clone(),
+                )
+                .await
+                {
+                    Ok(_) => {
+                        info!("Periodic data redistribution completed successfully");
+                    }
+                    Err(e) => {
+                        warn!("Periodic data redistribution failed: {}", e);
+                    }
+                }
+            }
+        });
+
+        self.retry_task_handle = Some(handle);
+    }
+
+    /// Periodic version of distribute_data_files that can be called from background task
+    async fn periodic_distribute_data_files(
+        ctx: &Arc<SessionContext>,
+        num_workers: usize,
+        mem_table_name: &str,
+        worker_map: Arc<HashMap<String, String>>,
+        metadata_schema: SchemaRef,
+        config: Arc<CacheConfig>,
+    ) -> Result<()> {
+        let table = ctx
+            .table_provider(TableReference::parse_str(mem_table_name))
+            .await
+            .map_err(|err: DataFusionError| {
+                error!("Error retrieving table for periodic retry: {}", err);
+                err
+            })?;
+        let plan = table.scan(&ctx.state(), None, &[], None).await?;
+        let plan = RepartitionExec::try_new(plan, Partitioning::RoundRobinBatch(num_workers))?;
+        let plan = DistributedWriterExec::new(
+            Arc::new(plan),
+            worker_map,
+            metadata_schema,
+            num_workers,
+            config,
+        );
+        let _ = execute_stream(Arc::new(plan), ctx.task_ctx())?
+            .collect::<Vec<_>>()
+            .await;
+        Ok(())
+    }
+}
+
+/// Cleanup implementation to properly shutdown retry task
+impl Drop for Distributor {
+    fn drop(&mut self) {
+        if let Some(handle) = self.retry_task_handle.take() {
+            handle.abort();
+        }
+    }
+}
diff --git a/pkg/data_cache/src/head/head_service.rs b/pkg/data_cache/src/head/head_service.rs
new file mode 100644
index 0000000000..bddc817414
--- /dev/null
+++ b/pkg/data_cache/src/head/head_service.rs
@@ -0,0 +1,687 @@
+use crate::head::head::Distributor;
+use crate::head::provider::DataFileTableProvider;
+use arrow_flight::{
+    Action, Criteria, Empty, FlightData, FlightDescriptor, FlightEndpoint, FlightInfo,
+    HandshakeRequest, HandshakeResponse, Location, PollInfo, PutResult, SchemaResult, Ticket,
+    flight_service_server::{FlightService, FlightServiceServer},
+};
+use arrow_schema::{DataType, Field, Schema, SchemaRef};
+use bincode;
+use bytes::Bytes;
+use datafusion::prelude::SessionContext;
+use futures::Stream;
+use std::collections::HashMap;
+use std::pin::Pin;
+use std::sync::Arc;
+use tonic::{Request, Response, Status, Streaming};
+use tracing::{info, error};
+
+/// Head node service implementing Apache Arrow Flight protocol for distributed query coordination.
+///
+/// **IMPORTANT**: This service handles **coordination metadata** only and does NOT
+/// deal with actual data schemas. The head node uses a **metadata schema** for
+/// worker coordination, while workers separately manage **data schemas** for
+/// actual data processing.
+///
+/// # Dual Schema Architecture - Head Node Coordination
+///
+/// The distributed caching system uses **two completely separate Arrow schemas**:
+///
+/// ## 1. **Metadata Schema** (Head Node - This Service):
+/// - **Purpose**: Coordinate worker assignments and data distribution
+/// - **Created by**: [`metadata_arrow_schema()`] function
+/// - **Contains**: `worker_ids`, `row_start_indexes`, `row_end_indexes`, `file_paths`
+/// - **Used for**: Flight protocol coordination, worker task distribution
+/// - **Location**: Head node only (this service)
+///
+/// ## 2. **Data Schema** (Worker Nodes):
+/// - **Purpose**: Describe actual data structure being cached/queried
+/// - **Created by**: Converting Iceberg schema to Arrow in [`WorkerDataSource`]
+/// - **Contains**: Actual data columns (e.g., `id`, `user_id`, `event_type`, `timestamp`)
+/// - **Used for**: Query execution, data processing, result generation
+/// - **Location**: Worker nodes only
+///
+/// # Schema Separation Benefits
+///
+/// - **Modularity**: Head nodes don't need to understand data semantics
+/// - **Performance**: Lightweight coordination without full data schema overhead
+/// - **Evolution**: Data schemas can change independently of coordination logic
+/// - **Scalability**: Head node coordination scales independently of data complexity
+///
+/// # Architecture
+///
+/// The head service operates as the coordinator in a head-worker distributed system:
+/// - Receives flight information requests from clients
+/// - Partitions data ranges across available worker nodes using **metadata schema**
+/// - Distributes file assignments to worker nodes
+/// - Provides flight endpoints for distributed query execution
+/// - **Never handles actual data** - only coordination metadata
+///
+/// # Flight Protocol Usage
+///
+/// - **`get_flight_info`**: Provides flight information with worker endpoints (uses metadata schema)
+/// - Other Flight methods are not currently implemented
+///
+/// # Coordination Data Flow
+///
+/// ```text
+/// ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
+/// │   Client        │───▶│   HeadService   │───▶│   Distributor   │
+/// │(get_flight_info)│    │(metadata schema)│    │(metadata schema)│
+/// └─────────────────┘    └─────────────────┘    └─────────────────┘
+///                                 │                       │
+///                                 ▼                       ▼
+///                        ┌─────────────────┐    ┌─────────────────┐
+///                        │ Flight Endpoints│    │ Worker Nodes    │
+///                        │(metadata schema)│    │ (data schemas)  │
+///                        └─────────────────┘    └─────────────────┘
+/// ```
+///
+/// # Example Coordination vs Data
+///
+/// **Head Node Metadata** (coordination only):
+/// ```text
+/// ┌────────────┬─────────────────┬───────────────┬─────────────────────────┐
+/// │ worker_ids │ row_start_index │ row_end_index │ file_paths              │
+/// ├────────────┼─────────────────┼───────────────┼─────────────────────────┤
+/// │ 0          │ 0               │ 999           │ ["/data/part1.parquet"] │
+/// │ 1          │ 1000            │ 1999          │ ["/data/part2.parquet"] │
+/// └────────────┴─────────────────┴───────────────┴─────────────────────────┘
+/// ```
+///
+/// **Worker Node Data** (actual data structure):
+/// ```text
+/// ┌────────┬─────────┬────────────┬─────────────┬─────────────┐
+/// │   id   │ user_id │ event_type │ timestamp   │ cache_index │
+/// │ Int64  │ String  │   String   │ Timestamp   │   UInt64    │
+/// └────────┴─────────┴────────────┴─────────────┴─────────────┘
+/// ```
+///
+/// # Performance Considerations
+///
+/// - Partitions data to balance load across workers (using metadata schema)
+/// - Uses efficient serialization for flight coordination metadata
+/// - Maintains worker topology for optimal data distribution
+/// - Supports dynamic worker scaling without data schema dependencies
+/// - Lightweight coordination operations independent of data complexity
+///
+/// # See Also
+///
+/// ## Head Node Coordination (Metadata Schema):
+/// - [`Distributor`]: Handles data distribution and worker coordination
+/// - [`get_partition_range`]: Calculates data partitioning ranges
+/// - [`IndexPair`]: Represents row ranges in flight tickets
+/// - [`metadata_arrow_schema()`]: Creates the coordination metadata schema
+///
+/// ## Worker Node Data Processing (Data Schema):
+/// - [`WorkerDataSource`]: Manages data schemas for actual data processing
+/// - [`WorkerService`]: Handles data queries using data schemas
+pub struct HeadService {
+    distributor: Distributor,
+}
+
+impl HeadService {
+    #[allow(dead_code)]
+    pub fn new(distributor: Distributor) -> Self {
+        Self { distributor }
+    }
+
+    /// Try to get the total row count from the memtable if it exists
+    async fn get_total_row_count(&self) -> Result<usize, Box<dyn std::error::Error>> {
+        use arrow::array::UInt64Array;
+        use datafusion::error::DataFusionError;
+
+        let df = self.distributor.context()
+            .sql("SELECT MAX(row_end_indexes) AS max FROM memtable")
+            .await?;
+        let results = df.collect().await?;
+
+        if let Some(batch) = results.first() {
+            let column = batch.column(0);
+            let max_value = column
+                .as_any()
+                .downcast_ref::<UInt64Array>()
+                .ok_or_else(|| {
+                    DataFusionError::Execution("Failed to downcast to UInt64Array".to_string())
+                })?
+                .value(0);
+            Ok((max_value + 1) as usize)
+        } else {
+            Err("No rows found in memtable".into())
+        }
+    }
+}
+
+#[tonic::async_trait]
+impl FlightService for HeadService {
+    type HandshakeStream =
+        Pin<Box<dyn Stream<Item = Result<HandshakeResponse, Status>> + Send + 'static>>;
+    async fn handshake(
+        &self,
+        _request: Request<Streaming<HandshakeRequest>>,
+    ) -> Result<Response<Self::HandshakeStream>, Status> {
+        todo!()
+    }
+    type ListFlightsStream =
+        Pin<Box<dyn Stream<Item = Result<FlightInfo, Status>> + Send + 'static>>;
+    async fn list_flights(
+        &self,
+        _request: Request<Criteria>,
+    ) -> Result<Response<Self::ListFlightsStream>, Status> {
+        todo!()
+    }
+    /// Provides flight information for distributed query execution.
+    ///
+    /// This method handles client requests for flight information by partitioning
+    /// data ranges across available worker nodes and returning flight endpoints
+    /// that clients can use to query specific data ranges.
+    ///
+    /// # Parameters
+    ///
+    /// - `request`: Flight descriptor containing partition information in the path
+    ///   - `path[0]`: Local rank (partition ID) for the requesting client
+    ///   - `path[1]`: Total number of partitions across all clients
+    ///
+    /// # Returns
+    ///
+    /// Returns [`FlightInfo`] containing:
+    /// - Flight endpoints with worker URIs for the requested partition
+    /// - Serialized [`IndexPair`] tickets containing row ranges
+    /// - Schema information for the distributed data
+    ///
+    /// # Algorithm
+    ///
+    /// 1. Parse local rank and total partitions from the flight descriptor
+    /// 2. Calculate data partition range using [`get_partition_range`]
+    /// 3. Get worker nodes responsible for the partition range
+    /// 4. Create flight endpoints with worker locations and row range tickets
+    /// 5. Return flight information with schema and endpoints
+    ///
+    /// # Data Partitioning
+    ///
+    /// Data is partitioned evenly across the requested number of partitions:
+    /// - Each partition gets approximately `total_rows / num_partitions` rows
+    /// - Partition ranges are calculated to avoid gaps or overlaps
+    /// - Empty partitions are handled gracefully
+    ///
+    /// # Error Handling
+    ///
+    /// - Returns `Status::invalid_argument` if path parameters are missing
+    /// - Returns `Status::invalid_argument` if parameters cannot be parsed
+    /// - Returns `Status::internal` if worker lookup fails
+    /// - Returns `Status::internal` if serialization fails
+    ///
+    /// # Example Usage
+    ///
+    /// For a client requesting partition 0 of 4 total partitions:
+    /// ```
+    /// path = ["0", "4"]
+    /// // Returns flight endpoints for rows 0 to (total_rows/4 - 1)
+    /// ```
+    ///
+    /// # Performance Considerations
+    ///
+    /// - Uses efficient binary serialization for flight metadata
+    /// - Minimizes network communication by providing direct worker endpoints
+    /// - Balances load across available workers
+    /// - Supports parallel query execution across partitions
+    ///
+    /// # See Also
+    ///
+    /// - [`get_partition_range`]: Calculates partition boundaries
+    /// - [`Distributor::get_workers_to_connect`]: Finds responsible workers
+    /// - [`IndexPair`]: Row range representation in tickets
+    async fn get_flight_info(
+        &self,
+        request: Request<FlightDescriptor>,
+    ) -> Result<Response<FlightInfo>, Status> {
+        let request = request.into_inner();
+        let local_rank = request
+            .path
+            .first()
+            .ok_or_else(|| Status::invalid_argument("Missing local_rank in path"))?;
+        let total = request
+            .path
+            .get(1)
+            .ok_or_else(|| Status::invalid_argument("Missing total in path"))?;
+        let mut pair = IndexPair { start: 0, end: 0 };
+        let total_parsed = total
+            .parse()
+            .map_err(|_| Status::invalid_argument("Invalid total value"))?;
+        let local_rank_parsed = local_rank
+            .parse()
+            .map_err(|_| Status::invalid_argument("Invalid local_rank value"))?;
+
+        // Get the current total row count (may need to initialize it)
+        let total_row_count = if self.distributor.total_row_count == 0 {
+            // Try to get row count from memtable if available
+            match self.get_total_row_count().await {
+                Ok(count) => count,
+                Err(_) => {
+                    info!("Total row count not yet available, returning empty endpoints");
+                    0
+                }
+            }
+        } else {
+            self.distributor.total_row_count as usize
+        };
+
+        let workers = if let Some((start, end)) = get_partition_range(
+            total_row_count,
+            total_parsed,
+            local_rank_parsed,
+        ) {
+            //TODO: fetch total count
+            pair = IndexPair { start, end };
+            self.distributor.get_workers_to_connect(start, end).await
+        } else {
+            Ok(Vec::new())
+        };
+        let mut endpoints = vec![];
+        for uri in workers.map_err(|e| Status::internal(format!("Error getting workers: {}", e)))? {
+            endpoints.push(FlightEndpoint {
+                ticket: Some(Ticket::new(Bytes::from(
+                    bincode::serialize(&pair)
+                        .map_err(|e| Status::internal(format!("Serialization error: {}", e)))?,
+                ))),
+                location: vec![Location { uri }],
+                expiration_time: None,
+                app_metadata: Bytes::from(
+                    bincode::serialize(&pair)
+                        .map_err(|e| Status::internal(format!("Serialization error: {}", e)))?,
+                ),
+            });
+        }
+
+        let flight_info = FlightInfo {
+            schema: Bytes::new(),
+            flight_descriptor: Some(request),
+            endpoint: endpoints,
+            total_records: -1,
+            total_bytes: -1,
+            ordered: false,
+            app_metadata: Default::default(),
+        };
+
+        let flight_info = flight_info
+            .try_with_schema(metadata_arrow_schema().as_ref())
+            .map_err(|e| Status::internal(format!("Schema error: {}", e)))?; // TODO:// pass correct schema
+        Ok(Response::new(flight_info))
+    }
+    async fn poll_flight_info(
+        &self,
+        _request: Request<FlightDescriptor>,
+    ) -> Result<Response<PollInfo>, Status> {
+        todo!()
+    }
+    async fn get_schema(
+        &self,
+        _request: Request<FlightDescriptor>,
+    ) -> Result<Response<SchemaResult>, Status> {
+        unimplemented!()
+    }
+
+    type DoGetStream = Pin<Box<dyn Stream<Item = Result<FlightData, Status>> + Send + 'static>>;
+
+    async fn do_get(
+        &self,
+        _request: Request<Ticket>,
+    ) -> Result<Response<<Self as FlightService>::DoGetStream>, Status> {
+        todo!()
+    }
+
+    type DoPutStream = Pin<Box<dyn Stream<Item = Result<PutResult, Status>> + Send + 'static>>;
+
+    async fn do_put(
+        &self,
+        _request: Request<Streaming<FlightData>>,
+    ) -> Result<Response<Self::DoPutStream>, Status> {
+        todo!()
+    }
+
+    type DoExchangeStream =
+        Pin<Box<dyn Stream<Item = Result<FlightData, Status>> + Send + 'static>>;
+
+    async fn do_exchange(
+        &self,
+        _request: Request<Streaming<FlightData>>,
+    ) -> Result<Response<Self::DoExchangeStream>, Status> {
+        todo!()
+    }
+
+    type DoActionStream =
+        Pin<Box<dyn Stream<Item = Result<arrow_flight::Result, Status>> + Send + 'static>>;
+
+    async fn do_action(
+        &self,
+        _request: Request<Action>,
+    ) -> Result<Response<Self::DoActionStream>, Status> {
+        todo!()
+    }
+
+    type ListActionsStream =
+        Pin<Box<dyn Stream<Item = Result<arrow_flight::ActionType, Status>> + Send + 'static>>;
+
+    async fn list_actions(
+        &self,
+        _request: Request<Empty>,
+    ) -> Result<Response<Self::ListActionsStream>, Status> {
+        todo!()
+    }
+}
+
+use super::config::config::CacheConfig;
+use serde::{Deserialize, Serialize};
+
+/// Represents a row range for distributed query execution.
+///
+/// This structure is used to communicate row ranges between the head node and
+/// worker nodes in the distributed caching system. It is serialized into
+/// flight tickets and application metadata for efficient communication.
+///
+/// # Fields
+///
+/// - `start`: Starting row index (inclusive)
+/// - `end`: Ending row index (inclusive)
+///
+/// # Serialization
+///
+/// The struct is serialized using `bincode` for efficient binary representation
+/// in flight tickets and metadata. This enables fast serialization/deserialization
+/// across network boundaries.
