How to Build Scalable Data Pipelines: Lessons from the Data Engineering Book

Data Ingestion 101: Building Robust Pipelines with CDC, Batch, and APIs 🛠️ Data ingestion is the "first gateway" of data engineering. The stability and efficiency of your ingestion layer directly determine the quality of all downstream processing and analytics. In this guide, based on the open-source data_engineering_book , we’ll explore how to handle different data sources, choose the right ingestion patterns, and implement a real-time CDC pipeline. 1. Understanding Your Data Sources We categorize data sources into two main dimensions: Form and Latency . By Form Structured: Databases (MySQL, PostgreSQL), CSVs, or ERP exports with fixed schemas. Semi-Structured: JSON/XML logs, Kafka messages, and NoSQL (MongoDB). These require schema inference or flattening. Unstructured: PDFs, images, and audio/video files. By Latency Batch (Offline): Daily/weekly reports or full database backups. High latency, but high data integrity. Streaming (Real-time): User clickstreams, payment logs, and DB cha