Data engineering is an essential part of any data-driven application. It involves creating and maintaining systems that allow data to be collected, stored, processed, and accessed efficiently. In this blog, we will explore three data engineering projects that you can build using Python. Each project will include a complete pipeline, from collecting the data to transforming it and finally storing or visualizing it. These projects will help you understand the key components of a data pipeline and how to implement them.
Project 1: Real-Time Data Ingestion and Processing Pipeline
Overview
In this project, we will build a real-time data ingestion and processing pipeline. Real-time data processing is important for applications that require immediate updates, like financial trading systems, real-time monitoring, or recommendation engines.
Tools You Will Need
- Python — A popular programming language.
- Apache Kafka — A tool to collect and stream data.
- Apache Spark — A tool for processing data in real-time.
- PostgreSQL — A database to store the processed data.