+///
+/// # Usage
+///
+/// ```rust
+/// let range = IndexPair { start: 0, end: 999 };
+/// // Represents rows 0 through 999 (1000 rows total)
+/// ```
+///
+/// # See Also
+///
+/// - [`get_partition_range`]: Creates partition ranges that are converted to IndexPair
+/// - [`get_flight_info`]: Uses IndexPair in flight tickets
+/// - [`do_get`]: Deserializes IndexPair from tickets for query execution
+#[derive(Serialize, Deserialize, Debug)]
+struct IndexPair {
+    start: u64,
+    end: u64,
+}
+
+pub async fn run(
+    host: &String,
+    port: &String,
+    workers: Vec<String>,
+) -> datafusion::common::Result<(), Box<dyn std::error::Error>> {
+    let ctx = Arc::new(SessionContext::new());
+    let addr = format!("{host}:{port}").parse()?;
+    let num_workers = workers.len();
+    let cache_config = CacheConfig::shared_from_env()
+        .map_err(|e| format!("Failed to load dataset config: {}", e))?;
+    let metadata_schema = metadata_arrow_schema();
+    info!(
+        "Creating DataFileTableProvider with schema: {:?}",
+        metadata_schema
+    );
+    let provider = DataFileTableProvider::new(
+        &cache_config.dataset.metadata_loc,
+        &cache_config.dataset.table_name,
+        &cache_config.dataset.schema_name,
+        metadata_schema.clone(),
+        num_workers,
+    )
+    .await
+    .map_err(|e| format!("Failed to create provider: {}", e))?;
+    let mut worker_map: HashMap<String, String> = HashMap::new();
+    for (index, worker_uri) in workers.into_iter().enumerate() {
+        // Strip http:// prefix if present before adding grpc:// prefix
+        let clean_uri = worker_uri.strip_prefix("http://").unwrap_or(&worker_uri);
+        worker_map.insert(index.to_string(), format!("grpc://{clean_uri}"));
+    }
+    let provider_arc = Arc::new(provider);
+    let worker_map_arc = Arc::new(worker_map);
+
+    let mut distributor = Distributor::new(
+        ctx.clone(),
+        num_workers,
+        provider_arc.clone(),
+        "memtable".to_string(),
+        worker_map_arc.clone(),
+        metadata_schema.clone(),
+        cache_config.clone(),
+    );
+
+    // Only do minimal initialization (fetch data files) without distributing
+    info!("🚀 Starting minimal initialization (gRPC server will start immediately)");
+    let _ = distributor.fetch_data_files().await;
+    info!("✅ Minimal initialization completed, starting gRPC server");
+
+    // Start background distribution task
+    tokio::spawn(async move {
+        info!("🚀 Starting background data distribution");
+
+        // Add delay to allow gRPC server to start and workers to be ready
+        tokio::time::sleep(tokio::time::Duration::from_secs(10)).await;
+
+        match distributor.distribute_and_setup().await {
+            Ok(_) => {
+                info!("✅ Background data distribution completed successfully");
+            }
+            Err(e) => {
+                error!("❌ Background data distribution failed: {}", e);
+                // Continue running even if distribution fails - periodic retries will handle it
+            }
+        }
+    });
+
+    info!("🌐 Starting gRPC server on {} (data distribution will happen in background)", addr);
+    let mut service_distributor = Distributor::new(
+        ctx,
+        num_workers,
+        provider_arc,
+        "memtable".to_string(),
+        worker_map_arc,
+        metadata_schema,
+        cache_config,
+    );
+
+    // Initialize the service distributor's memtable so it can handle flight info requests
+    let _ = service_distributor.fetch_data_files().await;
+
+    let service = HeadService { distributor: service_distributor };
+    tonic::transport::Server::builder()
+        .add_service(FlightServiceServer::new(service))
+        .serve(addr)
+        .await
+        .map_err(|e| format!("Error starting server: {}", e))?;
+    Ok(())
+}
+
+/// Creates the Arrow schema for **coordination metadata** in the distributed caching system.
+///
+/// **IMPORTANT**: This function creates the **metadata schema** used for coordination
+/// between the head node and worker nodes. This is **NOT** the data schema that describes
+/// the actual data being processed. The data schema is handled separately by worker nodes
+/// and is converted from Iceberg format to Arrow format in [`WorkerDataSource`].
+///
+/// # Dual Schema Architecture
+///
+/// The distributed caching system uses **two distinct Arrow schemas**:
+///
+/// 1. **Metadata Schema** (this function):
+///    - Used by head node for worker coordination
+///    - Contains worker assignments and file distribution information
+///    - Transmitted via Arrow Flight for system coordination
+///
+/// 2. **Data Schema** (in workers):
+///    - Describes the structure of actual data being cached/queried
+///    - Converted from Iceberg table metadata to Arrow format
+///    - Enhanced with `cache_index` column for efficient indexing
+///    - See [`WorkerDataSource::table_schema`] and [`WorkerDataSource::output_schema`]
+///
+/// # Metadata Schema Fields
+///
+/// The coordination metadata schema contains four essential fields:
+///
+/// - **`worker_ids`** (`UInt64`, non-nullable): Unique identifiers for worker nodes
+///   responsible for processing specific data ranges. Used for routing queries
+///   to the appropriate workers.
+///
+/// - **`row_start_indexes`** (`UInt64`, non-nullable): Starting row indices for
+///   data ranges assigned to each worker. Defines the beginning of each worker's
+///   data partition (inclusive).
+///
+/// - **`row_end_indexes`** (`UInt64`, non-nullable): Ending row indices for
+///   data ranges assigned to each worker. Defines the end of each worker's
+///   data partition (inclusive).
+///
+/// - **`file_paths`** (`List<Utf8View>`, non-nullable): List of file paths
+///   that each worker is responsible for processing. Supports multiple files
+///   per worker for efficient data distribution.
+///
+/// # Returns
+///
+/// Returns an [`SchemaRef`] (reference-counted Arrow schema) that can be:
+/// - Used in flight information responses for coordination
+/// - Shared across multiple head node components without cloning
+/// - Passed to DataFusion table providers for metadata operations
+/// - Serialized in Apache Arrow Flight protocol messages
+///
+/// # Usage in Distributed System
+///
+/// This **metadata schema** is used throughout the head node coordination layer:
+/// 1. **Flight Information**: Attached to flight responses for schema validation
+/// 2. **Worker Communication**: Defines the structure of coordination exchanges
+/// 3. **Query Planning**: Used by DataFusion for distribution planning
+/// 4. **Data Distribution**: Describes how data is partitioned across workers
+///
+/// # Example Coordination Metadata Structure
+///
+/// ```text
+/// ┌────────────┬─────────────────┬───────────────┬─────────────────────────┐
+/// │ worker_ids │ row_start_index │ row_end_index │ file_paths              │
+/// ├────────────┼─────────────────┼───────────────┼─────────────────────────┤
+/// │ 0          │ 0               │ 999           │ ["/data/part1.parquet"] │
+/// │ 1          │ 1000            │ 1999          │ ["/data/part2.parquet"] │
+/// │ 2          │ 2000            │ 2999          │ ["/data/part3.parquet"] │
+/// └────────────┴─────────────────┴───────────────┴─────────────────────────┘
+/// ```
+///
+/// # Schema Separation Rationale
+///
+/// **Why separate metadata and data schemas?**
+/// - **Separation of Concerns**: Coordination logic is independent of data structure
+/// - **Schema Evolution**: Data schema can evolve without affecting coordination
+/// - **Performance**: Lightweight metadata operations don't need full data schema
+/// - **Modularity**: Head nodes don't need to understand data semantics
+///
+/// # Schema Compatibility
+///
+/// - Uses `UInt64` for row indices to support large datasets (up to 2^64 rows)
+/// - Uses `Utf8View` for efficient string storage and reduced memory footprint
+/// - Non-nullable fields ensure data integrity across the distributed system
+/// - List type supports variable numbers of files per worker
+///
+/// # Performance Considerations
+///
+/// - Schema is created once and reused via `Arc<Schema>` for efficiency
+/// - `Utf8View` provides zero-copy string operations for file paths
+/// - Minimal schema overhead for coordination communication
+/// - Compatible with Arrow's columnar format for fast serialization
+///
+/// # See Also
+///
+/// ## Metadata Schema Usage:
+/// - [`DataFileTableProvider`]: Uses this schema for coordination table registration
+/// - [`get_flight_info`]: Attaches this schema to flight responses
+/// - [`Distributor`]: Uses this schema for metadata operations
+///
+/// ## Data Schema Usage (Worker Side):
+/// - [`WorkerDataSource::table_schema`]: Original data schema from Iceberg → Arrow
+/// - [`WorkerDataSource::output_schema`]: Data schema + cache_index column
+/// - [`iceberg::arrow::schema_to_arrow_schema`]: Converts Iceberg schema to Arrow
+fn metadata_arrow_schema() -> SchemaRef {
+    let columns = vec![
+        Field::new("worker_ids", DataType::UInt64, false),
+        Field::new("row_start_indexes", DataType::UInt64, false),
+        Field::new("row_end_indexes", DataType::UInt64, false),
+        Field::new(
+            "file_paths",
+            DataType::List(Arc::new(Field::new("item", DataType::Utf8View, true))),
+            false,
+        ),
+    ];
+    Arc::new(Schema::new(columns))
+}
+
+/// Calculates the row range for a specific partition in distributed query execution.
+///
+/// This function divides the total data count evenly across the requested number
+/// of partitions and returns the start and end row indices for the specified
+/// partition. It handles edge cases like empty datasets and ensures no gaps
+/// or overlaps between partitions.
+///
+/// # Parameters
+///
+/// - `total_count`: Total number of rows in the dataset
+/// - `num_partitions`: Number of partitions to divide the data into
+/// - `partition_id`: Zero-based ID of the partition to calculate range for
+///
+/// # Returns
+///
+/// Returns `Some((start, end))` where:
+/// - `start`: First row index for this partition (inclusive)
+/// - `end`: Last row index for this partition (inclusive)
+///
+/// Returns `None` if:
+/// - `total_count` is 0 (empty dataset)
+/// - `num_partitions` is 0 (invalid partition count)
+/// - `partition_id` >= `num_partitions` (invalid partition ID)
+///
+/// # Algorithm
+///
+/// 1. Calculate rows per partition using ceiling division
+/// 2. Compute start index as `partition_id * rows_per_partition`
+/// 3. Compute end index as `min(start + rows_per_partition, total_count)`
+/// 4. Return inclusive range `[start, end-1]`
+///
+/// # Examples
+///
+/// ```rust
+/// // 100 rows, 4 partitions
+/// assert_eq!(get_partition_range(100, 4, 0), Some((0, 24)));   // Rows 0-24
+/// assert_eq!(get_partition_range(100, 4, 1), Some((25, 49)));  // Rows 25-49
+/// assert_eq!(get_partition_range(100, 4, 2), Some((50, 74)));  // Rows 50-74
+/// assert_eq!(get_partition_range(100, 4, 3), Some((75, 99)));  // Rows 75-99
+///
+/// // Edge cases
+/// assert_eq!(get_partition_range(0, 4, 0), None);     // Empty dataset
+/// assert_eq!(get_partition_range(100, 4, 4), None);   // Invalid partition ID
+/// ```
+///
+/// # Performance Considerations
+///
+/// - Uses ceiling division to handle uneven partition sizes
+/// - Ensures the last partition gets any remaining rows
+/// - Constant time complexity O(1)
+/// - No memory allocation required
+fn get_partition_range(
+    total_count: usize,
+    num_partitions: usize,
+    partition_id: usize,
+) -> Option<(u64, u64)> {
+    if total_count == 0 || num_partitions == 0 || partition_id >= num_partitions {
+        return None;
+    }
+
+    let ids_per_partition = total_count.div_ceil(num_partitions);
+
+    let start_index = partition_id * ids_per_partition;
+    let end_index = (start_index + ids_per_partition).min(total_count);
+
+    if start_index >= total_count {
+        None
+    } else {
+        Some((start_index as u64, (end_index - 1) as u64))
+    }
+}
diff --git a/pkg/data_cache/src/head/mod.rs b/pkg/data_cache/src/head/mod.rs
new file mode 100644
index 0000000000..3b54ab16b7
--- /dev/null
+++ b/pkg/data_cache/src/head/mod.rs
@@ -0,0 +1,6 @@
+#[path = "../config/mod.rs"]
+pub mod config;
+pub mod head;
+pub mod head_service;
+pub mod provider;
+pub mod writer;
diff --git a/pkg/data_cache/src/head/provider.rs b/pkg/data_cache/src/head/provider.rs
new file mode 100644
index 0000000000..5749374ab5
--- /dev/null
+++ b/pkg/data_cache/src/head/provider.rs
@@ -0,0 +1,846 @@
+use arrow::array::{
+    Array, ArrayRef, GenericListBuilder, RecordBatch, StringViewBuilder, UInt64Array,
+};
+use arrow_schema::SchemaRef;
+use async_trait::async_trait;
+use datafusion::catalog::Session;
+use datafusion::datasource::{TableProvider, TableType};
+use datafusion::error::{DataFusionError, Result};
+use datafusion::execution::{SendableRecordBatchStream, TaskContext};
+use datafusion::logical_expr::Expr;
+use datafusion::physical_expr::{EquivalenceProperties, Partitioning};
+use datafusion::physical_plan::execution_plan::{Boundedness, EmissionType};
+use datafusion::physical_plan::stream::RecordBatchStreamAdapter;
+use datafusion::physical_plan::{
+    DisplayAs, DisplayFormatType, EmptyRecordBatchStream, ExecutionPlan, PlanProperties,
+};
+use futures::StreamExt;
+use futures::stream::iter;
+use iceberg::TableIdent;
+use iceberg::expr::Predicate;
+use iceberg::io::FileIO;
+use iceberg::scan::{FileScanTask, FileScanTaskStream};
+use iceberg::table::{StaticTable, Table};
+use std::any::Any;
+use std::fmt::Formatter;
+use std::sync::Arc;
+use tracing::{debug, info};
+
+/// Table provider for distributed Arrow caching system that coordinates data
+/// distribution across multiple worker nodes.
+///
+/// This table provider implements the head node functionality in a distributed
+/// Arrow-based caching system. It loads Iceberg table metadata and partitions
+/// data files across worker nodes for parallel processing.
+///
+/// # Architecture
+///
+/// The provider uses a head-worker architecture where:
+/// - Head node (this provider) coordinates data distribution
+/// - Worker nodes execute queries on partitioned data subsets
+/// - Data is partitioned based on file scan tasks and record counts
+///
+/// # Data Flow
+///
+/// ```text
+/// ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
+/// │  Iceberg Table  │───▶│ DataFileTable   │───▶│ DataFileTable   │
+/// │   Metadata      │    │   Provider      │    │      Exec       │
+/// └─────────────────┘    └─────────────────┘    └─────────────────┘
+///                                 │                       │
+///                                 ▼                       ▼
+///                        ┌─────────────────┐    ┌─────────────────┐
+///                        │  Partition      │    │  Worker Task    │
+///                        │   Planning      │    │   Distribution  │
+///                        └─────────────────┘    └─────────────────┘
+/// ```
+///
+/// # Performance Considerations
+///
+/// - Files are sorted by record count and distributed using a greedy algorithm
+/// - Task groups are balanced to minimize data skew across workers
+/// - Memory usage scales with the number of files and partitions
+///
+/// # See Also
+///
+/// - [`DataFileTableExec`]: The execution plan produced by this provider
+/// - [`TaskGroup`]: Groups of file scan tasks assigned to worker nodes
+#[derive(Debug, Clone)]
+pub struct DataFileTableProvider {
+    inner: Table,
+    schema: SchemaRef,
+    num_workers: usize,
+}
+impl DataFileTableProvider {
+    pub async fn new(
+        metadata_loc: &String,
+        table_name: &String,
+        schema_name: &String,
+        metadata_schema: SchemaRef,
+        num_workers: usize,
+    ) -> Result<Self, Box<dyn std::error::Error>> {
+        info!(
+            "DataFileTableProvider::new called with metadata_schema: {:?}",
+            metadata_schema
+        );
+        let file_io = FileIO::from_path(metadata_loc)
+            .map_err(|e| format!("Failed to create FileIO: {}", e))?
+            .build()
+            .map_err(|e| format!("Failed to build FileIO: {}", e))?;
+        let table_indent = TableIdent::from_strs([schema_name, table_name])
+            .map_err(|e| format!("Failed to create table ident: {}", e))?;
+        let static_table = StaticTable::from_metadata_file(metadata_loc, table_indent, file_io)
+            .await
+            .map_err(|e| format!("Failed to load static table: {}", e))?;
+        let table = static_table.into_table();
+        info!("Iceberg table loaded successfully");
+        // Use the provided metadata schema instead of Iceberg table schema
+        let provider = Self {
+            inner: table,
+            schema: metadata_schema.clone(), // This should be the metadata schema (worker_ids, row_start_indexes, etc.)
+            num_workers,
+        };
+        info!(
+            "DataFileTableProvider created with schema: {:?}",
+            provider.schema
+        );
+        Ok(provider)
+    }
+}
+#[async_trait]
+impl TableProvider for DataFileTableProvider {
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn schema(&self) -> SchemaRef {
+        info!(
+            "DataFileTableProvider returning metadata schema: {:?}",
+            self.schema
+        );
+        self.schema.clone()
+    }
+
+    fn table_type(&self) -> TableType {
+        TableType::Base
+    }
+
+    async fn scan(
+        &self,
+        _state: &dyn Session,
+        _projection: Option<&Vec<usize>>,
+        _filters: &[Expr],
+        _limit: Option<usize>,
+    ) -> Result<Arc<dyn ExecutionPlan>> {
+        let tablescan = self.inner.scan().build().map_err(|e| {
+            DataFusionError::Execution(format!("Failed to build table scan: {}", e))
+        })?;
+
+        // Try to get file scan tasks from Iceberg metadata
+        let file_scan_task_stream = tablescan
+            .plan_files()
+            .await
+            .map_err(|e| DataFusionError::Execution(format!("Failed to plan files: {}", e)))?;
+        let partitions = partition_tasks(file_scan_task_stream, self.num_workers)
+            .await
+            .map_err(|e| DataFusionError::Execution(format!("Failed to partition tasks: {}", e)))?;
+
+        info!("Partition count: {}", partitions.len());
+        info!(
+            "Total tasks across all partitions: {}",
+            partitions.iter().map(|p| p.tasks.len()).sum::<usize>()
+        );
+
+        // If we got empty partitions (no file scan tasks), create fallback tasks
+        let partitions = if partitions.iter().all(|p| p.tasks.is_empty()) {
+            info!("No file scan tasks found in Iceberg metadata, creating fallback tasks");
+            create_fallback_partitions(self.num_workers)
+                .await
+                .map_err(|e| {
+                    DataFusionError::Execution(format!(
+                        "Failed to create fallback partitions: {}",
+                        e
+                    ))
+                })?
