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How ETL and ELT workflows differ
Both patterns start by extracting data from sources such as databases, files, APIs, SaaS applications, sensors, or application events. Transformations may change data types and formats, clean or standardize values, remove duplicates, enrich records, or combine sources.
| Decision point | ETL | ELT |
|---|---|---|
| Order | Extract → transform → load | Extract → load → transform |
| Where transformation happens | Before data reaches the target, often in a separate processing environment | After loading, typically in the warehouse, lake, or analytics platform |
| What arrives in the target | Prepared, transformed data | Raw or minimally processed data; analytics-ready models still need transformation |
| Possible fit | Pre-load standardization, fixed-format or legacy destinations, established processes, edge filtering, or reducing processing in the destination | Capable cloud analytics platforms, large datasets, iterative modeling, or retaining source data for later use |
| Key checks | Processing infrastructure, destination format compatibility, what must be filtered before loading, and whether the target should receive raw data | Compute and storage costs, raw-data governance and access, transformation controls, and operational readiness |
AWS describes ETL transformations as occurring on a secondary processing server and ELT transformations in the target warehouse; Microsoft Learn also distinguishes pre-load ETL from post-load ELT. AWS’s ETL and ELT comparison and Microsoft Fabric Data Factory documentation describe these patterns.
What the workflows look like in practice
ETL: prepare before loading
Suppose a team needs to combine sales records from a database with information from historical scanned documents. In an ETL workflow, it can standardize and check the records in a processing stage, then load the prepared dataset into its destination. Microsoft gives cleaning, standardizing, and enriching data before loading as ETL examples.
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ELT: load, then prepare in the target
With ELT, the team can first land source data in a warehouse or lake, then create analysis-ready tables there. The target platform runs the transformations after the data arrives. This can make raw or minimally processed data available for later modeling, but it does not mean that every pipeline loads all data unchanged.
When ETL is a better fit
ETL is worth considering when important preparation must happen before data enters the target, or when the destination has limited processing capacity or expects a specific format. It may also suit an organization that already has a working pre-load process. Google notes that ETL can be useful where such a process exists or where the aim is to reduce resource use in BigQuery; Microsoft describes pre-load cleaning and standardization as ETL use cases. Those are platform-specific considerations, not proof that ETL is always less expensive or more secure.
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- Data must be filtered, masked, standardized, or checked before it reaches the target.
- The destination accepts only a fixed or constrained data format.
- An existing ETL process already meets quality and governance requirements.
- It is preferable to do some processing outside the analytics destination.
When ELT is a better fit
ELT can suit a warehouse, lake, or analytics platform with the capacity and controls to transform data after arrival. Loading first can support large datasets and repeated modeling, but raw-data access, storage, transformation costs, and operational controls need to be planned. Google recommends ELT to most BigQuery customers, and Microsoft says it works well for large datasets using modern cloud-scale compute; these recommendations reflect those platforms and their assumptions, not a universal rule.
- The target can reliably and economically run the required transformations.
- The team benefits from retaining source data and revisiting its models.
- Analysts need access to landed data before every business transformation is complete.
- Governance and permissions are in place for raw or minimally processed data.
How to choose for your workload
Assess the whole data path rather than choosing by acronym or product label. ETL and ELT are workflow patterns; individual platforms may support either or both.
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- Identify pre-load requirements. Decide whether data must be filtered, masked, standardized, or validated before entering the target.
- Assess the target. Confirm it can run the required transformations reliably, and estimate the associated compute and storage costs.
- Consider how data will be used. Determine whether the team needs early access to landed data or expects to rework models as requirements change.
- Check formats and governance. Establish which formats the destination accepts, who can access raw and transformed data, and what retention and quality controls apply at each stage.
- Compare operating costs and reprocessing. Include the processing environment, destination resources, storage, and the cost of rerunning work for the actual volume and workload.
- Keep a suitable existing process. If an established pipeline meets the requirements, changing patterns is not automatically an improvement.
There is no universal speed, cost, or security winner: results depend on sources, destination, data volumes, transformations, workload, and service configuration. Compare the actual architecture and controls.
Hybrid workflows and related terms
Combining ETL and ELT
A hybrid pipeline may do essential filtering or standardization before loading, then perform business transformations in the target analytics platform. Microsoft documents classic ETL, ELT, and combined workflows in Fabric Data Factory.
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Reverse ETL
Reverse ETL is a downstream movement pattern: processed query results or tables are exported from an analytics platform to other systems. Google describes exporting processed BigQuery data to other systems; reverse ETL is not another name for loading source data and transforming it in a warehouse.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Examples of platforms and tools
These are vendor-described examples of how products can fit the patterns, not neutral endorsements or guarantees of performance.
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- AWS: AWS describes Glue for serverless, event-driven and no-code ETL jobs, Redshift for ELT workflows, and Greengrass for edge ETL in its ETL and ELT overview.
- Google Cloud: BigQuery supports loading data and transforming it within BigQuery. Its documentation describes Dataform as a way to build collaborative SQL transformation pipelines with testing, documentation, and scheduling. See BigQuery’s loading, transforming, and exporting documentation.
- Microsoft: Fabric Data Factory documentation describes classic ETL, ELT, and combined workflows.
- dbt: dbt focuses on transforming raw warehouse data into data products. Its documentation covers version control, testing, modularity, CI/CD, and documentation; dbt is a transformation option in an ELT architecture, not a complete source-extraction and loading system by itself. See the dbt Developer Hub introduction.
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