KB: Data ELT vs ETL

 









ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are both data integration processes used for data management, typically within data warehouses, but they differ in the order of steps and their specific use cases.

ETL (Extract, Transform, Load)

  • Order: Data is first extracted from source systems, then transformed (cleaned, formatted, aggregated, etc.), and finally loaded into the target data warehouse or data store.
  • Transformation Location: Data is transformed in a staging area or in an ETL tool before it reaches the target.
  • Use Case: Traditional data warehouses, where transformations are needed to standardize data before storage. Often suitable for environments with limited storage or compute power.
  • Pros:
    • Ensures data is clean and structured upon arrival in the target system.
    • Good for legacy systems where transformations need to happen outside the data warehouse.
  • Cons:
    • Can be time-consuming and resource-intensive, especially for large datasets.
    • Transformation capabilities are limited by the ETL tool and staging resources.

ELT (Extract, Load, Transform)

  • Order: Data is extracted from source systems, loaded directly into the target data warehouse or lake, and then transformed within the storage system.
  • Transformation Location: Data transformation occurs within the target system, leveraging its compute power.
  • Use Case: Modern cloud-based data lakes and warehouses, where scalability and storage are less of an issue. Often used when dealing with large datasets that benefit from the power of cloud-based data engines.
  • Pros:
    • Faster initial loading of data since transformations happen post-load.
    • Leverages the processing power of modern data warehouses (like Snowflake or BigQuery) for efficient transformations.
  • Cons:
    • Raw data can occupy a lot of storage if not managed properly.
    • Requires powerful data warehousing solutions capable of handling transformations efficiently.

Summary

  • ETL: Useful for traditional data environments with structured data and limited storage.
  • ELT: Suited for cloud-based and scalable environments where you can load raw data directly and perform transformations later.

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