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This project serves as a comprehensive guide to building an end-to-end data engineering pipeline. It covers each stage from data ingestion to processing and finally to storage, utilizing a robust tech stack that includes Apache Airflow, Python, Apache Kafka, Apache Zookeeper, Apache Spark, and Cassandra. Everything is containerized using Docker for ease of deployment and scalability.
The workflow of the project
The project is designed with the following components:
Project Overview
This project leverages a variety of technologies to establish a robust data pipeline. Below is an outline of the key components and their roles within the system.
Data Source
- Random User Generator: We utilize the randomuser.me API to generate random user data, which serves as the initial input for our pipeline.
Data Orchestration
- Apache Airflow: Orchestrates the pipeline, managing the workflow from data ingestion to storage. Airflow is responsible for fetching data from the Random User Generator API and storing it in a PostgreSQL database.
Data Streaming
- Apache Kafka and Zookeeper: Facilitate the streaming of data from the PostgreSQL database to our processing engine. Kafka, in conjunction with Zookeeper, ensures reliable and scalable streaming capabilities.
Monitoring and Schema Management
- Control Center and Schema Registry: Essential for the monitoring and management of Kafka streams. The Control Center provides a comprehensive overview of the system's health and performance, while the Schema Registry maintains the integrity of data schemas within our streams.
Data Processing
- Apache Spark: Employs both master and worker nodes to process the data efficiently. Apache Spark's robust computing capabilities enable complex data transformations and analyses.
Data Storage
- Cassandra: The final destination for our processed data. Cassandra's distributed architecture ensures scalability and reliability for storing large volumes of data.
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