A collection of real-world Data Engineering projects showcasing ETL pipelines, cloud integrations, data processing frameworks, and orchestration tools using Python, SQL, Spark, Airflow, AWS, and more.
This repository contains multiple real-world Data Engineering projects designed to showcase my practical skills across:
- ETL pipelines
- Data orchestration
- Distributed processing
- Cloud storage
- SQL data transformations
Each folder contains independent mini-projects focusing on different areas of Data Engineering:
- ETL Pipelines: Extracting, transforming, and loading data using Python and Pandas.
- Airflow DAGs: Automating daily, weekly, and monthly data pipelines.
- Spark Jobs: Distributed processing of large datasets using PySpark.
- AWS Integrations: Interacting with AWS S3, Lambda, and Redshift for cloud-native pipelines.
- SQL Queries: Writing and optimizing complex SQL queries for reporting and data transformations.
- Dockerization: Packaging pipelines inside Docker containers for reproducible deployment.
This repository will be continuously updated as I explore more advanced topics, cloud architectures, and large-scale data processing systems.
- Python: Data processing, ETL pipelines, API integrations
- SQL: Data extraction, transformation, and query optimization
- Apache Airflow: Workflow orchestration and job scheduling
- Apache Spark: Distributed data processing
- AWS (S3, Lambda, Redshift, Glue): Cloud-based storage and compute
- Docker: Containerization of data pipelines
- Pandas / NumPy: Data wrangling and analysis
data-engineering-projects/
│
├── etl/ # ETL pipelines written in Python
├── airflow/ # Airflow DAGs for workflow automation
├── spark/ # Spark jobs for distributed processing
├── aws/ # AWS Lambda, Glue, S3 scripts
├── sql/ # SQL queries and transformations
├── docker/ # Dockerfiles for pipeline deployments
└── README.md # Project documentation