This technical guide provides a comprehensive, modern view of ETL and ELT pipelines, explaining how data ingestion, transformation, and orchestration power analytics, AI, and machine learning workloads. It walks through batch, micro-batch, and streaming ingestion patterns; evaluates data formats, volumes, and source reliability; and explains how lakehouse architectures unify data lakes and warehouses. The guide explores transformation patterns such as enrichment, aggregation, deduplication, and anonymization, alongside update strategies like inserts, upserts, and CDC. It also details orchestration concepts using DAGs, compares tools like Airflow and platform-native orchestration, and outlines best practices for observability, data quality, error handling, and recovery. The updated edition

Join for free to read