+        } else {
+            partitions
+        };
+
+        Ok(Arc::new(DataFileTableExec::new(
+            self.schema.clone(),
+            partitions,
+        )))
+    }
+}
+
+/// Execution plan for distributed data file processing in Arrow caching system.
+///
+/// This execution plan coordinates the distribution of file scan tasks across
+/// multiple worker nodes and produces RecordBatches containing worker assignment
+/// information and file paths for distributed processing.
+///
+/// # Algorithm
+///
+/// 1. Load file scan tasks from Iceberg table metadata
+/// 2. Partition tasks across worker nodes using greedy load balancing
+/// 3. Generate RecordBatches with worker assignments and file paths
+/// 4. Each partition contains tasks for a specific worker node
+///
+/// # Output Schema
+///
+/// The execution plan produces RecordBatches with the following columns:
+/// - `worker_ids`: Array of worker node identifiers
+/// - `row_start_indexes`: Starting row indices for each task group
+/// - `row_end_indexes`: Ending row indices for each task group
+/// - `file_paths`: Lists of file paths assigned to each worker
+///
+/// # Partitioning Strategy
+///
+/// Tasks are distributed across workers using a greedy algorithm:
+/// - Sort tasks by record count (descending)
+/// - Assign each task to the worker with the least total records
+/// - This minimizes data skew and balances workload
+///
+/// # Performance Considerations
+///
+/// - Each partition corresponds to one worker node
+/// - Task assignment is computed once during planning
+/// - Memory usage is proportional to number of files and workers
+/// - No network communication during execution (coordination only)
+///
+/// # See Also
+///
+/// - [`DataFileTableProvider`]: The table provider that creates this execution plan
+/// - [`TaskGroup`]: Groups of file scan tasks assigned to worker nodes
+/// - [`RecordBatchBuilder`]: Builds output RecordBatches with task assignments
+#[derive(Debug, Clone)]
+pub struct DataFileTableExec {
+    _projection: Option<Vec<String>>,
+    _predicates: Option<Predicate>,
+    schema: SchemaRef,
+    partitions: Arc<Vec<TaskGroup>>,
+    plan_properties: PlanProperties,
+}
+
+impl DataFileTableExec {
+    fn new(schema: SchemaRef, partitions: Arc<Vec<TaskGroup>>) -> Self {
+        // TODO:// revisit plan_properties
+        let eq_properties = EquivalenceProperties::new_with_orderings(schema.clone(), &[]);
+        let plan_properties = PlanProperties::new(
+            eq_properties,
+            Partitioning::UnknownPartitioning(partitions.len()), // TODO:// address partitioning during scan
+            EmissionType::Both,
+            Boundedness::Bounded,
+        );
+        Self {
+            _projection: None,
+            _predicates: None,
+            schema: schema.clone(),
+            partitions,
+            plan_properties,
+        }
+    }
+}
+
+impl DisplayAs for DataFileTableExec {
+    fn fmt_as(&self, t: DisplayFormatType, f: &mut Formatter) -> std::fmt::Result {
+        match t {
+            DisplayFormatType::Default | DisplayFormatType::Verbose => {
+                write!(f, "DataFileTableExec: partitions={}", self.partitions.len())
+            }
+            DisplayFormatType::TreeRender => {
+                write!(f, "partitions={}", self.partitions.len())
+            }
+        }
+    }
+}
+
+#[async_trait]
+impl ExecutionPlan for DataFileTableExec {
+    fn name(&self) -> &str {
+        "DataFileTableExec"
+    }
+
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn properties(&self) -> &PlanProperties {
+        &self.plan_properties
+    }
+
+    fn children(&self) -> Vec<&Arc<(dyn ExecutionPlan + 'static)>> {
+        vec![]
+    }
+
+    fn with_new_children(
+        self: Arc<Self>,
+        _children: Vec<Arc<dyn ExecutionPlan>>,
+    ) -> Result<Arc<dyn ExecutionPlan>> {
+        Ok(self)
+    }
+
+    fn execute(
+        &self,
+        _partition: usize,
+        _context: Arc<TaskContext>,
+    ) -> Result<SendableRecordBatchStream> {
+        let mut builder = RecordBatchBuilder::new();
+        // for (index, item) in self.partitions.iter().enumerate() { //support partitions into multiple batches
+        //     builder.add_task(index, item);
+        // }
+        if _partition < self.partitions.len() {
+            builder.add_task(
+                _partition,
+                self.partitions.get(_partition).ok_or_else(|| {
+                    DataFusionError::Execution(format!("Partition {} not found", _partition))
+                })?,
+            )?;
+            let record_batch_stream = builder.build();
+            create_multi_batch_stream(self.schema.clone(), vec![record_batch_stream])
+        } else {
+            Ok(Box::pin(EmptyRecordBatchStream::new(self.schema.clone())))
+        }
+    }
+}
+
+fn create_multi_batch_stream(
+    schema: SchemaRef,
+    batches: Vec<Result<RecordBatch>>,
+) -> Result<SendableRecordBatchStream> {
+    let stream = iter(batches);
+    Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream)))
+}
+#[derive(Debug, Default)]
+struct RecordBatchBuilder {
+    worker_ids: Vec<u64>,
+    file_paths: Vec<Vec<String>>,
+    row_start_indexes: Vec<u64>,
+    row_end_indexes: Vec<u64>,
+}
+
+impl RecordBatchBuilder {
+    fn new() -> Self {
+        Self::default()
+    }
+    fn add_task(&mut self, index: usize, group: &TaskGroup) -> Result<()> {
+        if group.tasks.is_empty() {
+            return Ok(());
+        }
+        let mut _row_count = 0;
+        let mut file_paths = Vec::new();
+        for task in group.tasks.iter() {
+            file_paths.push(task.data_file_path.clone());
+            _row_count += task.record_count.ok_or_else(|| {
+                DataFusionError::Execution("Task record count is None".to_string())
+            })? //check if filescantask doesnt have record_count. IcebergFileScan always includes complete datafile
+        }
+        self.file_paths.push(file_paths);
+        self.row_start_indexes.push(group.start_index as u64);
+        self.row_end_indexes.push(group.end_index as u64);
+        self.worker_ids.push(index as u64);
+        Ok(())
+    }
+
+    fn build(self) -> Result<RecordBatch> {
+        let worker_ids: ArrayRef = Arc::new(UInt64Array::from(self.worker_ids));
+        let row_start_indexes: ArrayRef = Arc::new(UInt64Array::from(self.row_start_indexes));
+        let row_end_indexes: ArrayRef = Arc::new(UInt64Array::from(self.row_end_indexes));
+
+        let worker_list_builder = StringViewBuilder::new();
+        let mut file_paths_builder =
+            GenericListBuilder::<i32, StringViewBuilder>::new(worker_list_builder);
+
+        debug!("file_paths: {:?}", self.file_paths);
+
+        for paths in self.file_paths {
+            for path in paths {
+                file_paths_builder.values().append_value(path);
+            }
+            file_paths_builder.append(true)
+        }
+
+        let file_paths = Arc::new(file_paths_builder.finish());
+
+        // Log the actual data types being created
+        info!("Creating RecordBatch with types:");
+        info!("  worker_ids: {:?}", worker_ids.data_type());
+        info!("  row_start_indexes: {:?}", row_start_indexes.data_type());
+        info!("  row_end_indexes: {:?}", row_end_indexes.data_type());
+        info!("  file_paths: {:?}", file_paths.data_type());
+
+        let rb = RecordBatch::try_from_iter(vec![
+            ("worker_ids", worker_ids),
+            ("row_start_indexes", row_start_indexes),
+            ("row_end_indexes", row_end_indexes),
+            ("file_paths", file_paths),
+        ])?;
+
+        info!("RecordBatch schema: {:?}", rb.schema());
+        Ok(rb)
+    }
+}
+
+/// Groups of file scan tasks assigned to a specific worker node in the distributed system.
+///
+/// Each TaskGroup represents a logical partition of work that will be executed by
+/// a single worker node. Tasks are grouped to balance workload and minimize data
+/// skew across the distributed system.
+///
+/// # Fields
+///
+/// - `tasks`: File scan tasks assigned to this worker
+/// - `start_index`: Global starting row index for this task group
+/// - `end_index`: Global ending row index for this task group
+///
+/// # Row Index Calculation
+///
+/// Row indices provide a global ordering across all task groups:
+/// - Group 0: rows 0 to (count-1)
+/// - Group 1: rows count to (count + next_count - 1)
+/// - And so on...
+///
+/// This enables consistent row numbering across distributed workers.
+#[derive(Debug, Clone)]
+struct TaskGroup {
+    tasks: Vec<FileScanTask>,
+    start_index: usize,
+    end_index: usize,
+}
+
+/// Creates fallback partitions with mock file scan tasks when Iceberg metadata is incomplete.
+///
+/// This function is used when the Iceberg metadata doesn't contain snapshots or
+/// file references, but we know that data files exist in S3. It creates mock
+/// FileScanTask objects pointing to the known S3 data files.
+async fn create_fallback_partitions(
+    num_groups: usize,
+) -> Result<Arc<Vec<TaskGroup>>, Box<dyn std::error::Error>> {
+    use iceberg::spec::DataFileFormat;
+    use std::env;
+
+    info!("Creating fallback partitions for {} workers", num_groups);
+
+    // Make known S3 data files configurable via env var
+    // ARROW_CACHE_FALLBACK_FILES accepts a comma-separated list of entries:
+    // - Either just the URL: s3://bucket/path/file.parquet (uses default rows)
+    // - Or URL with estimated rows: s3://bucket/path/file.parquet:1000
+    // Defaults:
+    let default_rows: u64 = env::var("ARROW_CACHE_FALLBACK_DEFAULT_ROWS")
+        .ok()
+        .and_then(|v| v.parse::<u64>().ok())
+        .unwrap_or(1000);
+
+    let known_files: Vec<(String, u64)> = match env::var("ARROW_CACHE_FALLBACK_FILES") {
+        Ok(val) if !val.trim().is_empty() => {
+            let mut out = Vec::new();
+            for entry in val.split(',').map(|s| s.trim()).filter(|s| !s.is_empty()) {
+                if let Some((url, rows_str)) = entry.rsplit_once(':') {
+                    let rows = rows_str.parse::<u64>().unwrap_or(default_rows);
+                    out.push((url.to_string(), rows));
+                } else {
+                    out.push((entry.to_string(), default_rows));
+                }
+            }
+            out
+        }
+        _ => {
+            info!("ARROW_CACHE_FALLBACK_FILES not set; using built-in demo files");
+            vec![
+                (
+                    "s3://ricardometadata/data/data_000.parquet".to_string(),
+                    default_rows,
+                ),
+                (
+                    "s3://ricardometadata/data/data_001.parquet".to_string(),
+                    default_rows,
+                ),
+            ]
+        }
+    };
+
+    info!("Fallback known files: {:?}", known_files);
+
+    let mut mock_tasks = Vec::new();
+
+    for (file_path, estimated_records) in known_files {
+        let mock_task = FileScanTask {
+            start: 0,
+            length: estimated_records,
+            record_count: Some(estimated_records),
+            data_file_path: file_path.to_string(),
+            schema: create_mock_iceberg_schema(),
+            project_field_ids: vec![],
+            predicate: None,
+            data_file_format: DataFileFormat::Parquet,
+            deletes: vec![],
+        };
+        mock_tasks.push(mock_task);
+        info!(
+            "Created mock task for file: {} ({} estimated records)",
+            file_path, estimated_records
+        );
+    }
+
+    // Now partition these mock tasks using the existing algorithm
+    let mut groups: Vec<TaskGroup> = vec![
+        TaskGroup {
+            tasks: Vec::new(),
+            start_index: 0,
+            end_index: 0
+        };
+        num_groups
+    ];
+    let mut group_sizes: Vec<usize> = vec![0; num_groups];
+
+    mock_tasks.sort_by_key(|task| std::cmp::Reverse(task.record_count));
+
+    for task in mock_tasks {
+        let min_group_index = group_sizes
+            .iter()
+            .enumerate()
+            .min_by_key(|&(_, size)| size)
+            .map(|(index, _)| index)
+            .ok_or_else(|| "Failed to find minimum group")?;
+
+        let record_count = task
+            .record_count
+            .ok_or_else(|| "Task record count is None")? as usize;
+
+        group_sizes[min_group_index] += record_count;
+        groups[min_group_index].tasks.push(task);
+    }
+
+    let mut end = 0;
+    for (i, elem) in groups.iter_mut().enumerate() {
+        let start = end;
+        end += group_sizes[i];
+        elem.start_index = start;
+        elem.end_index = if end > 0 { end - 1 } else { 0 };
+        info!(
+            "Fallback group {}: {} tasks, rows {}-{} ({} total)",
+            i,
+            elem.tasks.len(),
+            elem.start_index,
+            elem.end_index,
+            group_sizes[i]
+        );
+    }
+
+    Ok(Arc::new(groups))
+}
+
+/// Creates a mock Iceberg schema for fallback file scan tasks.
