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    Flow-alerta.cab

    Data Pipeline

    Data integration & searchData processing chainData flow pipeline
    Editorially reviewed on: 1 source
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    The short answer · 16 words

    In short

    A data pipeline moves and processes data traceably from sources via defined steps to a goal.

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

    How much detail would you like?

    Practical

    Pipelines automate recurring data flows, reduce manual transmissions, and create reproducible foundations for reports, search, and AI applications.

    Order data is read from the ERP, validated, unified and written daily into a data warehouse.

    SMEs should start with a critical data flow, document inputs and outputs, and establish accountability, monitoring, restart and data quality.

    At a glance

    Core
    A data pipeline is a coherent processing chain of sources, transformations and destinations. It can read, check, filter, enrich or store data. Batch and streaming are possible modes of operation; not every pipeline is ETL or modeled as DAG.
    Use
    Pipelines automate recurring data flows, reduce manual transmissions, and create reproducible foundations for reports, search, and AI applications.
    Example
    Order data is read from the ERP, validated, unified and written daily into a data warehouse.
    Limits
    Equivalent pipeline and orchestra, ignoring error paths and repeatability, missing data contracts, or rolling out changes without testing. Airflow controls tasks; Beam processes data – the roles differ.

    In context

    Data Lineage

    Data lineage makes it possible to understand from which sources data originates, how it flows and through which transformations it is changed.

    Questions about this term

    Is every pipeline ETL?

    No.

    Does it have to run in real time?

    No.

    What happens to mistakes?

    This must be defined by repetition, quarantine or demolition.

    Is a DAG the same?

    No, a DAG can represent dependencies.

    All details and background

    A data pipeline is a coherent processing chain of sources, transformations and destinations. It can read, check, filter, enrich or store data. Batch and streaming are possible modes of operation; not every pipeline is ETL or modeled as DAG.

    ELI5 – Explained simply

    Like an assembly line, it transports data from station to station, with each station taking on a clearly defined task.

    Why it matters in practice

    Pipelines automate recurring data flows, reduce manual transmissions, and create reproducible foundations for reports, search, and AI applications.

    Example

    Order data is read from the ERP, validated, unified and written daily into a data warehouse.

    Pitfalls and limits

    • Equivalent pipeline and orchestra, ignoring error paths and repeatability, missing data contracts, or rolling out changes without testing. Airflow controls tasks; Beam processes data – the roles differ.

    Origin & history

    Data pipelines evolved from ETL, integration and batch processing. Modern frameworks also support distributed streaming processing and portable execution models.

    Use in mid-sized companies

    • SMEs should start with a critical data flow, document inputs and outputs, and establish accountability, monitoring, restart and data quality.

    Related terms

    • ETL/ELT
    • Workflow orchestration
    • Data lineage
    • Data Contract
    • Streaming

    Matching Flow-alerta service

    AI assistants & automationData integration and automation: record data flows, implement robust processing steps, quality controls and monitoring.

    Matching tool / use case

    View use casesApache Beam for processing or Airflow for orchestration; use case: daily import, inspection and provision of order data.

    Sources

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

    Editorially reviewed on:

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