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Data

ETL

Extract, Transform, Load. The pipeline that moves data out of source systems, cleans it into a consistent shape, and loads it into a warehouse for analysis.

ETL describes the pipeline that gets data from where it is created into where it can be used: extract it from source systems, transform it into a clean and consistent shape, and load it into a warehouse or analytics store. A newer variant, ELT, loads the raw data first and transforms it inside the warehouse, which suits modern cloud data platforms.

The reason this matters is that most organisations’ data is scattered across systems that were never designed to be analysed together. The pipeline is what turns that scattered, inconsistent operational data into something a business can actually decide on, and its reliability is what determines whether the dashboards on top of it can be trusted.

Related terms

Working out whether you need ETL?

Definitions are the easy part. If you are trying to decide whether ETL belongs in your system, describe what you are building and a senior engineer will give you a straight answer, including when the answer is that you do not need it.

  1. 01A senior engineer reads it. Not a form queue, and not an account manager.
  2. 02We reply either with questions or with a straight answer that we are not the right fit.
  3. 03If it looks like a fit, a technical call with the person who would actually run the delivery.
  4. 04Then scope, effort and risk in writing, before anyone signs anything.
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