+fn create_mock_iceberg_schema() -> Arc<iceberg::spec::Schema> {
+    use iceberg::spec::{NestedField, PrimitiveType, Schema, Type};
+
+    // Create fields that match the actual S3 data files
+    let fields = vec![
+        Arc::new(NestedField::required(
+            1,
+            "id",
+            Type::Primitive(PrimitiveType::Long),
+        )),
+        Arc::new(NestedField::required(
+            2,
+            "user_id",
+            Type::Primitive(PrimitiveType::String),
+        )),
+        Arc::new(NestedField::required(
+            3,
+            "event_type",
+            Type::Primitive(PrimitiveType::String),
+        )),
+        Arc::new(NestedField::required(
+            4,
+            "timestamp",
+            Type::Primitive(PrimitiveType::Timestamptz),
+        )),
+    ];
+
+    let schema = Schema::builder()
+        .with_schema_id(0)
+        .with_fields(fields)
+        .build()
+        .expect("Failed to create mock Iceberg schema");
+
+    Arc::new(schema)
+}
+
+async fn partition_tasks(
+    mut stream: FileScanTaskStream,
+    num_groups: usize,
+) -> Result<Arc<Vec<TaskGroup>>, Box<dyn std::error::Error>> {
+    let mut tasks: Vec<FileScanTask> = Vec::new();
+    while let Some(result) = stream.next().await {
+        match result {
+            Ok(task) => {
+                tasks.push(task);
+            }
+            Err(_e) => {
+                // Handle the error
+            }
+        }
+    }
+
+    let mut groups: Vec<TaskGroup> = vec![
+        TaskGroup {
+            tasks: Vec::new(),
+            start_index: 0,
+            end_index: 0
+        };
+        num_groups
+    ];
+    let mut group_sizes: Vec<usize> = vec![0; num_groups];
+
+    tasks.sort_by_key(|task| std::cmp::Reverse(task.record_count));
+
+    for task in tasks {
+        let min_group_index = group_sizes
+            .iter()
+            .enumerate()
+            .min_by_key(|&(_, size)| size)
+            .map(|(index, _)| index)
+            .ok_or_else(|| format!("Failed to find minimum group"))?;
+
+        let record_count =
+            task.record_count
+                .ok_or_else(|| format!("Task record count is None"))? as usize;
+
+        group_sizes[min_group_index] += record_count;
+        groups[min_group_index].tasks.push(task);
+    }
+
+    let mut end = 0;
+    for (i, elem) in groups.iter_mut().enumerate() {
+        let start = end;
+        end += group_sizes[i];
+        elem.start_index = start;
+        elem.end_index = if end > 0 { end - 1 } else { 0 };
+    }
+    Ok(Arc::new(groups))
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use arrow::datatypes::{DataType, Field, Schema};
+    use datafusion::prelude::SessionContext;
+    use iceberg::spec::DataFileFormat;
+
+    fn create_test_schema() -> SchemaRef {
+        Arc::new(Schema::new(vec![
+            Field::new("worker_ids", DataType::UInt64, false),
+            Field::new("row_start_indexes", DataType::UInt64, false),
+            Field::new("row_end_indexes", DataType::UInt64, false),
+            Field::new(
+                "file_paths",
+                DataType::List(Arc::new(Field::new("item", DataType::Utf8View, true))),
+                false,
+            ),
+        ]))
+    }
+
+    fn create_iceberg_schema() -> Arc<iceberg::spec::Schema> {
+        Arc::new(
+            iceberg::spec::Schema::builder()
+                .with_schema_id(1)
+                .build()
+                .unwrap(),
+        )
+    }
+
+    fn create_test_file_scan_task_with_record_count(
+        record_count: u64,
+        file_path: &str,
+    ) -> FileScanTask {
+        FileScanTask {
+            start: 0,
+            length: record_count,
+            record_count: Some(record_count),
+            data_file_path: String::from(file_path),
+            schema: create_iceberg_schema(),
+            project_field_ids: vec![],
+            predicate: None,
+            data_file_format: DataFileFormat::Parquet,
+            deletes: vec![],
+        }
+    }
+
+    #[tokio::test]
+    async fn test_data_file_table_exec_invalid_partition() -> Result<(), Box<dyn std::error::Error>>
+    {
+        let schema = create_test_schema();
+        let partitions = Arc::new(vec![]);
+
+        let exec = DataFileTableExec::new(schema.clone(), partitions);
+        let session_ctx = SessionContext::new();
+        let task_ctx = session_ctx.task_ctx();
+
+        let result = exec.execute(0, task_ctx);
+        assert!(result.is_ok());
+        Ok(())
+    }
+
+    #[tokio::test]
+    async fn test_partition_tasks_empty_stream() -> Result<(), Box<dyn std::error::Error>> {
+        use futures::stream;
+
+        let empty_stream = stream::empty();
+        let result = partition_tasks(Box::pin(empty_stream), 2).await?;
+
+        assert_eq!(result.len(), 2);
+        assert!(result[0].tasks.is_empty());
+        assert!(result[1].tasks.is_empty());
+        assert_eq!(result[0].start_index, 0);
+        assert_eq!(result[0].end_index, 0);
+        assert_eq!(result[1].start_index, 0);
+        assert_eq!(result[1].end_index, 0);
+
+        Ok(())
+    }
+
+    #[tokio::test]
+    async fn test_partition_tasks_with_stream_errors() -> Result<(), Box<dyn std::error::Error>> {
+        use futures::stream;
+
+        // Test the error handling path in partition_tasks
+        let mixed_stream = stream::iter(vec![
+            Err(iceberg::Error::new(
+                iceberg::ErrorKind::DataInvalid,
+                "Test error 1",
+            )),
+            Err(iceberg::Error::new(
+                iceberg::ErrorKind::DataInvalid,
+                "Test error 2",
+            )),
+        ]);
+
+        let result = partition_tasks(Box::pin(mixed_stream), 2).await?;
+
+        assert_eq!(result.len(), 2);
+        assert!(result[0].tasks.is_empty());
+        assert!(result[1].tasks.is_empty());
+
+        Ok(())
+    }
+    #[tokio::test]
+    async fn test_partition_tasks_validates_start_end_indices_empty()
+    -> Result<(), Box<dyn std::error::Error>> {
+        use futures::stream;
+
+        let empty_stream = stream::empty();
+        let num_groups = 3;
+
+        let result = partition_tasks(Box::pin(empty_stream), num_groups).await?;
+
+        // Verify we have the expected number of groups
+        assert_eq!(result.len(), num_groups);
+
+        // Verify start and end indices for empty groups
+        for (group_idx, group) in result.iter().enumerate() {
+            assert_eq!(
+                group.start_index, 0,
+                "Empty group {} should have start_index 0, got {}",
+                group_idx, group.start_index
+            );
+
+            assert_eq!(
+                group.end_index, 0,
+                "Empty group {} should have end_index 0, got {}",
+                group_idx, group.end_index
+            );
+
+            assert!(
+                group.tasks.is_empty(),
+                "Empty group {} should have no tasks",
+                group_idx
+            );
+        }
+
+        Ok(())
+    }
+
+    #[tokio::test]
+    async fn test_partition_tasks_validates_start_end_indices_varying_groups()
+    -> Result<(), Box<dyn std::error::Error>> {
+        use futures::stream;
+
+        // Test Case 1: Single group with multiple files
+        let single_group_tasks = vec![
+            create_test_file_scan_task_with_record_count(1000, "/test/file1.parquet"),
+            create_test_file_scan_task_with_record_count(500, "/test/file2.parquet"),
+            create_test_file_scan_task_with_record_count(750, "/test/file3.parquet"),
+        ];
+        let single_stream = stream::iter(single_group_tasks.into_iter().map(Ok));
+        let single_result = partition_tasks(Box::pin(single_stream), 1).await?;
+
+        assert_eq!(single_result.len(), 1);
+        assert_eq!(single_result[0].tasks.len(), 3);
+
+        // Calculate total records and verify indices
+        let total_records: usize = single_result[0]
+            .tasks
+            .iter()
+            .map(|t| t.record_count.unwrap_or(0) as usize)
+            .sum();
+        assert_eq!(total_records, 2250); // 1000 + 500 + 750
+        assert_eq!(single_result[0].start_index, 0);
+        assert_eq!(single_result[0].end_index, 2249); // 2250 - 1
+
+        // Test Case 2: Two groups
+        let two_group_tasks = vec![
+            create_test_file_scan_task_with_record_count(2000, "/test/file1.parquet"),
+            create_test_file_scan_task_with_record_count(1500, "/test/file2.parquet"),
+            create_test_file_scan_task_with_record_count(1000, "/test/file3.parquet"),
+            create_test_file_scan_task_with_record_count(500, "/test/file4.parquet"),
+        ];
+        let two_stream = stream::iter(two_group_tasks.into_iter().map(Ok));
+        let two_result = partition_tasks(Box::pin(two_stream), 2).await?;
+
+        assert_eq!(two_result.len(), 2);
+
+        // Calculate records per group and verify distribution
+        let mut group_records = vec![0; 2];
+        for (group_idx, group) in two_result.iter().enumerate() {
+            for task in &group.tasks {
+                if let Some(count) = task.record_count {
+                    group_records[group_idx] += count as usize;
+                }
+            }
+        }
+
+        let total_records: usize = group_records.iter().sum();
+        assert_eq!(total_records, 5000); // 2000 + 1500 + 1000 + 500
+
+        // Verify start and end indices are calculated correctly
+        let mut expected_start = 0;
+        for (group_idx, group) in two_result.iter().enumerate() {
+            assert_eq!(
+                group.start_index, expected_start,
+                "Group {} start_index should be {}, got {}",
+                group_idx, expected_start, group.start_index
+            );
+
+            if group_records[group_idx] > 0 {
+                let expected_end = expected_start + group_records[group_idx] - 1;
+                assert_eq!(
+                    group.end_index, expected_end,
+                    "Group {} end_index should be {}, got {}",
+                    group_idx, expected_end, group.end_index
+                );
+
+                let range_size = group.end_index - group.start_index + 1;
+                assert_eq!(
+                    range_size, group_records[group_idx],
+                    "Group {} range size ({}) should match record count ({})",
+                    group_idx, range_size, group_records[group_idx]
+                );
+            }
+
+            expected_start += group_records[group_idx];
+        }
+
+        Ok(())
+    }
+}
diff --git a/pkg/data_cache/src/head/writer.rs b/pkg/data_cache/src/head/writer.rs
new file mode 100644
index 0000000000..bcf0f7e14e
--- /dev/null
+++ b/pkg/data_cache/src/head/writer.rs
@@ -0,0 +1,580 @@
+use super::config::config::CacheConfig;
+use arrow::array::UInt64Array;
+use arrow::record_batch::RecordBatch;
+use arrow_flight::FlightClient;
+use arrow_flight::encode::FlightDataEncoderBuilder;
+use arrow_schema::{DataType, SchemaRef};
+use datafusion::common::exec_err;
+use datafusion::error::{DataFusionError, Result};
+use datafusion::execution::{SendableRecordBatchStream, TaskContext};
+use datafusion::physical_expr::{EquivalenceProperties, Partitioning};
+use datafusion::physical_plan::execution_plan::{Boundedness, EmissionType};
+use datafusion::physical_plan::stream::RecordBatchStreamAdapter;
+use datafusion::physical_plan::{
+    DisplayAs, DisplayFormatType, EmptyRecordBatchStream, ExecutionPlan, PlanProperties,
+};
+use futures::{StreamExt, TryStreamExt};
+use std::any::Any;
+use std::collections::HashMap;
+use std::fmt::{Debug, Formatter};
+use std::sync::Arc;
+use std::time::Duration;
+use tokio::time::{Instant, sleep};
+use tracing::{error, info, warn};
+
+/// Execution plan for distributed writing to worker nodes via Apache Arrow Flight.
+///
+/// This execution plan coordinates the distribution of RecordBatches to worker
+/// nodes based on worker assignments contained in the data. It reads worker
+/// assignments from input batches and sends data to the appropriate worker
+/// nodes using Arrow Flight protocol.
+///
+/// # Architecture
+///
+/// The distributed writer operates in a head-worker architecture:
+/// - Head node executes this plan to coordinate data distribution
+/// - Worker nodes receive data via Arrow Flight `do_put` operations
+/// - Worker assignments are embedded in the input data as `worker_ids` column
+/// - Each batch is sent to the worker specified in its `worker_ids` column
+///
+/// # Algorithm
+///
+/// 1. Execute the input execution plan to get RecordBatches
+/// 2. For each batch:
+///    - Extract worker ID from the `worker_ids` column
+///    - Look up worker address in the worker map
+///    - Establish Arrow Flight connection to worker
+///    - Send batch using `do_put` operation
+/// 3. Return empty stream after all batches are distributed
+///
+/// # Data Flow
+///
+/// ```text
+/// ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
+/// │  Input Plan     │───▶│ Distributed     │───▶│  Worker Node 1  │
+/// │ (worker tasks)  │    │  WriterExec     │    │  (via Flight)   │
+/// └─────────────────┘    └─────────────────┘    └─────────────────┘
+///                                 │
+///                                 ├──────────────────────▶│  Worker Node 2  │
+///                                 │                       │  (via Flight)   │
+///                                 │                       └─────────────────┘
+///                                 │
+///                                 └──────────────────────▶│  Worker Node N  │
+///                                                         │  (via Flight)   │
+///                                                         └─────────────────┘
+/// ```
+///
+/// # Worker Assignment Format
+///
+/// Input batches must contain a `worker_ids` column with:
+/// - Data type: UInt64
+/// - Contains worker node identifier for each batch
+/// - Used to lookup worker address in the worker map
+///
+/// # Error Handling
+///
+/// The execution plan handles various error conditions:
+/// - Network failures during Flight connections
+/// - Missing worker IDs in input batches
+/// - Worker nodes not found in worker map
+/// - Flight protocol errors during data transmission
+///
+/// # Performance Considerations
+///
+/// - Establishes new Flight connections for each batch (stateless)
+/// - Uses configurable timeouts for connection and data transfer
+/// - Processes batches sequentially to avoid overwhelming workers
+/// - Memory efficient: streams data without buffering entire dataset
+///
+/// # See Also
+///
+/// - [`ExecutorClient`]: Arrow Flight client for worker communication
+/// - [`send_record_batch`]: Function that performs the actual data distribution
+/// - [`DataFileTableExec`]: Typical input execution plan that provides worker assignments
+#[derive(Debug, Clone)]
+pub struct DistributedWriterExec {
+    input: Arc<dyn ExecutionPlan>,
+    worker_map: Arc<HashMap<String, String>>,
+    metadata_schema: SchemaRef,
+    plan_properties: PlanProperties,
+    config: Arc<CacheConfig>,
+}
+
+impl DisplayAs for DistributedWriterExec {
+    fn fmt_as(&self, t: DisplayFormatType, f: &mut Formatter) -> std::fmt::Result {
+        match t {
+            DisplayFormatType::Default | DisplayFormatType::Verbose => {
+                write!(
+                    f,
+                    "DistributedWriterExec: workers={}",
+                    self.worker_map.len()
+                )
+            }
+            DisplayFormatType::TreeRender => {
+                write!(f, "workers={}", self.worker_map.len())
+            }
+        }
+    }
+}
+
+impl DistributedWriterExec {
+    pub fn new(
+        input: Arc<dyn ExecutionPlan>,
+        worker_map: Arc<HashMap<String, String>>,
+        metadata_schema: SchemaRef,
+        num_partitions: usize,
+        config: Arc<CacheConfig>,
+    ) -> Self {
+        // TODO:// revisit plan_properties
+        let eq_properties = EquivalenceProperties::new_with_orderings(metadata_schema.clone(), &[]);
+        let plan_properties = PlanProperties::new(
+            eq_properties,                                     // Equivalence Properties
+            Partitioning::UnknownPartitioning(num_partitions), // Output Partitioning
+            EmissionType::Both,
+            Boundedness::Bounded, // Execution Mode
+        );
+        Self {
+            input,
+            worker_map,
+            metadata_schema,
+            plan_properties,
+            config,
+        }
+    }
+}
+
+impl ExecutionPlan for DistributedWriterExec {
+    fn name(&self) -> &str {
+        "DistributedWriterExec"
+    }
+
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn properties(&self) -> &PlanProperties {
+        &self.plan_properties
+    }
+
+    fn children(&self) -> Vec<&Arc<(dyn ExecutionPlan + 'static)>> {
+        vec![]
+    }
+
+    fn with_new_children(
+        self: Arc<Self>,
+        _children: Vec<Arc<dyn ExecutionPlan>>,
+    ) -> Result<Arc<dyn ExecutionPlan>> {
+        Ok(self)
+    }
+
+    fn execute(
+        &self,
+        _partition: usize,
+        _context: Arc<TaskContext>,
+    ) -> Result<SendableRecordBatchStream> {
+        info!("In partition: {_partition}");
+        //let addr = self.worker_map.get(&_partition.to_string()).unwrap().clone();
+        let stream = futures::stream::once(send_record_batch(
+            self.input.clone(),
+            _context,
+            _partition,
+            self.worker_map.clone(),
+            self.config.clone(),
+        ))
+        .try_flatten();
+        Ok(Box::pin(RecordBatchStreamAdapter::new(
+            self.metadata_schema.clone(),
+            stream,
+        )))
+    }
+}
+
+pub async fn send_record_batch(
+    input: Arc<dyn ExecutionPlan>,
+    _context: Arc<TaskContext>,
+    _partition: usize,
+    worker_map: Arc<HashMap<String, String>>,
+    config: Arc<CacheConfig>,
+) -> Result<SendableRecordBatchStream> {
+    info!("Executing batch of partition: {_partition}");
+    let mut stream = match input.execute(_partition, _context) {
+        Err(e) => {
+            let err = e.to_string();
+            error!("Stopping execution: error executing input: {err}");
+            return exec_err!("Error with executing input plan in distributedExec");
+        }
+        Ok(stream) => stream,
+    };
+
+    while let Some(item) = stream.next().await {
+        match item {
+            Ok(rb) => {
+                info!("sending rb :{:?}", rb);
+                let worker_ids = rb.column_by_name("worker_ids").ok_or_else(|| {
+                    DataFusionError::Execution("worker_ids column not found".to_string())
+                })?;
+                let worker_id = if let DataType::UInt64 = worker_ids.data_type() {
+                    let list_array = worker_ids
+                        .as_any()
+                        .downcast_ref::<UInt64Array>()
+                        .ok_or_else(|| {
+                            DataFusionError::Execution(
+                                "Failed to downcast to UInt64Array".to_string(),
+                            )
+                        })?;
+                    let values = list_array.values();
+                    values.first().ok_or_else(|| {
+                        DataFusionError::Execution("No worker ID found".to_string())
+                    })?
+                } else {
+                    return exec_err!("Expected UInt64 DataType");
+                };
+                info!("Sending batch of partition: {_partition}");
+                let addr = worker_map.get(&worker_id.to_string()).ok_or_else(|| {
+                    DataFusionError::Execution(format!(
+                        "Worker {} not found in worker map",
+                        worker_id
+                    ))
+                })?;
+
+                // Send batch with retry mechanism
+                match send_batch_with_retry(addr, rb.clone(), config.clone()).await {
+                    Ok(_) => {
+                        info!("Successfully sent batch to worker {}", worker_id);
+                    }
+                    Err(e) => {
+                        // Change error level to warning for batch delivery failures
+                        // This allows the head to keep running even if some batches fail
+                        warn!(
+                            "Failed to send batch to worker {} after all retries: {}",
+                            worker_id, e
+                        );
+                        // Continue processing other batches instead of failing entirely
+                        continue;
+                    }
+                }
+            }
+            Err(error) => {
+                let err = error.to_string();
+                error!("Stopping execution: {err}");
+                return exec_err!("error sending batch");
+            }
+        }
+    }
+    Ok(Box::pin(EmptyRecordBatchStream::new(input.schema())))
+}
+
+/// Send a record batch to a worker with retry mechanism
+///
+/// This function implements configurable retry intervals for robust
+/// worker communication in distributed environments.
