Real architecture decisions, trade-offs, and lessons from production systems. Written by engineers, for engineers.
System Blueprints Not all ETL pipelines are the same. Every data source requires a different extraction strategy, transformation approach, and loading pattern. This guide covers every major type, from database migration to API ingestion, streaming, OCR, and media processing.
System Blueprints 80 percent of ETL code is the same across every pipeline. The 80/20 framework captures that common infrastructure so you focus on what makes your pipeline unique.
System Blueprints AI systems need data structured for embeddings and vector storage. Traditional ETL stops at the database. AI-ready ETL continues to the vector store.
System Blueprints Hard-coded field mappings work until they do not. Configuration-driven ETL lets you change behavior without changing code.
System Blueprints Foreign keys create dependencies. Order matters. Load tables wrong and every insert fails. Here is how to manage multi-table dependencies.
System Blueprints When an ETL pipeline fails at 3 AM, you need to know exactly what happened. Event-driven observability gives you that story.
System Blueprints Phone numbers arrive in 47 different formats. Dates come as strings or 0000-00-00. These production-tested cleaners handle edge cases that break naive implementations.
System Blueprints With thousands of records, loading everything into an array crashes your server. Iterator patterns solve this by processing one record at a time, keeping memory constant.
System Blueprints Your ETL pipeline fails when everything is tangled together. The 6-phase pattern separates responsibilities so failures become obvious and debugging becomes easy.
System Blueprints A step-by-step guide to building ETL pipelines that actually work in production. Based on real implementations across multiple data engineering projects.