Modern-Data-Architecture-and-related-Concepts

Modern data architecture is a contemporary approach to designing and implementing data systems that can effectively handle the challenges and opportunities presented by the ever-increasing volume, variety, and velocity of data.

Key Characteristics and Components

• Data integration and ingestion: Robust capabilities to collect data from diverse sources (structured, unstructured, streaming, external).
• Data storage and management: Combination of data lakes, data warehouses, and NoSQL databases.
• Data processing and analytics: Support for batch, stream, and real-time analytics using technologies like Apache Spark, Apache Flink, and cloud-based services.
• Cloud-native and hybrid environments: Embracing cloud computing and accommodating hybrid infrastructure.
• Data governance and security: Prioritizing data privacy, compliance, and protection.
• Self-service data access and analytics: Empowering users with tools for data exploration and insights.
• Scalability and agility: Designed to scale seamlessly and integrate new data sources.
• DataOps and DevOps principles: Fostering collaboration and agility in data projects.

Benefits

• Improved data accessibility.
• Faster time-to-insights.
• Enhanced decision-making.
• Leveraging advanced analytics.

Important Note

Modern data architecture is not a one-size-fits-all approach. Organizations should tailor it to their specific needs.

Topics Covered

• DATA WAREHOUSE
• DATA MART
• DATA LAKE
• DATA PIPELINE
• DATA MESH
• DATA LAKE HOUSE
• DATA SWAMP
• DATA FABRIC

Details

DATA WAREHOUSE

• Centralized repository for structured and semi-structured data.
• Supports business intelligence (BI) activities.
• Uses Extract, Transform, Load (ETL) process.
• Optimized for analytical processing (OLAP).
• Benefits: Data integration, quality, decision-making, historical analysis, performance, scalability, security.

DATA MART

• Subset of a data warehouse focused on a specific business function.
• Tailored view of data for a specific user group.
• Simplified access and improved performance.
• Enhanced data governance and flexibility.
• Cost-effective compared to a full data warehouse.

DATA LAKE

• Centralized repository for raw, diverse data (structured, semi-structured, unstructured).
• Stores data in its native format.
• Scalable and uses "schema-on-read."
• Supports data exploration and discovery.
• Integrates with analytical tools.
• Requires robust governance and security.
• Promotes data democratization.

DATA PIPELINE

• Processes to extract, transform, and load (ETL) data.
• Components: Ingestion, transformation, storage, integration, validation, orchestration, delivery.
• Tools: Apache Kafka, Airflow, Spark, NiFi.
• Benefits: Reliability, freshness, automation, scalability, governance.

DATA MESH

• Decentralized approach to data management.
• Domain-oriented teams with data ownership.
• Self-serve data infrastructure.
• Federated data governance.
• Data as a product.
• Addresses scalability and agility challenges.

DATA LAKE HOUSE

• Combines data lake and data warehouse features.
• Stores raw data with schema enforcement.
• Optimized query engines and indexing.
• Supports hybrid workloads.
• Incorporates data governance and security.

DATA SWAMP

• Disorganized and unmanageable data lake.
• Lacks governance, structure, and quality control.
• Characteristics: Lack of organization, poor quality, limited discoverability, inadequate governance, lack of collaboration.
• Negative consequences: reduced productivity, inaccurate decisions, increased costs, compliance and security risks.
• Requires governance and data maintenance.

DATA FABRIC

• Architectural approach for seamless data integration across distributed systems.
• Unified view of data assets.
• Characteristics: Integration, distributed management, abstraction, governance, metadata, orchestration, scalability.
• Benefits: Agility, integration, governance, flexibility, reduced silos.