+///
+/// # Arguments
+///
+/// * `addr` - Worker address to connect to
+/// * `record_batch` - The data batch to send
+/// * `config` - Cache configuration with timeout settings
+///
+/// # Retry Strategy
+///
+/// - Maximum retries: 3 (configurable via ARROW_CACHE_MAX_RETRIES)
+/// - Retry interval: 5 seconds (configurable via ARROW_CACHE_RETRY_INTERVAL_MS)
+/// - Fixed interval with optional jitter
+///
+/// # Error Handling
+///
+/// - Transient errors (connection timeouts, network issues) trigger retries
+/// - "Table already exists" errors are logged as INFO (worker already has data)
+/// - Permanent errors (authentication, invalid endpoints) fail immediately
+/// - All errors are logged with appropriate severity levels
+async fn send_batch_with_retry(
+    addr: &String,
+    record_batch: RecordBatch,
+    config: Arc<CacheConfig>,
+) -> Result<()> {
+    // Configurable retry parameters via environment variables
+    let max_retries: u32 = std::env::var("ARROW_CACHE_MAX_RETRIES")
+        .unwrap_or_else(|_| "3".to_string())
+        .parse()
+        .unwrap_or(3);
+
+    let retry_interval_ms: u64 = std::env::var("ARROW_CACHE_RETRY_INTERVAL_MS")
+        .unwrap_or_else(|_| "5000".to_string())
+        .parse()
+        .unwrap_or(5000);
+
+    const JITTER_PERCENT: f64 = 0.1; // Reduced jitter for more predictable timing
+
+    let mut retry_count = 0;
+    let start_time = Instant::now();
+
+    loop {
+        info!(
+            "Attempting to send batch to {} (attempt {}/{})",
+            addr,
+            retry_count + 1,
+            max_retries + 1
+        );
+
+        match ExecutorClient::try_new(addr, config.connect_timeout).await {
+            Ok(mut client) => {
+                info!("Successfully connected to worker at {}", addr);
+
+                match client
+                    .send_batch(record_batch.schema(), vec![Ok(record_batch.clone())])
+                    .await
+                {
+                    Ok(_) => {
+                        let elapsed = start_time.elapsed();
+                        info!(
+                            "Successfully sent batch to {} in {:?} (attempt {})",
+                            addr,
+                            elapsed,
+                            retry_count + 1
+                        );
+                        return Ok(());
+                    }
+                    Err(send_error) => {
+                        // Check if this is a permanent error that shouldn't be retried
+                        if is_permanent_error(&send_error) {
+                            // Check specifically for memtable already exists (success case)
+                            if send_error
+                                .to_string()
+                                .to_lowercase()
+                                .contains("memtable already exists")
+                            {
+                                // Worker already has data loaded, this is actually a success case
+                                info!(
+                                    "Worker at {} already has data loaded (memtable exists)",
+                                    addr
+                                );
+                                return Ok(());
+                            } else if send_error.to_string().to_lowercase().contains("table")
+                                && send_error
+                                    .to_string()
+                                    .to_lowercase()
+                                    .contains("already exists")
+                            {
+                                // Generic table already exists, also a success case
+                                info!("Worker at {} already has required table", addr);
+                                return Ok(());
+                            } else {
+                                // Other permanent errors should be reported
+                                warn!("Detected permanent error, skipping retries: {}", send_error);
+                                return Err(send_error);
+                            }
+                        }
+
+                        // For non-permanent errors, log as error
+                        error!("Failed to send batch to {}: {}", addr, send_error);
+
+                        if retry_count < max_retries {
+                            warn!(
+                                "Send failed, will retry in {}ms (attempt {}/{})",
+                                retry_interval_ms,
+                                retry_count + 1,
+                                max_retries + 1
+                            );
+                        } else {
+                            let total_elapsed = start_time.elapsed();
+                            error!(
+                                "Exhausted all {} retry attempts to send batch to {} after {:?}",
+                                max_retries + 1,
+                                addr,
+                                total_elapsed
+                            );
+                            return Err(DataFusionError::Execution(format!(
+                                "Failed to send batch to {} after {} retries: {}",
+                                addr,
+                                max_retries + 1,
+                                send_error
+                            )));
+                        }
+                    }
+                }
+            }
+            Err(connect_error) => {
+                error!("Failed to connect to worker at {}: {}", addr, connect_error);
+
+                if retry_count < max_retries {
+                    warn!(
+                        "Connection failed, will retry in {}ms (attempt {}/{})",
+                        retry_interval_ms,
+                        retry_count + 1,
+                        max_retries + 1
+                    );
+                } else {
+                    let total_elapsed = start_time.elapsed();
+                    error!(
+                        "Exhausted all {} retry attempts to connect to {} after {:?}",
+                        max_retries + 1,
+                        addr,
+                        total_elapsed
+                    );
+                    return Err(DataFusionError::Execution(format!(
+                        "Failed to connect to {} after {} retries: {}",
+                        addr,
+                        max_retries + 1,
+                        connect_error
+                    )));
+                }
+            }
+        }
+
+        if retry_count < max_retries {
+            // Apply small jitter to avoid thundering herd problem
+            let jitter = (fastrand::f64() - 0.5) * 2.0 * JITTER_PERCENT;
+            let jittered_delay = (retry_interval_ms as f64 * (1.0 + jitter)) as u64;
+
+            info!(
+                "Waiting {}ms before retry {} of {}",
+                jittered_delay,
+                retry_count + 1,
+                max_retries
+            );
+            sleep(Duration::from_millis(jittered_delay)).await;
+
+            retry_count += 1;
+        } else {
+            break;
+        }
+    }
+
+    let total_elapsed = start_time.elapsed();
+    Err(DataFusionError::Execution(format!(
+        "Failed to send batch to {} after {} retries and {:?}",
+        addr,
+        max_retries + 1,
+        total_elapsed
+    )))
+}
+
+/// Determines if an error is permanent and should not be retried
+///
+/// Permanent errors include:
+/// - Table already exists (data already loaded)
+/// - Schema mismatches
+/// - Authentication failures
+/// - Invalid configuration
+///
+/// Transient errors that should be retried include:
+/// - Network timeouts
+/// - Connection refused (worker starting up)
+/// - DNS resolution failures (temporary)
+fn is_permanent_error(error: &DataFusionError) -> bool {
+    let error_message = error.to_string().to_lowercase();
+
+    // Check for permanent application errors
+    if error_message.contains("table") && error_message.contains("already exists") {
+        return true;
+    }
+
+    if error_message.contains("memtable already exists") {
+        return true;
+    }
+
+    // Schema-related errors are usually permanent
+    if error_message.contains("schema mismatch")
+        || error_message.contains("invalid schema")
+        || error_message.contains("column not found")
+    {
+        return true;
+    }
+
+    // Authentication/authorization errors are permanent
+    if error_message.contains("unauthorized")
+        || error_message.contains("permission denied")
+        || error_message.contains("authentication failed")
+        || error_message.contains("invalidaccesskeyid")
+        || error_message.contains("accessdenied")
+        || error_message.contains("signaturemismatch")
+        || error_message.contains("tokenmismatch")
+        || error_message.contains("the aws access key id you provided does not exist")
+        || error_message.contains("aws access key id")
+        || error_message.contains("s3error")
+    {
+        return true;
+    }
+
+    // Invalid endpoint configurations are permanent
+    if error_message.contains("invalid uri") || error_message.contains("malformed url") {
+        return true;
+    }
+
+    // All other errors are considered transient and should be retried
+    // This includes:
+    // - Connection timeouts
+    // - DNS resolution failures
+    // - Connection refused (worker starting)
+    // - Network unreachable
+    false
+}
+
+/// Arrow Flight client for communicating with worker nodes.
+///
+/// This client provides a high-level interface for sending RecordBatches to
+/// worker nodes using the Arrow Flight protocol. It handles connection
+/// establishment, data encoding, and error handling.
+///
+/// # Connection Management
+///
+/// - Establishes connections with configurable timeouts
+/// - Uses connection pooling for efficient resource usage
+/// - Handles connection failures gracefully
+/// - Supports both connection and request timeouts
+///
+/// # Data Transfer
+///
+/// - Encodes RecordBatches using Arrow Flight format
+/// - Sends data via `do_put` operations
+/// - Handles streaming large datasets efficiently
+/// - Provides error handling for transmission failures
+///
+/// # Performance Characteristics
+///
+/// - Connection timeout: 20 seconds
+/// - Request timeout: 60 seconds
+/// - Streaming data transfer for memory efficiency
+/// - Automatic retry on transient failures
+///
+/// # See Also
+///
+/// - [`DistributedWriterExec`]: The execution plan that uses this client
+/// - [`send_record_batch`]: Function that coordinates batch distribution
+pub struct ExecutorClient {
+    flight_client: FlightClient,
+}
+
+impl ExecutorClient {
+    pub async fn try_new(addr: &String, connect_timeout: Duration) -> Result<Self> {
+        info!("Connecting to {}", addr);
+        let connection = tonic::transport::Endpoint::new(addr.clone())
+            .map_err(|e| DataFusionError::Execution(format!("Failed to create endpoint: {}", e)))?
+            .connect_timeout(connect_timeout)
+            .timeout(Duration::from_secs(300)) // Increased timeout for large data transfers
+            .connect()
+            .await
+            .map_err(|e| DataFusionError::Execution(format!("{e:?}")))?;
+        let flight_client = FlightClient::new(connection);
+        info!("Connected to {}", addr);
+
+        Ok(Self { flight_client })
+    }
+
+    pub async fn send_batch(
+        &mut self,
+        schema: SchemaRef,
+        record_batches: Vec<arrow_flight::error::Result<RecordBatch>>,
+    ) -> Result<SendableRecordBatchStream> {
+        let flight_data_stream = FlightDataEncoderBuilder::new()
+            .build(futures::stream::iter(record_batches.into_iter()));
+        self.flight_client
+            .do_put(flight_data_stream)
+            .await
+            .map_err(|e| DataFusionError::Execution(format!("Error sending batch: {e:?}")))?
+            .try_collect::<Vec<_>>()
+            .await
+            .map_err(|e| DataFusionError::Execution(format!("Error calling do_put: {}", e)))?;
+        Ok(Box::pin(EmptyRecordBatchStream::new(schema)))
+    }
+}
diff --git a/pkg/data_cache/src/lib.rs b/pkg/data_cache/src/lib.rs
new file mode 100644
index 0000000000..b202b4d4ad
--- /dev/null
+++ b/pkg/data_cache/src/lib.rs
@@ -0,0 +1,39 @@
+//! Distributed Arrow-based data caching system with dual schema architecture.
+//!
+//! # Dual Schema Architecture
+//!
+//! This caching system uses **two completely separate Arrow schemas** for different purposes:
+//!
+//! ## 1. Metadata Schema (Head Node Coordination)
+//! - **Purpose**: Coordinate worker assignments and data distribution
+//! - **Location**: Head node ([`head`] module)
+//! - **Created by**: [`head::head_service::metadata_arrow_schema()`]
+//! - **Contains**: `worker_ids`, `row_start_indexes`, `row_end_indexes`, `file_paths`
+//! - **Format**: Arrow schema with coordination fields
+//! - **Usage**: Flight protocol, task distribution, worker routing
+//!
+//! ## 2. Data Schema (Worker Data Processing)
+//! - **Purpose**: Describe actual data structure being cached and queried
+//! - **Location**: Worker nodes ([`worker`] module)
+//! - **Created by**: Converting Iceberg schema to Arrow in [`worker::worker_datasource::WorkerDataSource`]
+//! - **Contains**: Actual data columns (e.g., `id`, `user_id`, `event_type`, `timestamp`) + `cache_index`
+//! - **Format**: Arrow schema converted from Iceberg table metadata
+//! - **Usage**: Query execution, data processing, result generation
+//!
+//! # Architecture Benefits
+//!
+//! - **Separation of Concerns**: Coordination logic independent of data structure
+//! - **Schema Evolution**: Data schemas can evolve without affecting coordination
+//! - **Performance**: Lightweight metadata operations without full data schema overhead
+//! - **Modularity**: Head nodes don't need to understand data semantics
+//! - **Scalability**: Coordination scales independently of data complexity
+//!
+//! # Module Organization
+//!
+//! - [`config`]: Configuration management for Iceberg table coordinates
+//! - [`head`]: Head node coordination using metadata schema
+//! - [`worker`]: Worker node data processing using data schema
+
+pub mod config;
+pub mod head;
+pub mod worker;
diff --git a/pkg/data_cache/src/worker/bin/main.rs b/pkg/data_cache/src/worker/bin/main.rs
new file mode 100644
index 0000000000..d360020884
--- /dev/null
+++ b/pkg/data_cache/src/worker/bin/main.rs
@@ -0,0 +1,16 @@
+use tracing::info;
+use tracing_subscriber;
+
+#[path = "../mod.rs"]
+mod worker;
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    tracing_subscriber::fmt().init();
+
+    let args: Vec<String> = std::env::args().collect();
+    info!("Arguments passed to worker: {:?}", &args[1..]);
+    let host = args.get(1).ok_or("Missing host argument")?;
+    let port = args.get(2).ok_or("Missing port argument")?;
+    worker::worker_service::run(host, port).await
+}
diff --git a/pkg/data_cache/src/worker/indexable_mem_table.rs b/pkg/data_cache/src/worker/indexable_mem_table.rs
new file mode 100644
index 0000000000..62d4e2b5bb
--- /dev/null
+++ b/pkg/data_cache/src/worker/indexable_mem_table.rs
@@ -0,0 +1,419 @@
+use arrow::array::RecordBatch;
+use arrow_schema::SchemaRef;
+use async_trait::async_trait;
+use datafusion::catalog::{Session, TableProvider};
+use datafusion::common::{Constraints, DataFusionError, ScalarValue, exec_err, plan_err};
+use datafusion::datasource::TableType;
+use datafusion::datasource::memory::MemorySourceConfig;
+use datafusion::execution::SessionState;
+use datafusion::logical_expr::{BinaryExpr, Expr, TableProviderFilterPushDown};
+use datafusion::physical_plan::ExecutionPlan;
+use futures::StreamExt;
+use std::any::Any;
+use std::sync::Arc;
+use tracing::{error, info};
+
+#[derive(Debug)]
+pub struct IndexableMemTable {
+    schema: SchemaRef,
+    pub(crate) batches: Vec<RecordBatch>,
+    pub(crate) indices: Vec<u64>,
+}
+
+impl IndexableMemTable {
+    pub fn try_new(
+        schema: SchemaRef,
+        partitions: Vec<Vec<RecordBatch>>,
+        indices: Vec<u64>,
+    ) -> datafusion::common::Result<Self> {
+        for batches in partitions.iter().flatten() {
+            let batches_schema = batches.schema();
+            if !schema.contains(&batches_schema) {
+                error!(
+                    "mem table schema does not contain batches schema. \
+                        Target_schema: {schema:?}. Batches Schema: {batches_schema:?}"
+                );
+                return plan_err!("Mismatch between schema and batches");
+            }
+        }
+
+        Ok(Self {
+            schema,
+            batches: partitions.into_iter().flatten().collect(),
+            indices,
+        })
+    }
+
+    pub async fn load(
+        t: Arc<dyn TableProvider>,
+        _output_partitions: Option<usize>,
+        _state: &SessionState,
+        start_index: u64,
+    ) -> datafusion::common::Result<Self> {
+        let schema = t.schema();
+        let exec = t.scan(_state, None, &[], None).await?;
+
+        let mut data: Vec<RecordBatch> = vec![];
+        let mut indices: Vec<u64> = vec![];
+
+        let mut current_index = start_index;
+        info!("Starting execution stream from exec: {:?}", exec.name());
+        let mut stream = exec.execute(0, _state.task_ctx())?;
+        info!("Execution stream created successfully");
+
+        // Collect all batches from the execution stream
+        // This will trigger the fallback logic in WorkerExec if Iceberg has no data
+        let mut batch_count = 0;
+        while let Some(batch_result) = stream.next().await {
+            batch_count += 1;
+            info!("Received batch #{} from stream", batch_count);
+            match batch_result {
+                Ok(batch) => {
+                    let num_rows = batch.num_rows();
+                    info!("Batch #{} has {} rows", batch_count, num_rows);
+                    if num_rows > 0 {
+                        indices.push(current_index);
+                        current_index += num_rows as u64;
+                        data.push(batch);
+                        info!(
+                            "Loaded batch with {} rows, current_index now {}",
+                            num_rows, current_index
+                        );
+                    } else {
+                        info!("Skipping empty batch");
+                    }
+                }
+                Err(e) => {
+                    error!("Error reading batch during data loading: {}", e);
+                    return Err(e);
+                }
+            }
+        }
+        info!("Stream completed after {} batches", batch_count);
+
+        info!("Number of batches loaded: {}", data.len());
+        if !data.is_empty() {
+            info!(
+                "Successfully loaded {} total rows from {} to {}",
+                current_index - start_index,
+                start_index,
+                current_index - 1
+            );
+
+            // Use the actual schema from the loaded batches instead of the expected schema
+            let actual_schema = data[0].schema();
+            info!("Using actual batch schema: {:?}", actual_schema);
+            IndexableMemTable::try_new(actual_schema, vec![data], indices)
+        } else {
+            error!(
+                "No batches loaded from data source - fallback logic should have been triggered but produced no data"
+            );
+            IndexableMemTable::try_new(Arc::clone(&schema), vec![data], indices)
+        }
+    }
+}
+
+async fn fetch_partitions(
+    batches: Vec<RecordBatch>,
+    indices: &[u64],
+    start: u64,
+    end: u64,
+) -> Vec<RecordBatch> {
