ETL Pipeline Basics: Extract, Transform, Load
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. --- What ETL Actually Does An ETL pipeline moves data from one or more sources into a destination, reshaping it along the way. The three phases are sequential by design: you can't transform what you haven't extracted, and you shouldn't load data that hasn't been validated. The alternative, ELT (Extract, Load, Transform), loads raw data first…
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