🏗️ IBM Data Engineering Capstone Project

This capstone project showcases the practical application of key data engineering skills by simulating a real-world scenario in which I served as a Junior Data Engineer. I designed and implemented a scalable data analytics platform by working across various technologies in the data engineering lifecycle.

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🚀 Project Overview

This capstone project simulates the role of a Junior Data Engineer tasked with designing and implementing an end-to-end data analytics platform using multiple data engineering tools and technologies.
It’s the final course in the IBM Data Engineering Professional Certificate, combining all prior learning into one practical project.

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🧠 What I Learned

✅ Design and build data platforms using OLTP & OLAP architectures
✅ Implement data pipelines with ETL processes using Python and Apache Airflow
✅ Query structured and unstructured data using MySQL, PostgreSQL, and MongoDB
✅ Perform big data analytics and ML predictions using Apache Spark
✅ Visualize insights via dashboards in Google Looker Studio and IBM Cognos Analytics

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🧰 Skills & Tools Used

• 🐍 Python & SQL
• 🐘 PostgreSQL | 🐬 MySQL | 🍃 MongoDB
• 🛠️ Apache Airflow
• 🔍 Apache Spark (MLlib)
• 📊 IBM Cognos Analytics | Google Looker Studio
• 🗃️ OLTP & Data Warehousing
• 🧱 ETL & Data Pipelines
• 🐧 Linux Shell Scripting
• 📂 JSON, CSV, .tar.gz, and data transformations

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📦 Modules Breakdown

Module	Description
📁 1. Data Platform Architecture & OLTP	Designed OLTP schemas & created MySQL databases
🍃 2. NoSQL with MongoDB	Queried JSON documents and used MongoDB indexes
🗄️ 3. Data Warehouse	Built dimensional models & populated warehouse tables
📈 4. Data Analytics & Reporting	Wrote complex SQL queries with ROLLUP, CUBE, and aggregations
🔁 5. ETL & Pipelines	Built ETL flows with Python scripts and Apache Airflow DAGs
⚡ 6. Big Data Analytics with Spark	Trained and deployed ML models using Spark MLlib
✅ 7. Final Submission	Delivered final reports, dashboards, and peer-reviewed projects

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📊 Dashboard Samples

Tool	Preview
Google Looker Studio
IBM Cognos Analytics

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📂 Project Assets

📁 OLTP Database Design
📁 NoSQL Queries & Exports
📁 Data Warehouse Scripts & CSVs
📁 Airflow DAGs & Python Scripts
📁 SparkML Model & Predictions
📁 Dashboards (Google Looker, Cognos)

📌 Key Skills Demonstrated

• 🗃️ Relational & NoSQL Database Design (MySQL, MongoDB)
• 🏗️ Data Warehouse Modeling and Querying (PostgreSQL, IBM Db2)
• 🔄 ETL Pipeline Development (Python, Shell, Apache Airflow)
• 🔥 Big Data Analytics with Apache Spark
• 📊 Data Visualization (Google Looker Studio, IBM Cognos Analytics)
• 🐧 Linux Shell Scripting
• 🧪 SQL queries using ROLLUP, CUBE, GROUPING SETS, and Materialized Query Tables (MQTs)

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🧪 Capstone Modules & Labs Overview

📁 Module 1: Data Platform Architecture & OLTP

• Designed an OLTP schema and created MySQL tables.
• Imported and exported data using SQL and shell scripts.
• Defined primary keys and indexes for optimized access.

🍃 Module 2: Querying Data in NoSQL (MongoDB)

• Loaded product catalog data into MongoDB.
• Performed filter queries and aggregation pipelines.
• Exported collections using mongoexport.

🏗️ Module 3: Building a Data Warehouse

• Created star schema with dimensions and fact tables in PostgreSQL.
• Imported e-commerce sales data.
• Performed OLAP queries with CUBE, ROLLUP, and GROUPING SETS.

📈 Module 4: Data Analytics

• Wrote analytical SQL queries to uncover trends in sales data.
• Used Materialized Query Tables to improve performance.

🔁 Module 5: ETL & Data Pipelines

• Wrote Python scripts for extract, transform, and load processes.
• Automated the pipeline using Apache Airflow DAGs.
• Processed and cleaned web logs into structured format.

⚡ Module 6: Big Data Analytics with Apache Spark

• Used Spark to load and transform product review data.
• Built a machine learning model using Spark MLlib.
• Saved and reloaded the trained model for prediction tasks.

📊 Module 7: Dashboards & Final Submission

• Built sales dashboards using:
  • Google Looker Studio: Interactive charts, filters, KPIs.
  • IBM Cognos Analytics: Custom visualizations and report generation.
• Submitted final project artifacts for peer review.

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🧠 Summary

This project helped solidify my knowledge of:

• Building data infrastructure from ground up
• Managing both structured and semi-structured data
• Automating and scaling data workflows
• Communicating data insights through visual tools

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🏁 Outcome

✅ Proficiency in end-to-end data engineering workflows
✅ Prepared for real-world junior-level data engineering roles

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🧠 Reflections

This project was a culmination of weeks of learning and hands-on practice. I strengthened my data engineering foundations and became confident in building real-world data solutions end-to-end. 🧩💡

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💼 Ideal For

• Hiring managers evaluating full-stack data engineers
• Recruiters seeking professionals skilled in data architecture, pipelines, and analytics
• Anyone interested in practical data engineering workflows

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🔗 Related Projects

• ETL Pipeline Automation using Airflow
• MongoDB NoSQL Data Exploration
• Apache Spark ML Prediction Models

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🏁 Let's Connect!

If you're interested in my other data projects or collaborations:
🌐 My Portfolio | 💼 LinkedIn | 📂 GitHub Projects