ETL vs. ELT: Key Differences and When to Use Each
ETL transforms data before loading it into the destination. ELT loads data first and transforms it there. Choose based on privacy requirements, destination capabilities, cost and how often your analytics needs change.
The difference
ETL means extract, transform, load. ELT means extract, load, transform.
With ETL, a processing step cleans or reshapes data before it reaches the destination. With ELT, transformation uses the destination's processing capabilities after loading.
Choosing an approach
Consider ETL when sensitive fields must be removed before loading, or the destination needs a fixed structure. Consider ELT when a suitable warehouse can handle transformations and teams need different views of the same source data.
Example
A shop might mask customer identifiers before loading a reporting dataset. Alternatively, it could load appropriately protected data into a warehouse and build separate sales and inventory models there.
Interview follow-up
Explain how you would control access, handle schema changes and compare processing costs. ETL is not obsolete, and ELT does not remove governance requirements.
Interview question
Why has the ELT pattern become more prevalent than ETL in modern data architectures?
- a.The rise of powerful cloud data warehouses allows for efficient in-database transformations.Correct
- b.ELT completely eliminates the need for data transformation, simplifying the entire process.
- c.Data extraction and loading tools for ELT are fundamentally faster than those for ETL.
- d.ELT pipelines are inherently more secure and compliant by default.
Why? this is the answer
ELT's popularity is driven by powerful cloud data warehouses that can efficiently handle transformations on raw data, offering greater flexibility. ETL, not ELT, is often chosen for strict compliance because it allows data to be cleaned or anonymized before loading.
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