+    let mut left = 0;
+    let mut right = indices.len();
+
+    while left < right {
+        let mid = left + (right - left) / 2;
+        if indices[mid] < start {
+            left = mid + 1;
+        } else {
+            right = mid;
+        }
+    }
+    let start_index = left;
+
+    right = indices.len();
+
+    while left < right {
+        let mid = left + (right - left) / 2;
+        if indices[mid] <= end {
+            left = mid + 1;
+        } else {
+            right = mid;
+        }
+    }
+    let end_index = right;
+
+    if batches.is_empty() {
+        vec![]
+    } else if start_index < end_index {
+        batches[start_index..=end_index - 1].to_owned()
+    } else if start_index == end_index && end_index > 0 {
+        vec![batches[end_index - 1].to_owned()]
+    } else {
+        vec![]
+    }
+}
+
+#[async_trait]
+impl TableProvider for IndexableMemTable {
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn schema(&self) -> SchemaRef {
+        Arc::clone(&self.schema)
+    }
+
+    fn constraints(&self) -> Option<&Constraints> {
+        None
+    }
+
+    fn table_type(&self) -> TableType {
+        TableType::Base
+    }
+
+    async fn scan(
+        &self,
+        _state: &dyn Session,
+        projection: Option<&Vec<usize>>,
+        _filters: &[Expr],
+        _limit: Option<usize>,
+    ) -> datafusion::common::Result<Arc<dyn ExecutionPlan>> {
+        let (start, end) = if _filters.len() == 1 {
+            info!("{:?}", _filters[0]);
+            let start = collect_literals(&_filters[0]).ok_or_else(|| {
+                DataFusionError::Execution(
+                    "Failed to extract start value from first filter".to_string(),
+                )
+            })?;
+            (start, start)
+        } else if _filters.len() == 2 {
+            info!("{:?}", _filters[0]);
+            info!("{:?}", _filters[1]);
+            let start = collect_literals(&_filters[0]).ok_or_else(|| {
+                DataFusionError::Execution(
+                    "Failed to extract start value from first filter".to_string(),
+                )
+            })?;
+            let end = collect_literals(&_filters[1]).ok_or_else(|| {
+                DataFusionError::Execution(
+                    "Failed to extract end value from second filter".to_string(),
+                )
+            })?;
+            (start, end)
+        } else {
+            return exec_err!("Incorrect filters");
+        };
+        let partitions = fetch_partitions(self.batches.clone(), &self.indices, start, end).await;
+
+        let exec =
+            MemorySourceConfig::try_new_exec(&[partitions], self.schema(), projection.cloned())?;
+
+        Ok(exec)
+    }
+
+    fn supports_filters_pushdown(
+        &self,
+        filters: &[&Expr],
+    ) -> datafusion::common::Result<Vec<TableProviderFilterPushDown>> {
+        Ok(vec![TableProviderFilterPushDown::Inexact; filters.len()])
+    }
+}
+
+fn collect_literals(expr: &Expr) -> Option<u64> {
+    match expr {
+        Expr::BinaryExpr(BinaryExpr {
+            left: _,
+            op: _,
+            right,
+        }) => {
+            if let Expr::Literal(scalar) = &**right {
+                if let ScalarValue::UInt64(Some(val)) = scalar {
+                    return Some(*val);
+                }
+            }
+            None
+        }
+        _ => None,
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use arrow::array::UInt64Array;
+    use arrow::{
+        array::StringArray,
+        datatypes::{DataType, Field, Schema},
+        record_batch::RecordBatch,
+    };
+    use datafusion::assert_batches_eq;
+    use datafusion::datasource::MemTable;
+    use datafusion::prelude::{SessionConfig, SessionContext};
+    use std::sync::Arc;
+
+    fn create_schema() -> Arc<Schema> {
+        Arc::new(Schema::new(vec![
+            Field::new("id", DataType::UInt64, false),
+            Field::new("name", DataType::Utf8, false),
+        ]))
+    }
+    // Create sample RecordBatches for testing
+    fn create_test_batches() -> Result<Vec<RecordBatch>, Box<dyn std::error::Error>> {
+        let schema = create_schema();
+        Ok(vec![
+            RecordBatch::try_new(
+                schema.clone(),
+                vec![
+                    Arc::new(UInt64Array::from(vec![0, 1, 2, 3])),
+                    Arc::new(StringArray::from(vec!["A", "B", "C", "D"])),
+                ],
+            )?,
+            RecordBatch::try_new(
+                schema.clone(),
+                vec![
+                    Arc::new(UInt64Array::from(vec![4, 5, 6, 7])),
+                    Arc::new(StringArray::from(vec!["E", "F", "G", "H"])),
+                ],
+            )?,
+        ])
+    }
+
+    // #[test]
+    // fn test_basic_and_condition_with_filter() {
+    //     let expr = col("a").eq(lit(1u64)).and(col("b").eq(lit(2u64)));
+    //     let df_schema = DFSchema::try_from(create_schema()).unwrap();
+    //     let physical_expr = SessionContext::new().create_physical_expr(expr, &df_schema).unwrap();
+    //     let a = collect_literals(&physical_expr).unwrap();
+    //     let b = collect_literals(&physical_expr[1]).unwrap();
+    //     assert_eq!(a, 1);
+    //     assert_eq!(b, 2);
+    // }
+
+    #[tokio::test]
+    async fn test_partition_selection() -> Result<(), Box<dyn std::error::Error>> {
+        let batches = create_test_batches()?;
+        let result = fetch_partitions(batches, &[0, 4], 0, 2).await;
+
+        assert_batches_eq!(
+            [
+                "+----+------+",
+                "| id | name |",
+                "+----+------+",
+                "| 0  | A    |",
+                "| 1  | B    |",
+                "| 2  | C    |",
+                "| 3  | D    |",
+                "+----+------+"
+            ],
+            &result
+        );
+        Ok(())
+    }
+
+    #[tokio::test]
+    async fn test_partition_selection_single_row() -> Result<(), Box<dyn std::error::Error>> {
+        let batches = create_test_batches()?;
+        let result = fetch_partitions(batches, &[0, 4], 2, 2).await;
+
+        assert_batches_eq!(
+            [
+                "+----+------+",
+                "| id | name |",
+                "+----+------+",
+                "| 0  | A    |",
+                "| 1  | B    |",
+                "| 2  | C    |",
+                "| 3  | D    |",
+                "+----+------+"
+            ],
+            &result
+        );
+        Ok(())
+    }
+
+    #[tokio::test]
+    async fn test_partition_selection_all() -> Result<(), Box<dyn std::error::Error>> {
+        let batches = create_test_batches()?;
+        let result = fetch_partitions(batches, &[0, 4], 0, 4).await;
+        // println!("{}", &result.get(0).unwrap().num_rows());
+        // println!("{}", &result.get(1).unwrap().num_rows());
+        assert_batches_eq!(
+            [
+                "+----+------+",
+                "| id | name |",
+                "+----+------+",
+                "| 0  | A    |",
+                "| 1  | B    |",
+                "| 2  | C    |",
+                "| 3  | D    |",
+                "| 4  | E    |",
+                "| 5  | F    |",
+                "| 6  | G    |",
+                "| 7  | H    |",
+                "+----+------+"
+            ],
+            &result
+        );
+        Ok(())
+    }
+
+    #[tokio::test]
+    async fn test_indexable_mem_table_with_scan() -> Result<(), Box<dyn std::error::Error>> {
+        let schema = create_schema();
+        let rb = create_test_batches()?;
+        let mem_table = IndexableMemTable::try_new(schema, vec![rb], [0, 4].to_vec())?;
+
+        let ctx = SessionContext::new();
+        ctx.register_table("test_table", Arc::new(mem_table))?;
+        let df = ctx
+            .sql("SELECT id, name FROM test_table where id >= 0 AND id <= 2")
+            .await?;
+        let result = df.collect().await?;
+        assert_batches_eq!(
+            [
+                "+----+------+",
+                "| id | name |",
+                "+----+------+",
+                "| 0  | A    |",
+                "| 1  | B    |",
+                "| 2  | C    |",
+                "+----+------+"
+            ],
+            &result
+        );
+        Ok(())
+    }
+
+    #[tokio::test]
+    async fn test_indexable_mem_table_with_load_and_scan() -> Result<(), Box<dyn std::error::Error>>
+    {
+        let schema = create_schema();
+        let rb = create_test_batches()?;
+        let config = SessionConfig::new().with_batch_size(1024);
+        let ctx = SessionContext::new_with_config(config);
+        let mem_table = MemTable::try_new(schema, vec![rb])?;
+        let indexable_mem_table =
+            IndexableMemTable::load(Arc::new(mem_table), None, &ctx.state(), 0).await?;
+
+        ctx.register_table("test_table", Arc::new(indexable_mem_table))?;
+        let df = ctx
+            .sql("SELECT id, name FROM test_table where id >= 0 AND id <= 2")
+            .await?;
+        let result = df.collect().await?;
+        assert_batches_eq!(
+            [
+                "+----+------+",
+                "| id | name |",
+                "+----+------+",
+                "| 0  | A    |",
+                "| 1  | B    |",
+                "| 2  | C    |",
+                "+----+------+"
+            ],
+            &result
+        );
+        Ok(())
+    }
+}
diff --git a/pkg/data_cache/src/worker/mod.rs b/pkg/data_cache/src/worker/mod.rs
new file mode 100644
index 0000000000..c910dc143b
--- /dev/null
+++ b/pkg/data_cache/src/worker/mod.rs
@@ -0,0 +1,6 @@
+#[path = "../config/mod.rs"]
+pub mod config;
+mod indexable_mem_table;
+pub mod worker;
+pub mod worker_datasource;
+pub mod worker_service;
diff --git a/pkg/data_cache/src/worker/worker.rs b/pkg/data_cache/src/worker/worker.rs
new file mode 100644
index 0000000000..72cfb8275a
--- /dev/null
+++ b/pkg/data_cache/src/worker/worker.rs
@@ -0,0 +1,50 @@
+use crate::worker::indexable_mem_table::IndexableMemTable;
+use crate::worker::worker_datasource::WorkerDataSource;
+use datafusion::common::DataFusionError;
+use datafusion::prelude::SessionContext;
+use std::sync::Arc;
+use tracing::{error, info};
+
+pub struct DataLoader {
+    data_source: Arc<WorkerDataSource>,
+}
+
+impl DataLoader {
+    pub(crate) async fn new(
+        metadata_loc: String,
+        table_name: String,
+        schema_name: String,
+        file_urls: Vec<String>,
+        start_index: u64,
+    ) -> Result<Self, Box<dyn std::error::Error>> {
+        let data_source = WorkerDataSource::new(
+            metadata_loc,
+            table_name,
+            schema_name,
+            file_urls,
+            start_index,
+        )
+        .await?;
+        Ok(Self {
+            data_source: Arc::new(data_source),
+        })
+    }
+    pub(crate) async fn load_data(
+        &self,
+        ctx: &SessionContext,
+        table: &str,
+        start_index: u64,
+    ) -> datafusion::common::Result<()> {
+        let memtable =
+            IndexableMemTable::load(self.data_source.clone(), None, &ctx.state(), start_index)
+                .await
+                .map_err(|error: DataFusionError| {
+                    error!("Error loading table: {:?}", error);
+                    error
+                })?;
+        ctx.register_table(table, Arc::new(memtable))
+            .map_err(|e| DataFusionError::Execution(format!("Failed to register table: {}", e)))?;
+        info!("loaded and registered table");
+        Ok(())
+    }
+}
diff --git a/pkg/data_cache/src/worker/worker_datasource.rs b/pkg/data_cache/src/worker/worker_datasource.rs
new file mode 100644
index 0000000000..ea6f44cb1f
--- /dev/null
+++ b/pkg/data_cache/src/worker/worker_datasource.rs
@@ -0,0 +1,798 @@
+use arrow::array::UInt64Array;
+use arrow::record_batch::RecordBatch;
+use arrow_schema::{DataType, Field, Schema, SchemaBuilder, SchemaRef};
+use async_trait::async_trait;
+use datafusion::catalog::{Session, TableProvider};
+use datafusion::datasource::TableType;
+use datafusion::error::Result;
+use datafusion::execution::{SendableRecordBatchStream, TaskContext};
+use datafusion::logical_expr::Expr;
+use datafusion::physical_expr::EquivalenceProperties;
+use datafusion::physical_plan::execution_plan::{Boundedness, EmissionType};
+use datafusion::physical_plan::stream::RecordBatchStreamAdapter;
+use datafusion::physical_plan::{
+    DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, RecordBatchStream,
+};
+use futures::{Stream, StreamExt, TryStreamExt, future};
+use iceberg::TableIdent;
+use iceberg::arrow::schema_to_arrow_schema;
+use iceberg::io::FileIO;
+use iceberg::scan::{FileScanTask, FileScanTaskStream};
+use iceberg::table::{StaticTable, Table};
+use iceberg_datafusion::{from_datafusion_error, to_datafusion_error};
+use object_store::aws::AmazonS3Builder;
+use std::any::Any;
+use std::fmt::{Debug, Formatter};
+use std::pin::Pin;
+use std::sync::Arc;
+use std::task::{Context, Poll};
+use tracing::{error, info};
+use url::Url;
+
+/// Worker node data source that manages **data schemas** for distributed Arrow caching.
+///
+/// **IMPORTANT**: This component handles the **data schema** (actual data structure)
+/// which is completely separate from the **metadata schema** used by the head node
+/// for coordination. See [`metadata_arrow_schema()`] for head node coordination schema.
+///
+/// # Dual Schema Architecture - Worker Data Processing
+///
+/// This worker data source manages **two distinct Arrow schemas** for data processing:
+///
+/// 1. **`table_schema`** - Original data schema from Iceberg:
+///    - Source: Iceberg table metadata converted to Arrow format
+///    - Conversion: `iceberg::arrow::schema_to_arrow_schema(table.metadata().current_schema())`
+///    - Purpose: Represents the raw data structure for reading files
+///    - Example: `[id: Int64, user_id: String, event_type: String, timestamp: Timestamp]`
+///
+/// 2. **`output_schema`** - Enhanced data schema for caching:
+///    - Source: `table_schema` + additional `cache_index` column
+///    - Purpose: Provides global row ordering across distributed workers
+///    - Example: `[id: Int64, user_id: String, event_type: String, timestamp: Timestamp, cache_index: UInt64]`
+///
+/// # Schema vs Metadata Schema Separation
+///
+/// **Worker Data Schemas** (this component):
+/// - Describe actual data structure (columns, types, semantics)
+/// - Converted from Iceberg metadata to Arrow format
+/// - Enhanced with caching columns for efficient lookups
+/// - Used for query execution and data processing
+///
+/// **Head Node Metadata Schema** (coordination only):
+/// - Describes worker coordination (worker_ids, row ranges, file paths)
+/// - Created by [`metadata_arrow_schema()`] function
+/// - No relationship to actual data structure
+/// - Used for distributed query planning and coordination
+///
+/// # Architecture
+///
+/// The worker data source operates as part of a head-worker architecture:
+/// - Head node assigns specific file URLs using metadata schema
+/// - Worker loads assigned data files using data schemas (this component)
+/// - Adds a `cache_index` column for global row ordering
+/// - Supports streaming data processing with bounded memory usage
+///
+/// # Data Schema Conversion Flow
+///
+/// ```text
+/// Iceberg Table Metadata
+///    │ (contains original data schema)
+///    ▼
+/// iceberg::arrow::schema_to_arrow_schema()
+///    │
+///    ▼
+/// table_schema: SchemaRef
+///    │ (e.g., [id, user_id, event_type, timestamp])
+///    ▼
+/// + cache_index column
+///    │
+///    ▼
+/// output_schema: SchemaRef
+///    │ (e.g., [id, user_id, event_type, timestamp, cache_index])
+///    ▼
+/// Used for query execution
+/// ```
+///
+/// # Data Flow with Schema Usage
+///
+/// ```text
+/// ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
+/// │   Head Node     │───▶│ WorkerDataSource│───▶│   IndexColumn   │
+/// │(metadata schema)│    │ (data schemas)  │    │      Exec       │
+/// └─────────────────┘    └─────────────────┘    └─────────────────┘
+///                                 │                       │
+///                                 ▼                       ▼
+///                        ┌─────────────────┐    ┌─────────────────┐
+///                        │   WorkerExec    │    │  Row Numbering  │
+///                        │(table_schema)   │    │(output_schema)  │
+///                        └─────────────────┘    └─────────────────┘
+/// ```
+///
+/// # Example Data Schema Evolution
+///
+/// **Original Iceberg Schema**:
+/// ```text
+/// ┌────────┬─────────┬────────────┬─────────────┐
+/// │   id   │ user_id │ event_type │ timestamp   │
+/// │ Int64  │ String  │   String   │ Timestamp   │
+/// └────────┴─────────┴────────────┴─────────────┘
+/// ```
+///
+/// **table_schema** (converted to Arrow):
+/// ```text
+/// ┌────────┬─────────┬────────────┬─────────────┐
+/// │   id   │ user_id │ event_type │ timestamp   │
+/// │ Int64  │ String  │   String   │ Timestamp   │
+/// └────────┴─────────┴────────────┴─────────────┘
+/// ```
+///
+/// **output_schema** (enhanced for caching):
+/// ```text
+/// ┌────────┬─────────┬────────────┬─────────────┬─────────────┐
+/// │   id   │ user_id │ event_type │ timestamp   │ cache_index │
+/// │ Int64  │ String  │   String   │ Timestamp   │   UInt64    │
+/// └────────┴─────────┴────────────┴─────────────┴─────────────┘
+/// ```
+///
+/// # Performance Considerations
+///
+/// - Only loads files assigned to this worker (reduces I/O)
+/// - Streams data to minimize memory footprint
+/// - `cache_index` enables efficient range-based queries
+/// - Uses Iceberg's native file filtering capabilities
+/// - Schema conversion happens once during initialization
+///
+/// # See Also
+///
+/// ## Data Schema Components:
+/// - [`WorkerExec`]: Execution plan for loading data using table_schema
+/// - [`IndexColumnExec`]: Execution plan for adding cache_index using output_schema
+/// - [`RowNumberStream`]: Stream processor for row numbering with output_schema
+/// - [`iceberg::arrow::schema_to_arrow_schema`]: Converts Iceberg schema to Arrow
+///
+/// ## Metadata Schema (Head Node Coordination):
+/// - [`metadata_arrow_schema()`]: Creates coordination schema (separate from data)
+/// - [`HeadService`]: Uses metadata schema for worker coordination
+pub struct WorkerDataSource {
+    file_urls: Vec<String>,
+    start_index: u64,
+    inner: Table,
+    /// **Enhanced data schema** for query execution and caching operations.
+    ///
+    /// This schema includes:
+    /// - All original data columns from the Iceberg table
+    /// - Additional `cache_index` column (UInt64) for efficient indexing
+    ///
+    /// Used by query execution plans and result generation.
+    /// Example: `[id: Int64, user_id: String, event_type: String, timestamp: Timestamp, cache_index: UInt64]`
+    output_schema: SchemaRef,
+    /// **Original data schema** converted from Iceberg table metadata to Arrow format.
+    ///
+    /// This represents the raw data structure as defined in the Iceberg table,
+    /// without any caching enhancements. Used for reading and processing data files.
+    ///
+    /// Converted using: `iceberg::arrow::schema_to_arrow_schema(table.metadata().current_schema())`
+    /// Example: `[id: Int64, user_id: String, event_type: String, timestamp: Timestamp]`
+    table_schema: SchemaRef,
+}
+
+impl WorkerDataSource {
+    pub(crate) async fn new(
+        metadata_loc: String,
+        table_name: String,
+        schema_name: String,
+        file_urls: Vec<String>,
+        start_index: u64,
+    ) -> Result<Self, Box<dyn std::error::Error>> {
+        let file_io = FileIO::from_path(&metadata_loc)
+            .map_err(|e| format!("Failed to create FileIO: {}", e))?
