ETL is essentially the heavy lifting behind many BI and analytics solutions. In the extract phase you pull data from various systems - relational databases, file stores, APIs and more. During transformation the data is cleaned, mapped, re‑encoded or aggregated to meet the target system’s requirements. Finally, the data is loaded into a unified target such as a data warehouse or data lake.
What makes ETL interesting (and sometimes complex) is the diversity of sources and the demanding nature of targets: you must handle different formats, update cycles, large volumes and requirements for data quality and consistency. Questions arise such as “do we need history?”, “should we use micro‑batches or real‑time streams?”, or “should transformations happen before or after loading (ETL vs ELT)?” With a solid ETL pipeline in place, you have a reliable foundation for dashboards, forecasting and data‑driven decisions.
