Glossary
The infrastructure that extracts data from source systems, transforms it into a consistent format, and loads it into a destination — like a data warehouse or another application — the plumbing that keeps disconnected systems working from consistent data.
Why It Matters
Different systems store the same real-world concept — a customer, an order — in different formats and structures. ETL pipelines are what reconcile those differences so a business intelligence dashboard, an AI system, or a downstream application can rely on clean, consistent data instead of each source's raw, incompatible format.
This is part of our Connected Operations capability — the work that removes manual reconciliation between systems that were never designed to talk to each other.
See the full Connected Operations capabilityRelated Terms
What's the difference between ETL and ELT?
ETL transforms data before loading it into the destination; ELT loads raw data first and transforms it afterward, inside the destination system — a distinction that mostly reflects where the transformation compute happens, driven by modern warehouse capabilities.
Does every business need a formal ETL pipeline?
Businesses with only one or two systems and simple reporting needs often don't. It becomes necessary once multiple systems need to stay consistent, or once downstream systems (like an AI pipeline or BI dashboard) depend on clean, unified data.