Learn to build a data engineering system with Kafka, Spark, Airflow, Postgres, and Docker. This tutorial offers a step-by-step guide to building a complete pipeline using real-world data, ideal for beginners interested in practical data engineering applications.

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The post provides a detailed guide on building an end-to-end data engineering system using Kafka for data streaming, Spark for data transformation, Airflow for orchestration, PostgreSQL for storage, and Docker for setup and deployment. It is structured into two phases: the first focuses on constructing the data pipeline, while the second will cover creating an application to interact with the database using language models. This project is particularly suited for beginners to data engineering, aiming to deepen their practical knowledge of handling data systems.

End-to-End Data Engineering System on Real Data with Kafka, Spark, Airflow, Postgres, and Docker