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    Data Lake

    Data integration & searchEnterprise Data Lake
    Editorially reviewed on: 1 source
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    The short answer · 20 words

    In short

    A data lake stores large amounts of heterogeneous data flexibly and provides them for various later processing and analysis purposes.

    Flow-alerta.cab (2026): Data Lake. https://flow-alerta.cab/en/glossar/data-lake. Editorially reviewed on: 10 October 2026.

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    Practical

    A data lake can support analysis, archiving and ML on a common database. The benefit only arises through catalogue, quality, security, cost control and clearly responsible data products.

    Machine logs, order data and documents end up in versions in object memories; curated zones supply analyses and models.

    SMEs should pre-determine sources, controllers, protection classes, storage and specific use cases. A small curated area is usually more valuable than a disorderly complete import.

    At a glance

    Core
    A data lake is a scalable repository for structured, semi-structured and unstructured data. Data can be available in original or processed form; structure and processing are often only defined for the respective use case. It must be distinguished from Data Warehouse and Lakehouse.
    Use
    A data lake can support analysis, archiving and ML on a common database. The benefit only arises through catalogue, quality, security, cost control and clearly responsible data products.
    Example
    Machine logs, order data and documents end up in versions in object memories; curated zones supply analyses and models.
    Limits
    “Save everything” without purpose, metadata or owner creates a data swamp. Other risks include sensitive data, uncontrolled copies, small files, missing life cycle rules, and unexpected query costs.

    In context

    Data Warehouse

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

    Questions about this term

    Data Lake or Warehouse? The lake is more flexible for heterogeneous data; the warehouse optimizes curated analyses. Does everything have to remain raw? No. Is schema-on-read mandatory? No, it's a common pattern. What is a Lakehouse? An architecture with additional table and transaction features on Lake storage.

    All details and background

    A data lake is a scalable repository for structured, semi-structured and unstructured data. Data can be available in original or processed form; structure and processing are often only defined for the respective use case. It must be distinguished from Data Warehouse and Lakehouse.

    ELI5 – Explained simply

    Like a large lake, it collects data from various tributaries. Before drinking from it, you need a known access and a suitable preparation.

    Why it matters in practice

    A data lake can support analysis, archiving and ML on a common database. The benefit only arises through catalogue, quality, security, cost control and clearly responsible data products.

    Example

    Machine logs, order data and documents end up in versions in object memories; curated zones supply analyses and models.

    Pitfalls and limits

    • “Save everything” without purpose, metadata or owner creates a data swamp. Other risks include sensitive data, uncontrolled copies, small files, missing life cycle rules, and unexpected query costs.

    Origin & history

    In 2010, James Dixon coined the Data Lake metaphor in contrast to processed data marts. Today’s architectures use the term more broadly, often combining multiple sources, zones and processing services.

    Use in mid-sized companies

    • SMEs should pre-determine sources, controllers, protection classes, storage and specific use cases. A small curated area is usually more valuable than a disorderly complete import.

    Related terms

    Matching Flow-alerta service

    AI assistants & automationData architecture and integration: prioritize sources, design zone model, metadata, accesses, quality and usable data pipelines.

    Matching tool / use case

    View use casesAmazon S3, Azure Data Lake Storage or Google Cloud Storage/BigLake; Use Case: central storage of operational, document and sensor data for curated analyses.

    Sources

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

    Editorially reviewed on:

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