ETL stands for extract, transform, and load: a data-integration process that collects data from source systems, prepares it for a defined purpose, and writes it to a destination such as a database or data warehouse. In conventional ETL, data is transformed before it is loaded into the target.
What do extract, transform, and load mean?
The three terms describe the main stages of moving data from its sources into a system where it can be used. The stages may be distinct, but they do not always run as isolated jobs or wait for one another to finish.
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Extract
Extraction identifies and copies or exports relevant data from one or more sources. Sources may include databases, business applications, APIs, files, and devices. Extracted data may pass through a staging area before further processing. AWS explains ETL and its stages.
Transform
Transformation applies rules that make data suitable for its intended use and destination. This can include cleaning, filtering, sorting, joining, aggregating, deduplicating, validating, standardizing fields, and shaping data to match the target schema. The rules should reflect what the target or analysis requires, rather than being arbitrary changes.
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Load
Loading writes the prepared data into a target system, such as a database or data warehouse. An initial load may populate the target with a full dataset; later runs may add new or changed records, or refresh the full dataset.
How does ETL work in a data pipeline?
A pipeline connects the stages so that data can move from source systems to a destination on a chosen schedule or as it becomes available. In a simple batch setup, a job extracts source data, applies transformation rules, and then loads the result. A staging area can hold extracted data while it is being prepared.
The phases need not be strictly sequential across an entire dataset. Microsoft’s Azure Architecture Center notes, “Often, the three ETL phases run in parallel to save time.” In practice, different portions of data can be extracted, transformed, and loaded at the same time. Microsoft’s ETL guidance describes this overlap.
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Organizations use ETL to consolidate information from separate systems and prepare it for analytics, reporting, or machine learning. Transformation can make data more consistent and usable for a defined target or analytical purpose. For example, joining records from separate sources or standardizing fields can help produce a dataset that fits a target schema.
ETL describes a process, not a guarantee that data is accurate or useful. The outcome depends on the chosen sources, transformation rules, validation, and loading approach.
ETL versus ELT: what is the difference?
The defining difference is when and where transformation happens. Google Cloud’s overview of ETL also describes the distinction.
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| Pattern | Order | Where transformation happens |
|---|---|---|
| ETL | Extract, transform, load | Before data is loaded into the target |
| ELT | Extract, load, transform | Within the target after loading |
ETL prepares data before it reaches the target. ELT loads extracted data first, often retaining raw data in the destination, and uses the destination’s computing resources for later transformations. Neither pattern is universally best; the choice depends on the target platform’s capabilities, the data’s types and volume, governance and quality needs, and how quickly transformed data is needed.
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No. ETL can run as scheduled batches or use incremental updates that send only new or changed data. Modern architectures can also process continuous streams, sometimes alongside batch pipelines used for backfills or reprocessing. Streaming changes how frequently data is processed; it does not change the meaning of ETL or its defining order of transformation before loading.
When should you think about ETL or ELT?
When choosing a pattern for a particular pipeline, consider where transformations will run and what the destination can handle. Also account for the data’s types and volume, governance and quality controls, how soon raw data must be available, and whether the pipeline needs batch, incremental, or streaming processing. These factors help determine which approach suits the use case; the acronym alone does not.
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