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    Extract, Load, Transform (ELT)

    Data integration & searchELTELT data pipelineModern Data Stack ELT
    Editorially reviewed on: 1 source
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    The short answer · 18 words

    In short

    Data integration paradigm in which raw data is first loaded into the target warehouse and only transformed there.

    Flow-alerta.cab (2026): Extract, Load, Transform (ELT). https://flow-alerta.cab/en/glossar/extract-load-transform-elt. Editorially reviewed on: 10 October 2026.

    How much detail would you like?

    Practical

    High (standard architecture approach in the Modern Data Stack)

    Hypothetical example: A company loads raw data from Shopify and Google Ads into BigQuery every hour and transforms it into metric tables with dbt.

    Centralization of heterogeneous corporate data for business intelligence, dashboards and AI analyses in SMEs.

    At a glance

    Core
    ELT loads source data into a target data platform before the professional transformation. There they are processed, for example, with SQL and dbt. Raw data management, access rights and retention are conscious architectural decisions; source data is not necessarily completely unchanged.
    Use
    High (standard architecture approach in the Modern Data Stack)
    Example
    Hypothetical example: A company loads raw data from Shopify and Google Ads into BigQuery every hour and transforms it into metric tables with dbt.
    Limits
    Exploding storage costs when unthinkingly loading huge amounts of unstructured data; lack of governance for raw data without a schema.

    In context

    ETL

    ETL extracts data, transforms it and then loads it into a target system.

    Data Warehouse

    A data warehouse integrates and historicizes data for reliable analyses, key figures and reports.

    Questions about this term

    What can be an advantage?

    Transformations use the computing power of the target platform and can be adapted later. Faster implementation and permanent raw data management are not guaranteed.

    What tools are included?

    Extraction/loading tools and transformations on the target platform, for example with dbt.

    All details and background

    ELT loads source data into a target data platform before the professional transformation. There they are processed, for example, with SQL and dbt. Raw data management, access rights and retention are conscious architectural decisions; source data is not necessarily completely unchanged.

    ELI5 – Explained simply

    Instead of cutting vegetables before packing, you throw everything into the food processor and cut it only when you cook.

    Why it matters in practice

    High (standard architecture approach in the Modern Data Stack)

    Example

    Hypothetical example: A company loads raw data from Shopify and Google Ads into BigQuery every hour and transforms it into metric tables with dbt.

    Pitfalls and limits

    • Exploding storage costs when unthinkingly loading huge amounts of unstructured data; lack of governance for raw data without a schema.

    Origin & history

    ELT has been more widely used with scalable data platforms and is used alongside ETL.

    Use in mid-sized companies

    • Centralization of heterogeneous corporate data for business intelligence, dashboards and AI analyses in SMEs.

    Related terms

    Matching Flow-alerta service

    AI opportunity analysis & roadmapAI consulting: Examine the suitability, data and prerequisites of a possible use case on the topic of “Extract, Load and Transform (ELT)”.

    Matching tool / use case

    View use casesUse Case: Processing source data in the warehouse / Tool example: dbt

    Sources

    Quellenprüfung: 10.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.

    Editorially reviewed on:

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