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ETL stands for extract, transform, and load: a process for moving data from one or more sources, preparing it for a specific purpose, and placing it in a destination such as a data warehouse or data lake. The order matters: ETL transforms data before loading it into the target.
What happens in an ETL process?
A typical workflow moves data from source systems through extraction and transformation to a target. A staging area may temporarily hold extracted data between steps.
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Sources → extract → optional staging → transform → load → target
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Sources can include databases, files, APIs, and websites; destinations can include warehouses, lakes, or other data stores. The staging area may be temporary or retained to help with troubleshooting, depending on the design. AWS explains ETL stages and staging, while Microsoft Learn describes data integration components and sources.
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1. Extract data from sources
Extraction retrieves or copies data from source systems. It can capture updates as they occur, periodically identify changed records, or take a full extract. Full extraction moves more data, so AWS advises limiting it to small tables when no method for identifying changes is available.
2. Transform data for its intended use
Transformation applies the rules needed to make data consistent and useful in the target. A team might map source fields to target fields, standardize formats, combine data from multiple sources, enrich records, remove duplicates, or correct incomplete and erroneous entries. The rules should reflect the target’s requirements and the intended analysis; cleaning alone cannot guarantee accurate results if the source data or rules are flawed. AWS describes extraction, transformation, and loading, and Google Cloud outlines the ETL stages.
3. Load prepared data into a target
Loading writes transformed data to the destination. An initial full load can transfer a complete dataset; later incremental loads apply new or changed records rather than reloading everything. Which approach is suitable depends on how the source tracks changes and how the target is designed.
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Organizations use ETL to consolidate information held in separate systems and prepare it for reporting, business intelligence, analytics, or machine-learning workflows. ETL can connect data from legacy systems and cloud platforms, and can be used with structured or unstructured data. These are common applications, not automatic outcomes: useful analysis still depends on the quality of source data and the transformation rules. IBM describes ETL’s role in analytics.
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How is ETL different from ELT?
ETL and ELT both move and prepare data; their main difference is when and where transformation happens.
| Workflow | Order | Where transformation happens | Design consideration |
|---|---|---|---|
| ETL | Extract, transform, load | Before data enters the target | Transformation rules and target structures are defined before loading. |
| ELT | Extract, load, transform | Inside the target after loading | Raw data can be loaded first and shaped using the target’s processing capabilities. |
ELT can suit cloud environments where the destination has the capacity to process transformations, and some architectures combine ETL and ELT approaches. Neither sequence is universally faster, cheaper, or better; the choice depends on transformation needs, available compute, and whether it is useful to retain raw data before shaping it. Google Cloud explains ELT and hybrid approaches.
How do batch and streaming ETL differ?
Batch pipelines process data in groups at scheduled intervals. Streaming pipelines handle an ongoing flow of data. Streaming describes the processing pattern, not a guaranteed response time: actual latency depends on the system and its configuration. Google Cloud covers batch and streaming ETL, and AWS explains batch and continuous stream processing.
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How should you evaluate an ETL approach or tool?
ETL is a process pattern, not a single product. Services such as AWS Glue are examples of tools that support ETL jobs, but no one approach or tool is best for every workload. Compare options against the systems and operating requirements you actually have:
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- Whether the tool connects to your required source systems and destinations.
- Whether its transformation options can implement your data-quality and business rules.
- Whether it supports the batch or streaming pattern and update frequency you need.
- Where processing happens and whether that environment can handle the workload.
- How it supports monitoring, recovery, and governance.
- What it costs under your expected data volumes and processing pattern.
The right choice depends on the sources, target capabilities, transformations, update frequency, scale, and operating constraints—not on the ETL label alone. Google Cloud and Microsoft Learn describe data-integration capabilities in their respective environments.
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