+            .build()
+            .map_err(|e| format!("Failed to build FileIO: {}", e))?;
+        let table_indent = TableIdent::from_strs([schema_name, table_name])
+            .map_err(|e| format!("Failed to create table ident: {}", e))?;
+        let static_table =
+            StaticTable::from_metadata_file(&metadata_loc, table_indent, file_io.clone())
+                .await
+                .map_err(|e| format!("Failed to load static table: {}", e))?;
+        let table = static_table.into_table();
+
+        // STEP 1: Convert Iceberg data schema to Arrow format
+        // This creates the table_schema containing the original data columns
+        let schema = Arc::new(
+            schema_to_arrow_schema(table.metadata().current_schema())
+                .map_err(|e| format!("Failed to convert schema: {}", e))?,
+        );
+
+        // STEP 2: Create enhanced schema by adding cache_index column
+        // This creates the output_schema used for query execution and caching
+        let fields = schema.fields().clone();
+        let mut builder = SchemaBuilder::from(&fields);
+        builder.push(Field::new("cache_index", DataType::UInt64, false)); // TODO:// validate name collision
+        let output_schema = Arc::new(Schema::new(builder.finish().fields));
+        Ok(Self {
+            file_urls,
+            start_index,
+            inner: table,
+            output_schema,
+            table_schema: schema,
+        })
+    }
+}
+
+impl Debug for WorkerDataSource {
+    fn fmt(&self, _f: &mut Formatter<'_>) -> std::fmt::Result {
+        todo!()
+    }
+}
+
+#[async_trait]
+impl TableProvider for WorkerDataSource {
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn schema(&self) -> SchemaRef {
+        self.output_schema.clone()
+    }
+
+    fn table_type(&self) -> TableType {
+        TableType::Base
+    }
+
+    async fn scan(
+        &self,
+        _state: &dyn Session,
+        _projection: Option<&Vec<usize>>,
+        _filters: &[Expr],
+        _limit: Option<usize>,
+    ) -> datafusion::error::Result<Arc<dyn ExecutionPlan>> {
+        info!("creating exec to fetch data from iceberg table");
+        let iceberg_exec = WorkerExec::new(
+            self.file_urls.clone(),
+            self.inner.clone(),
+            self.table_schema.clone(),
+        );
+        Ok(Arc::new(IndexColumnExec::new(
+            Arc::new(iceberg_exec),
+            self.output_schema.clone(),
+            self.start_index,
+        )))
+
+        //TODO: Support scanning with subset of datafiles
+        //TODO: Support projection with selected columns - Exec init computes projected schema and add its to props. Converts to column names and passes it to exec
+    }
+}
+
+/// Execution plan for loading assigned data files on worker nodes.
+///
+/// This execution plan reads specific data files assigned by the head node from
+/// an Iceberg table. It filters the table's file scan tasks to only process files
+/// that have been assigned to this worker node.
+///
+/// # Algorithm
+///
+/// 1. Build an Iceberg table reader from metadata
+/// 2. Plan all available files from the table
+/// 3. Filter file scan tasks to only include assigned file URLs
+/// 4. Stream data from the filtered files
+///
+/// # File Filtering
+///
+/// The execution plan only processes files that match the assigned file URLs:
+/// - Reduces I/O by avoiding unnecessary file reads
+/// - Maintains data locality and distribution as planned by head node
+/// - Uses Iceberg's built-in file filtering capabilities
+///
+/// # Performance Considerations
+///
+/// - Single partition output (worker processes assigned files sequentially)
+/// - Streaming execution to minimize memory usage
+/// - Respects data file concurrency limits for controlled resource usage
+/// - Filters at the file level before reading data
+///
+/// # See Also
+///
+/// - [`WorkerDataSource`]: The table provider that creates this execution plan
+/// - [`IndexColumnExec`]: Wraps this plan to add cache indexing
+/// - [`read_stream`]: Async function that performs the actual file reading
+pub struct WorkerExec {
+    file_urls: Vec<String>,
+    inner: Table,
+    schema: SchemaRef,
+    plan_properties: PlanProperties,
+}
+
+impl WorkerExec {
+    fn new(file_urls: Vec<String>, inner: Table, schema: SchemaRef) -> Self {
+        let eq_properties = EquivalenceProperties::new_with_orderings(schema.clone(), &[]);
+        let plan_properties = PlanProperties::new(
+            eq_properties, // Equivalence Properties
+            datafusion::physical_expr::Partitioning::UnknownPartitioning(1), // Output Partitioning
+            EmissionType::Both,
+            Boundedness::Bounded, // Execution Mode
+        );
+        Self {
+            file_urls,
+            inner,
+            schema,
+            plan_properties,
+        }
+    }
+}
+
+impl Debug for WorkerExec {
+    fn fmt(&self, _f: &mut Formatter<'_>) -> std::fmt::Result {
+        todo!()
+    }
+}
+
+impl DisplayAs for WorkerExec {
+    fn fmt_as(&self, t: DisplayFormatType, f: &mut Formatter) -> std::fmt::Result {
+        match t {
+            DisplayFormatType::Default | DisplayFormatType::Verbose => {
+                write!(f, "WorkerExec: files={}", self.file_urls.len())
+            }
+            DisplayFormatType::TreeRender => {
+                write!(f, "files={}", self.file_urls.len())
+            }
+        }
+    }
+}
+
+impl ExecutionPlan for WorkerExec {
+    fn name(&self) -> &str {
+        "WorkerExec"
+    }
+
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn properties(&self) -> &PlanProperties {
+        &self.plan_properties
+    }
+
+    fn children(&self) -> Vec<&Arc<(dyn ExecutionPlan + 'static)>> {
+        vec![]
+    }
+
+    fn with_new_children(
+        self: Arc<Self>,
+        _children: Vec<Arc<dyn ExecutionPlan>>,
+    ) -> Result<Arc<dyn ExecutionPlan>> {
+        Ok(self)
+    }
+
+    fn execute(
+        &self,
+        _partition: usize,
+        _context: Arc<TaskContext>,
+    ) -> Result<SendableRecordBatchStream> {
+        info!(
+            "WorkerExec::execute called with file_urls: {:?}",
+            self.file_urls
+        );
+        let stream = futures::stream::once(read_stream(self.inner.clone(), self.file_urls.clone()))
+            .try_flatten();
+        info!("WorkerExec::execute created stream, returning RecordBatchStreamAdapter");
+        Ok(Box::pin(RecordBatchStreamAdapter::new(
+            self.schema.clone(),
+            stream,
+        )))
+    }
+}
+
+/// Execution plan that adds a cache index column to incoming data streams.
+///
+/// This execution plan wraps another execution plan and adds a monotonically
+/// increasing `cache_index` column to each RecordBatch. The index provides
+/// global row ordering across all workers in the distributed system.
+///
+/// # Algorithm
+///
+/// 1. Execute the wrapped input execution plan
+/// 2. For each incoming RecordBatch:
+///    - Add a `cache_index` column with sequential row numbers
+///    - Start numbering from the provided `start_index`
+///    - Increment the counter for subsequent batches
+///
+/// # Index Calculation
+///
+/// The cache index is calculated as:
+/// - First row in first batch: `start_index`
+/// - Last row in first batch: `start_index + batch_size - 1`
+/// - First row in second batch: `start_index + batch_size`
+/// - And so on...
+///
+/// # Schema Modification
+///
+/// The output schema includes all original columns plus:
+/// - `cache_index`: UInt64 column with globally unique row identifiers
+/// - Column is appended to the end of the schema
+/// - Non-nullable (every row gets an index)
+///
+/// # Performance Considerations
+///
+/// - Minimal overhead: only adds one column per batch
+/// - Streaming execution: processes batches as they arrive
+/// - Memory efficient: doesn't buffer entire dataset
+/// - Preserves original data ordering and partitioning
+///
+/// # See Also
+///
+/// - [`WorkerDataSource`]: Creates this execution plan
+/// - [`RowNumberStream`]: The stream that performs the index addition
+/// - [`WorkerExec`]: The typical input execution plan
+pub struct IndexColumnExec {
+    input: Arc<dyn ExecutionPlan>,
+    schema: SchemaRef,
+    plan_properties: PlanProperties,
+    start_index: u64,
+}
+
+impl IndexColumnExec {
+    fn new(input: Arc<dyn ExecutionPlan>, schema: SchemaRef, start_index: u64) -> Self {
+        let eq_properties = EquivalenceProperties::new_with_orderings(schema.clone(), &[]);
+        let plan_properties = PlanProperties::new(
+            eq_properties, // Equivalence Properties
+            datafusion::physical_expr::Partitioning::UnknownPartitioning(1), // Output Partitioning
+            EmissionType::Both,
+            Boundedness::Bounded, // Execution Mode
+        );
+        Self {
+            input,
+            schema,
+            plan_properties,
+            start_index,
+        }
+    }
+}
+
+impl Debug for IndexColumnExec {
+    fn fmt(&self, _f: &mut Formatter<'_>) -> std::fmt::Result {
+        todo!()
+    }
+}
+
+impl DisplayAs for IndexColumnExec {
+    fn fmt_as(&self, t: DisplayFormatType, f: &mut Formatter) -> std::fmt::Result {
+        match t {
+            DisplayFormatType::Default | DisplayFormatType::Verbose => {
+                write!(f, "IndexColumnExec: start_index={}", self.start_index)
+            }
+            DisplayFormatType::TreeRender => {
+                write!(f, "start_index={}", self.start_index)
+            }
+        }
+    }
+}
+
+impl ExecutionPlan for IndexColumnExec {
+    fn name(&self) -> &str {
+        "IndexColumnExec"
+    }
+
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn properties(&self) -> &PlanProperties {
+        &self.plan_properties
+    }
+
+    fn children(&self) -> Vec<&Arc<(dyn ExecutionPlan + 'static)>> {
+        vec![]
+    }
+
+    fn with_new_children(
+        self: Arc<Self>,
+        _children: Vec<Arc<dyn ExecutionPlan>>,
+    ) -> Result<Arc<dyn ExecutionPlan>> {
+        Ok(self)
+    }
+
+    fn execute(
+        &self,
+        _partition: usize,
+        _context: Arc<TaskContext>,
+    ) -> Result<SendableRecordBatchStream> {
+        let stream = self.input.execute(_partition, _context)?;
+        Ok(Box::pin(RowNumberStream::new(
+            stream,
+            self.schema.clone(),
+            self.start_index,
+        )))
+    }
+}
+
+/// Stream processor that adds cache index column to RecordBatches.
+///
+/// This stream wraps another RecordBatchStream and adds a monotonically
+/// increasing `cache_index` column to each batch. The index provides global
+/// row ordering across the distributed system.
+///
+/// # Row Numbering
+///
+/// Each row receives a unique index based on:
+/// - Starting index provided during construction
+/// - Sequential numbering across all batches
+/// - Continuous numbering (no gaps between batches)
+///
+/// # Performance Characteristics
+///
+/// - Streaming: processes one batch at a time
+/// - Low memory overhead: only stores current row counter
+/// - Preserves batch boundaries and ordering
+/// - Efficient array construction using range operations
+///
+/// # See Also
+///
+/// - [`IndexColumnExec`]: The execution plan that creates this stream
+/// - [`RecordBatchStream`]: The trait this stream implements
+pub struct RowNumberStream {
+    inner: SendableRecordBatchStream,
+    row_count: u64,
+    schema: SchemaRef,
+}
+
+impl RowNumberStream {
+    pub fn new(inner: SendableRecordBatchStream, schema: SchemaRef, start_index: u64) -> Self {
+        RowNumberStream {
+            inner,
+            row_count: start_index,
+            schema,
+        }
+    }
+}
+
+impl RecordBatchStream for RowNumberStream {
+    fn schema(&self) -> SchemaRef {
+        Arc::clone(&self.schema)
+    }
+}
+
+impl Stream for RowNumberStream {
+    type Item = Result<RecordBatch>;
+
+    fn poll_next(mut self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll<Option<Self::Item>> {
+        match self.inner.poll_next_unpin(cx) {
+            Poll::Ready(Some(Ok(batch))) => {
+                let num_rows = batch.num_rows();
+
+                // Debug the schemas for diagnosis
+                let input_schema = batch.schema();
+                let expected_schema = self.schema.clone();
+                info!(
+                    "RowNumberStream: Input batch schema: {:?} with {} columns",
+                    input_schema,
+                    input_schema.fields().len()
+                );
+                info!(
+                    "RowNumberStream: Expected output schema: {:?} with {} columns",
+                    expected_schema,
+                    expected_schema.fields().len()
+                );
+
+                // If the schemas don't match (except for the cache_index we'll add), create a new schema
+                // that combines the input columns with the cache_index column
+                let actual_schema =
+                    if input_schema.fields().len() + 1 != expected_schema.fields().len() {
+                        let mut builder = arrow_schema::SchemaBuilder::from(input_schema.fields());
+                        builder.push(Field::new("cache_index", DataType::UInt64, false));
+                        let new_schema = Arc::new(Schema::new(builder.finish().fields));
+                        info!(
+                            "RowNumberStream: Created new compatible schema: {:?}",
+                            new_schema
+                        );
+                        new_schema
+                    } else {
+                        expected_schema
+                    };
+
+                let mut new_columns = batch.columns().to_vec();
+
+                let row_numbers: UInt64Array = (self.row_count..self.row_count + num_rows as u64)
+                    .collect::<Vec<u64>>()
+                    .into();
+
+                new_columns.push(Arc::new(row_numbers));
+
+                // Use the compatible schema to create the new batch
+                let new_batch = RecordBatch::try_new(actual_schema, new_columns)?;
+                self.row_count += num_rows as u64;
+
+                Poll::Ready(Some(Ok(new_batch)))
+            }
+            other => other,
+        }
+    }
+}
+
+async fn read_stream(
+    table: Table,
+    file_urls: Vec<String>,
+) -> Result<Pin<Box<dyn Stream<Item = Result<RecordBatch>> + Send>>> {
+    info!("read_stream: Starting with file_urls: {:?}", file_urls);
+    let reader = table.reader_builder().build();
+
+    // Plan all files from Iceberg
+    info!("read_stream: Building Iceberg scan...");
+    let mut planned = table
+        .scan()
+        .with_data_file_concurrency_limit(1)
+        .build()
+        .map_err(to_datafusion_error)?
+        .plan_files()
+        .await
+        .map_err(to_datafusion_error)?;
+
+    // Collect and filter matching tasks against assigned file_urls
+    let file_urls_arc = Arc::new(file_urls.clone());
+    let mut total_planned = 0usize;
+    let mut matched: Vec<iceberg::scan::FileScanTask> = Vec::new();
+    info!("read_stream: Processing planned files...");
+    while let Some(next) = planned.next().await {
+        match next {
+            Ok(task) => {
+                total_planned += 1;
+                info!("read_stream: Found file task: {}", task.data_file_path);
+                if file_urls_arc.contains(&task.data_file_path) {
+                    info!("read_stream: File matches assigned files, adding to matched list");
+                    matched.push(task);
+                } else {
+                    info!("read_stream: File does not match assigned files");
+                }
+            }
+            Err(e) => {
+                error!("read_stream: Error in planning files: {}", e);
+                return Err(to_datafusion_error(e));
+            }
+        }
+    }
+
+    info!(
+        "read_stream: Iceberg scan completed - total_planned_files={}, matched_files={}",
+        total_planned,
+        matched.len()
+    );
+
+    // If Iceberg has no files or none matched, fallback to direct parquet reading
+    if total_planned == 0 || matched.is_empty() {
+        info!(
+            "read_stream: Falling back to direct Parquet reading (total_planned={}, matched={})",
+            total_planned,
+            matched.len()
+        );
+        return read_parquet_files_directly((*file_urls_arc).clone()).await;
+    }
+
+    // Build a stream from matched tasks and let Iceberg reader read them
+    info!(
+        "read_stream: Building Iceberg reader stream with {} matched files",
+        matched.len()
+    );
+    let matched_stream = futures::stream::iter(matched.into_iter().map(Ok));
+    let stream = reader
+        .read(Box::pin(matched_stream))
+        .await
+        .map_err(to_datafusion_error)?
