A Comprehensive Guide to Building a Modern Data Pipeline
This project provides a hands-on experience in building a complete, end-to-end data engineering pipeline. It shows how to ingest, process, and store data using a combination of industry-standard tools and technologies. The entire project is containerized using Docker, making it easy to deploy and replicate the environment.
The project is designed with the following components:
- Data Source: We use
randomuser.meAPI to generate random user data for our pipeline. - Apache Airflow: Responsible for orchestrating the pipeline and storing fetched data in a PostgreSQL database.
- Apache Kafka and Zookeeper: Used for streaming data from PostgreSQL to the processing engine.
- Control Center and Schema Registry: Helps in monitoring and schema management of our Kafka streams.
- Apache Spark: For data processing with its master and worker nodes.
- Cassandra: Where the processed data will be stored.
- Setting up a data pipeline with Apache Airflow
- Real-time data streaming with Apache Kafka
- Distributed synchronization with Apache Zookeeper
- Data processing techniques with Apache Spark
- Data storage solutions with Cassandra and PostgreSQL
- Containerizing your entire data engineering setup with Docker
- Apache Airflow: Data pipeline orchestration.
- Python: For data processing and Airflow DAGs.
- Apache Kafka: Real-time stream processing.
- Apache Zookeeper: Distributed coordination for Kafka.
- Apache Spark: Distributed data processing.
- Cassandra: Highly scalable database.
- PostgreSQL: Relational database for initial data storage.
- Docker: Containerization platform.