ETL/ELT (Extract Transform Load) is processes that move data from source systems into analytics destinations.
ETL (transform before load) was traditional; ELT (transform after load) won as cloud warehouses became cheap. Modern ELT stack: Fivetran/Stitch (extract+load) + dbt (transform). Reverse ETL (Hightouch, Census) sends warehouse data back to operational tools. By 2026, "modern data stack" is mainstream at $5M+ ARR companies.
ETL/ELT is how data gets from operational systems into the warehouse where it can be analyzed. Without reliable pipelines, dashboards lag, decisions wait, and the data team becomes the bottleneck.
An ETL pipeline pulls daily order data from a Shopify API (extract), enriches each order with customer lifetime value (transform), and loads the result into the analytics warehouse — running every morning before standups.
ETL is not always linear "extract, transform, load." Modern ELT loads raw data first and transforms it inside the warehouse, taking advantage of cheap warehouse compute.
Invest in observability for pipelines (success/failure alerts, row counts, schema drift) before adding more pipelines; a silent failure that nobody notices is worse than no pipeline at all.
ETL/ELT (Extract Transform Load) falls under the Data category.
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