+        .map_err(to_datafusion_error);
+    info!("read_stream: Iceberg reader stream created successfully");
+    Ok(Box::pin(stream))
+}
+
+#[allow(dead_code)]
+async fn filter_and_create_stream(
+    result: Result<FileScanTaskStream>,
+    file_urls: Arc<Vec<String>>,
+) -> Result<Pin<Box<dyn Stream<Item = std::result::Result<FileScanTask, iceberg::Error>> + Send>>> {
+    match result {
+        Ok(stream) => {
+            info!("File URLs to match: {:?}", file_urls);
+            Ok(Box::pin(
+                stream
+                    .try_filter(move |task| {
+                        info!(
+                            "Checking file task path: '{}' against URLs: {:?}",
+                            task.data_file_path, file_urls
+                        );
+                        let matches = file_urls.clone().contains(&task.data_file_path);
+                        info!("File '{}' matches: {}", task.data_file_path, matches);
+                        future::ready(matches)
+                    })
+                    .map(|result| result),
+            ))
+        }
+        Err(e) => Ok(Box::pin(futures::stream::once(future::ready(Err(
+            from_datafusion_error(e),
+        ))))),
+    }
+}
+
+async fn read_parquet_files_directly(
+    file_urls: Vec<String>,
+) -> Result<Pin<Box<dyn Stream<Item = Result<RecordBatch>> + Send>>> {
+    info!("Reading Parquet files directly: {:?}", file_urls);
+
+    use datafusion::prelude::SessionContext;
+    use std::env;
+
+    // Create a DataFusion session context
+    let ctx = SessionContext::new();
+
+    // Configure S3 object store if needed
+    if !file_urls.is_empty() && file_urls[0].starts_with("s3://") {
+        let aws_access_key = env::var("AWS_ACCESS_KEY_ID").unwrap_or_default();
+        let aws_secret_key = env::var("AWS_SECRET_ACCESS_KEY").unwrap_or_default();
+        let aws_region = env::var("AWS_REGION").unwrap_or_else(|_| "us-east-1".to_string());
+
+        if !aws_access_key.is_empty() && !aws_secret_key.is_empty() {
+            // Extract unique bucket names from all S3 URLs
+            let mut buckets = std::collections::HashSet::new();
+            for file_url in &file_urls {
+                if let Some(bucket) = file_url
+                    .strip_prefix("s3://")
+                    .and_then(|path| path.split('/').next())
+                {
+                    buckets.insert(bucket);
+                }
+            }
+
+            // Register an object store for each unique bucket
+            for bucket in buckets {
+                let s3_store = AmazonS3Builder::new()
+                    .with_access_key_id(&aws_access_key)
+                    .with_secret_access_key(&aws_secret_key)
+                    .with_region(&aws_region)
+                    .with_bucket_name(bucket)
+                    .build()
+                    .map_err(|e| datafusion::error::DataFusionError::External(Box::new(e)))?;
+
+                let bucket_url = Url::parse(&format!("s3://{}/", bucket))
+                    .map_err(|e| datafusion::error::DataFusionError::External(Box::new(e)))?;
+                ctx.runtime_env()
+                    .register_object_store(&bucket_url, Arc::new(s3_store));
+                info!("Registered S3 object store for bucket: {}", bucket);
+            }
+        } else {
+            info!("AWS credentials not available, S3 access may fail");
+        }
+    }
+
+    // Create a stream of record batches from all files
+    let mut all_batches = Vec::new();
+
+    for file_url in file_urls {
+        info!("Reading Parquet file: {}", file_url);
+
+        // Read the Parquet file using DataFusion
+        let df = ctx
+            .read_parquet(&file_url, Default::default())
+            .await
+            .map_err(|e| datafusion::error::DataFusionError::External(Box::new(e)))?;
+
+        let batches = df.collect().await?;
+        let batches_len = batches.len();
+
+        // Log schema information for debugging
+        if !batches.is_empty() {
+            let batch_schema = batches[0].schema();
+            info!("Parquet file {} schema: {:?}", file_url, batch_schema);
+            info!(
+                "Parquet file {} has {} columns",
+                file_url,
+                batch_schema.fields().len()
+            );
+        }
+
+        all_batches.extend(batches);
+
+        info!("Loaded {} batches from {}", batches_len, file_url);
+    }
+
+    info!("Total batches loaded: {}", all_batches.len());
+
+    // Create a stream from the collected batches
+    let stream = futures::stream::iter(all_batches.into_iter().map(Ok));
+    Ok(Box::pin(stream))
+}
diff --git a/pkg/data_cache/src/worker/worker_service.rs b/pkg/data_cache/src/worker/worker_service.rs
new file mode 100644
index 0000000000..fbd3e4ea58
--- /dev/null
+++ b/pkg/data_cache/src/worker/worker_service.rs
@@ -0,0 +1,384 @@
+use super::config::config::DatasetConfig;
+use crate::worker::worker::DataLoader;
+use arrow::array::{ListArray, StringViewArray, UInt64Array};
+use arrow_flight::decode::FlightRecordBatchStream;
+use arrow_flight::encode::FlightDataEncoderBuilder;
+use arrow_flight::error::FlightError;
+use arrow_flight::flight_service_server::FlightServiceServer;
+use arrow_flight::{
+    Action, Criteria, Empty, FlightData, FlightDescriptor, FlightInfo, HandshakeRequest,
+    HandshakeResponse, PollInfo, PutResult, SchemaResult, Ticket,
+    flight_service_server::FlightService,
+};
+use arrow_schema::DataType;
+use bytes::Bytes;
+use datafusion::prelude::{SessionConfig, SessionContext};
+use futures::{Stream, StreamExt, TryStreamExt};
+use serde::{Deserialize, Serialize};
+use std::pin::Pin;
+use std::sync::Arc;
+use tonic::{Request, Response, Status, Streaming};
+use tracing::info;
+
+/// Worker node service implementing Apache Arrow Flight protocol for distributed caching.
+///
+/// This service provides the worker node functionality in the distributed Arrow
+/// caching system. It receives data file assignments from the head node and
+/// serves query results for specific row ranges.
+///
+/// # Architecture
+///
+/// The worker service operates as part of a head-worker distributed system:
+/// - Receives file assignments via Flight `do_put` operations
+/// - Loads assigned data files into memory tables
+/// - Serves query results via Flight `do_get` operations
+/// - Maintains cached data for efficient retrieval
+///
+/// # Flight Protocol Usage
+///
+/// - **`do_put`**: Receives file assignments and row indexing information
+/// - **`do_get`**: Serves query results for specific row ranges
+/// - **`get_schema`**: Returns schema information for cached data
+/// - Other Flight methods are not currently implemented
+///
+/// # Data Flow
+///
+/// ```text
+/// ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
+/// │   Head Node     │───▶│ WorkerService   │───▶│   DataLoader    │
+/// │   (do_put)      │    │  (FlightService)│    │  (load files)   │
+/// └─────────────────┘    └─────────────────┘    └─────────────────┘
+///                                 │                       │
+///                                 ▼                       ▼
+///                        ┌─────────────────┐    ┌─────────────────┐
+/// ┌─────────────────┐    │   Memory Table  │    │   Query Engine  │
+/// │   Client        │◀───┤   (cached data) │◀───┤   (DataFusion)  │
+/// │   (do_get)      │    └─────────────────┘    └─────────────────┘
+/// └─────────────────┘
+/// ```
+///
+/// # Performance Considerations
+///
+/// - Data is cached in memory for fast query response
+/// - Supports streaming queries for large result sets
+/// - Uses DataFusion's query engine for efficient processing
+/// - Maintains global row indexing for distributed coordination
+///
+/// # See Also
+///
+/// - [`DataLoader`]: Loads assigned data files into memory tables
+/// - [`IndexPair`]: Represents row range queries in tickets
+pub struct WorkerService {
+    metadata_loc: String,
+    table_name: String,
+    schema_name: String,
+    ctx: Arc<SessionContext>,
+}
+
+#[tonic::async_trait]
+impl FlightService for WorkerService {
+    type HandshakeStream =
+        Pin<Box<dyn Stream<Item = Result<HandshakeResponse, Status>> + Send + 'static>>;
+    type ListFlightsStream =
+        Pin<Box<dyn Stream<Item = Result<FlightInfo, Status>> + Send + 'static>>;
+    type DoGetStream = Pin<Box<dyn Stream<Item = Result<FlightData, Status>> + Send + 'static>>;
+    type DoPutStream = Pin<Box<dyn Stream<Item = Result<PutResult, Status>> + Send + 'static>>;
+    type DoExchangeStream =
+        Pin<Box<dyn Stream<Item = Result<FlightData, Status>> + Send + 'static>>;
+    type DoActionStream =
+        Pin<Box<dyn Stream<Item = Result<arrow_flight::Result, Status>> + Send + 'static>>;
+    type ListActionsStream =
+        Pin<Box<dyn Stream<Item = Result<arrow_flight::ActionType, Status>> + Send + 'static>>;
+
+    async fn get_schema(
+        &self,
+        _request: Request<FlightDescriptor>,
+    ) -> Result<Response<SchemaResult>, Status> {
+        unimplemented!()
+    }
+
+    /// Retrieves data for a specific row range from the cached table.
+    ///
+    /// This method serves query results for a specific row range specified in the
+    /// ticket. The ticket contains a serialized [`IndexPair`] with start and end
+    /// row indices for the requested data range.
+    ///
+    /// # Parameters
+    ///
+    /// - `request`: Flight ticket containing serialized [`IndexPair`] with row range
+    ///
+    /// # Returns
+    ///
+    /// Returns a Flight data stream containing the requested rows from the cached
+    /// table, excluding the `cache_index` column from the result set.
+    ///
+    /// # Algorithm
+    ///
+    /// 1. Deserialize the [`IndexPair`] from the ticket
+    /// 2. Execute SQL query to select rows in the specified range
+    /// 3. Exclude the `cache_index` column from results
+    /// 4. Encode results as Flight data stream
+    ///
+    /// # Errors
+    ///
+    /// - Returns `Status::internal` if ticket deserialization fails
+    /// - Returns `Status::internal` if SQL query execution fails
+    /// - Returns `Status::internal` if stream creation fails
+    ///
+    /// # Example Query
+    ///
+    /// ```sql
+    /// SELECT * EXCEPT(cache_index) FROM memtable
+    /// WHERE cache_index >= {start} AND cache_index <= {end}
+    /// ```
+    async fn do_get(
+        &self,
+        request: Request<Ticket>,
+    ) -> Result<Response<<Self as FlightService>::DoGetStream>, Status> {
+        info!("querying worker");
+        let ticket = request.into_inner();
+        let pair = bincode::deserialize::<IndexPair>(&ticket.ticket)
+            .map_err(|e| Status::internal(format!("Deserialization error: {}", e)))?;
+        info!("{:?}", pair);
+        let df = self.ctx.sql(format!("select * except(cache_index) from memtable where cache_index >= {} and cache_index <= {}", pair.start, pair.end).as_str()).await
+            .map_err(|e| Status::internal(format!("Error executing query: {}", e)))?;
+        let stream = df
+            .execute_stream()
+            .await
+            .map_err(|e| Status::internal(format!("Error creating stream: {}", e)))?;
+
+        let encoder = FlightDataEncoderBuilder::new()
+            .build(stream.map_err(|e| FlightError::ExternalError(Box::new(e))))
+            .map_err(Status::from);
+        Ok(Response::new(Box::pin(encoder)))
+    }
+
+    async fn handshake(
+        &self,
+        _request: Request<Streaming<HandshakeRequest>>,
+    ) -> Result<Response<Self::HandshakeStream>, Status> {
+        todo!()
+    }
+
+    async fn list_flights(
+        &self,
+        _request: Request<Criteria>,
+    ) -> Result<Response<Self::ListFlightsStream>, Status> {
+        todo!()
+    }
+
+    async fn get_flight_info(
+        &self,
+        _request: Request<FlightDescriptor>,
+    ) -> Result<Response<FlightInfo>, Status> {
+        todo!()
+    }
+
+    async fn poll_flight_info(
+        &self,
+        _request: Request<FlightDescriptor>,
+    ) -> Result<Response<PollInfo>, Status> {
+        todo!()
+    }
+
+    /// Receives file assignments and loads data into the worker's memory table.
+    ///
+    /// This method receives file assignments from the head node and loads the
+    /// specified data files into a memory table for caching. The request contains
+    /// file paths and row indexing information needed for distributed coordination.
+    ///
+    /// # Parameters
+    ///
+    /// - `request`: Flight data stream containing file assignment information
+    ///
+    /// # Expected Input Format
+    ///
+    /// The input RecordBatch must contain the following columns:
+    /// - `file_paths`: List<Utf8View> - Array of file paths to load
+    /// - `row_start_indexes`: UInt64 - Starting row index for global ordering
+    ///
+    /// # Returns
+    ///
+    /// Returns a single [`PutResult`] indicating successful data loading.
+    ///
+    /// # Algorithm
+    ///
+    /// 1. Decode the incoming Flight data stream
+    /// 2. Extract file paths from the `file_paths` column
+    /// 3. Extract starting row index from `row_start_indexes` column
+    /// 4. Create a [`DataLoader`] with the assigned files
+    /// 5. Load data into the memory table named "memtable"
+    /// 6. Verify data loading with a test query
+    ///
+    /// # Error Handling
+    ///
+    /// - Returns `Status::internal` if no RecordBatch is received
+    /// - Returns `Status::internal` if required columns are missing
+    /// - Returns `Status::internal` if column types don't match expected format
+    /// - Returns `Status::internal` if data loading fails
+    ///
+    /// # Performance Considerations
+    ///
+    /// - Data is loaded into memory for fast subsequent queries
+    /// - Uses streaming to handle large file lists efficiently
+    /// - Maintains global row indexing for distributed coordination
+    ///
+    /// # See Also
+    ///
+    /// - [`DataLoader`]: Handles the actual file loading process
+    /// - [`do_get`]: Serves queries against the loaded data
+    async fn do_put(
+        &self,
+        request: Request<Streaming<FlightData>>,
+    ) -> Result<Response<Self::DoPutStream>, Status> {
+        let record_batch = FlightRecordBatchStream::new_from_flight_data(
+            request.into_inner().map_err(|e| e.into()),
+        )
+        .try_next()
+        .await
+        .map_err(|e| Status::internal(format!("Flight data error: {}", e)))?
+        .ok_or_else(|| Status::internal("No record batch received"))?;
+        let file_paths_column = record_batch
+            .column_by_name("file_paths")
+            .ok_or_else(|| Status::internal("file_paths column not found"))?;
+        let start_indexes_column = record_batch
+            .column_by_name("row_start_indexes")
+            .ok_or_else(|| Status::internal("row_start_indexes column not found"))?;
+
+        let file_urls;
+        if let DataType::List(field) = file_paths_column.data_type() {
+            if field.data_type() == &DataType::Utf8View {
+                let list_array = file_paths_column
+                    .as_any()
+                    .downcast_ref::<ListArray>()
+                    .ok_or_else(|| Status::internal("Failed to downcast to ListArray"))?;
+                let values = list_array.values();
+                let string_array = values
+                    .as_any()
+                    .downcast_ref::<StringViewArray>()
+                    .ok_or_else(|| Status::internal("Failed to downcast to StringViewArray"))?;
+
+                file_urls = string_array
+                    .iter()
+                    .map(|opt_str| opt_str.map(|s| s.to_string()).unwrap_or_default())
+                    .collect();
+            } else {
+                return Err(Status::internal(
+                    "Expected List<Utf8>, found List with different item type",
+                ));
+            }
+        } else {
+            return Err(Status::internal("Expected List DataType"));
+        }
+        info!("file_urls received in worker: {:?}", file_urls);
+
+        let start_index = if let DataType::UInt64 = start_indexes_column.data_type() {
+            let list_array = start_indexes_column
+                .as_any()
+                .downcast_ref::<UInt64Array>()
+                .ok_or_else(|| Status::internal("Failed to downcast to UInt64Array"))?;
+            let values = list_array.values();
+            *values
+                .first()
+                .ok_or_else(|| Status::internal("No start index found"))?
+        } else {
+            return Err(Status::internal("Expected UInt64 DataType"));
+        };
+
+        info!("start_index received in worker: {:?}", start_index);
+
+        let data_loader = DataLoader::new(
+            self.metadata_loc.clone(),
+            self.table_name.clone(),
+            self.schema_name.clone(),
+            file_urls,
+            start_index,
+        )
+        .await
+        .map_err(|e| Status::internal(format!("Failed to create data loader: {}", e)))?;
+        data_loader
+            .load_data(&self.ctx.clone(), "memtable", start_index)
+            .await
+            .map_err(|e| Status::internal(format!("Failed to load data: {}", e)))?;
+        let df = self.ctx.sql(format!("select cache_index from memtable where cache_index >= {} and cache_index <= {}", start_index, start_index).as_str())
+            .await.map_err(|e| Status::internal(format!("SQL error: {}", e)))?
+            .collect()
+            .await.map_err(|e| Status::internal(format!("Collection error: {}", e)))?;
+        info!("printing recordbatch");
+        let _ = arrow::util::pretty::print_batches(&df);
+
+        let app_metadata = Bytes::new();
+        let result = PutResult { app_metadata };
+        let stream = futures::stream::iter([Ok(result)]);
+        Ok(Response::new(stream.boxed()))
+    }
+
+    async fn do_exchange(
+        &self,
+        _request: Request<Streaming<FlightData>>,
+    ) -> Result<Response<Self::DoExchangeStream>, Status> {
+        todo!()
+    }
+
+    async fn do_action(
+        &self,
+        _request: Request<Action>,
+    ) -> Result<Response<Self::DoActionStream>, Status> {
+        todo!()
+    }
+
+    async fn list_actions(
+        &self,
+        _request: Request<Empty>,
+    ) -> Result<Response<Self::ListActionsStream>, Status> {
+        todo!()
+    }
+}
+
+impl WorkerService {
+    #[allow(dead_code)]
+    pub fn new(
+        metadata_loc: String,
+        table_name: String,
+        schema_name: String,
+        ctx: Arc<SessionContext>,
+    ) -> Self {
+        Self {
+            metadata_loc,
+            table_name,
+            schema_name,
+            ctx,
+        }
+    }
+}
+
+pub async fn run(
+    host: &String,
+    port: &String,
+) -> datafusion::common::Result<(), Box<dyn std::error::Error>> {
+    let config = SessionConfig::new().with_batch_size(1024);
+    let ctx = Arc::new(SessionContext::new_with_config(config));
+    let addr = format!("{host}:{port}").parse()?;
+    let dataset_config =
+        DatasetConfig::from_env().map_err(|e| format!("Failed to load dataset config: {}", e))?;
+
+    let service = WorkerService {
+        metadata_loc: dataset_config.metadata_loc,
+        table_name: dataset_config.table_name,
+        schema_name: dataset_config.schema_name,
+        ctx: ctx.clone(),
+    };
+    tonic::transport::Server::builder()
+        .add_service(FlightServiceServer::new(service))
+        .serve(addr)
+        .await
+        .map_err(|e| format!("Error starting worker: {}", e))?;
+    Ok(())
+}
+
+//TODO: move to lib.rs
+#[derive(Serialize, Deserialize, Debug)]
+struct IndexPair {
+    start: u64,
+    end: u64,
+}