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ETL stands for extract, transform, and load: a process for collecting data from one or more sources, preparing it for a specific use, and placing it in a destination such as a database, data warehouse, or data lake. The order matters: in ETL, data is transformed before it is loaded into the target.
What the three ETL steps do
1. Extract data from its sources
Extraction retrieves or copies data from systems such as databases, files, APIs, and applications. Depending on the source and its change-tracking capabilities, an extraction may capture updates continuously, check for changes periodically, or copy all available data. A staging area can temporarily hold extracted data before the next step. AWS describes these extraction and staging patterns in its ETL overview.
A full extraction can move more data than necessary. AWS notes that it is generally best limited to small tables when there is no mechanism for identifying changes; where possible, capturing only new or changed records can reduce unnecessary transfers.
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2. Transform the data
Transformation prepares data to meet the target’s format and the needs of its users. Teams can map source fields to target fields, standardize values, join data from multiple sources, remove duplicates, correct errors, filter incomplete records, or enrich records with additional information. These rules also encode assumptions about what the data means, so the quality of later reports or analyses depends on both the source data and the transformations applied. Google Cloud describes the stages and common uses of ETL in its ETL overview.
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3. Load the prepared data
Loading writes transformed data to its destination, such as a warehouse, lake, or other data store. An initial full load transfers a complete dataset; subsequent incremental loads apply new or changed records. The destination is where downstream users or systems can access the prepared data.
How an ETL workflow fits together
A basic flow looks like this:
Sources → extract → optional staging → transform → load → target
The staging area is an intermediate stop, not a required destination in every design. AWS notes that staging may be transient or retained, including for troubleshooting. A pipeline can also include scheduling, monitoring, and recovery around the data movement and transformation. AWS explains the broader concept in its ETL pipeline overview.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallETL is one kind of data pipeline. A pipeline is the wider workflow that moves and processes data; an ETL pipeline specifically follows the extract-transform-load sequence. Pipelines may run in batches or process a continuing stream of data.
ETL versus ELT
ETL and ELT both move data from sources to a destination. Their difference is when and where transformation occurs.
| Dimension | ETL | ELT |
|---|---|---|
| Order | Extract, transform, then load | Extract, load, then transform |
| Where transformation happens | Before data enters the target | Inside the target data store, after loading |
| Design consideration | Transformation rules and target structures are established before loading | Raw data can be loaded first and shaped later using the target’s processing capabilities |
ELT can be useful when a target system can handle the transformation workload or when keeping raw data before shaping it is valuable. Google Cloud also notes that some modern architectures use a hybrid approach. Neither sequence is inherently faster, cheaper, or better in every situation; the choice depends on the source systems, destination, transformations, and operating constraints. See Google Cloud’s ELT overview for the sequence and hybrid context.
Batch ETL and streaming ETL
Batch processing
Batch pipelines process groups of records at scheduled intervals. This pattern can suit data that does not need to be updated continuously, such as periodic reporting data.
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Streaming processing
Streaming pipelines handle continuing data flows as they arrive. Streaming does not by itself guarantee a particular delivery time: actual latency depends on the system and its configuration. AWS discusses the distinction between continuous stream processing and periodic batch runs in its ETL pipeline overview; Google Cloud also describes batch and streaming pipelines in its ETL overview.
Why organizations use ETL
ETL can bring data together from separate systems and prepare it for reporting, business intelligence, analytics, or machine-learning workflows. It can be used with structured or unstructured data and with legacy or cloud systems. Integration alone does not ensure reliable results: source quality and transformation rules still determine whether the prepared data is useful. IBM provides additional context on ETL and analytics in its ETL overview.
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What to consider when choosing an ETL approach or tool
ETL is a process pattern, not a particular product. AWS Glue is one documented service example; Google Cloud and Microsoft also describe data-integration capabilities. There is no context-free best tool or architecture. Compare options against the actual workload:
- Connectivity: Can the system connect to the required sources and destination?
- Transformation needs: Can it express the cleaning, mapping, joining, and enrichment rules the data requires?
- Timing and volume: Does the workload need scheduled batches, streaming, or both, and how much data must move?
- Processing location: Should transformation happen before loading, within the target, or across a hybrid design?
- Operations and governance: What monitoring, recovery, access controls, and data-governance capabilities are needed?
- Cost: How do processing and storage costs behave for the expected workload?
For a specific service example, see the AWS Glue concepts documentation. Microsoft describes its data-integration context in Microsoft Fabric’s Data Factory overview.
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