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What Is ETL? A Practical Definition
ETL — Extract, Transform, Load — is the process of pulling data from a source, reshaping it, and writing it somewhere it can be used. Here's what that actually means in practice.
The three stages
Extract pulls data out of a source system — a database table, an API, a file. Transform reshapes it: filtering rows, renaming or computing columns, aggregating values. Load writes the result somewhere it can be used — another table, a report, a dashboard.
Why it matters
Raw source data is rarely in the shape you need it in. A sales database might store one row per transaction, but a report needs revenue totaled by month and region. ETL is the process that bridges the two.
ETL, visually
Traditionally, ETL meant writing scripts — SQL, Python, or a dedicated ETL language — to define each step. Visual tools represent the same three stages as nodes on a canvas instead: an Input node for extract, Filter and Summarize nodes for transform, and an Output node for load. The advantage isn't less capability — it's that the logic is visible to anyone looking at the canvas, not just the person who wrote the script.
See also: ETL vs. ELT, What Is a Visual Workflow Builder?
Frequently asked questions
Extract, Transform, Load — the three stages of moving data from a source system into a usable form somewhere else.
See DataQloo’s Visual Designer for yourself.