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ETL means extract, transform, and load: a data integration process that gathers information from source systems, prepares it for a specific use, and writes it to a destination such as a database or data warehouse. In the conventional ETL sequence, transformation happens before the prepared data is loaded.
What do extract, transform, and load mean?
ETL describes three kinds of work in a data pipeline. The sources may include databases, business applications, APIs, files, or devices. A staging area can hold data after extraction while it is prepared for the destination.
- Extract: collect or copy the relevant data from one or more source systems.
- Transform: prepare that data for its intended purpose and target. Rules may clean, filter, sort, join, aggregate, deduplicate, validate, standardize fields, or apply the target’s schema.
- Load: write the prepared data into the destination system. An initial load may populate it with a full dataset; later loads may send only new or changed data, or refresh the full dataset.
These phases describe the work, not necessarily three separate jobs that run one after another. Microsoft’s Azure Architecture Center notes that phases can overlap or run in parallel as portions of data become available.
Why use ETL?
Organizations use ETL to combine information from separate systems and prepare it for analytics, reporting, or machine learning. Transformation rules help make data consistent and usable for a defined target or analytical task. For example, a pipeline can bring together records from different sources, standardize fields, validate values, and aggregate results before loading them for analysis.
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How does ETL differ from ELT?
ETL and ELT both move and prepare data. The defining difference is when and where transformation happens.
| Pattern | Order | Where transformation happens |
|---|---|---|
| ETL | Extract, transform, load | Before data is loaded into the target |
| ELT | Extract, load, transform | After data is loaded, within the destination |
ETL prepares data before it reaches the target. ELT makes raw data available in the destination first and uses that platform’s computing resources for later transformations. The choice can depend on the target’s processing capabilities, data types and volume, governance requirements, and how quickly data needs to be available. Neither pattern is universally best; each suits different technical and operational needs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does ETL have to run in batches?
No. ETL commonly runs on a schedule in batches or processes incremental updates, but modern pipelines can also process continuous streams. Streaming changes how often data moves and how the pipeline is organized; it does not change what ETL stands for. Batch processing can also be used alongside streaming for backfills or reprocessing.
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What to remember
- ETL stands for extract, transform, and load.
- In the conventional ETL pattern, data is transformed before it is loaded into the target.
- Extraction can collect data from multiple sources, with a staging area between extraction and processing.
- Transformation prepares data for its intended purpose; loading writes the prepared result to a destination.
- ETL may use batch, incremental, or streaming approaches, and its phases need not be isolated sequential jobs.
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