ETL (Extract, Transform, Load)
The process of moving data from source systems into a data warehouse, cleaning it along the way.
What is ETL (Extract, Transform, Load)?
ETL is the foundational data engineering pattern. **Extract** raw data from source systems (databases, APIs, files). **Transform** it — clean, aggregate, validate, join. **Load** the result into a destination warehouse or database where analysts can query it.
Modern variants flip the order — **ELT** loads raw data first, then transforms in the warehouse using SQL (often dbt). This is the dominant pattern in 2026 at Indian product companies — Snowflake / BigQuery / Redshift + dbt for transformation.
For data analysts and scientists, understanding the ETL pipeline upstream of your dashboards is critical. The 9-5pm of most data analyst jobs is built on someone else having designed the ETL well.
ETL / ELT is core data engineering. Even pure analysts need to understand it to debug data quality issues.
A D2C beauty brand's ETL pipeline pulls orders from Shopify, ad spend from Meta + Google, and inventory from their WMS, joins them nightly in BigQuery, and serves the dashboards their CMO opens at 9am.
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