ETL Pipeline Basics: Extract, Transform, Load Explained With Examples
Understand ETL pipelines: extraction strategies, transformation patterns, loading targets, and tools like Airflow, dbt, and Fivetran for each stage.
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Tags: etl, data-engineering, pipelines
ETL Pipeline Basics: Extract, Transform, Load Explained With Examples ETL — Extract, Transform, Load — is the backbone of every data integration system. Whether you're moving data from a SaaS API into a data warehouse, syncing databases, or feeding a machine learning pipeline, you're doing ETL. This guide covers how each phase works in practice, where engineers get tripped up, and how to build pipelines that stay reliable under real-world conditions. Phase 1: Extract Extraction is about getting data out of source systems faithfully. This sounds simple but has several sharp edges. Source Types Relational databases — Query tables directly via JDBC/ODBC. For full extracts, a works. For incremental extracts, you need a high-watermark column like or . REST APIs — Most real-world APIs paginate.…
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