Data Pipelines: Efficient Data Movement and Transformation
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Data Pipelines: Efficient Data Movement and Transformation

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Learn how data pipelines facilitate smooth data flow from sources to destinations, ensuring efficient management.

Page Summary

An explainer of data pipelines, the automated workflows that extract data from source systems, transform it, and load it into destinations for analysis, opening with Uber's nightly cascade producing company-wide dashboards by morning. It covers the foundational ETL-versus-ELT decision, the anatomy of a modern pipeline (source connectors, streaming infrastructure, processing engines, storage, orchestration), batch versus streaming, ensuring reliability through idempotency, data-quality validation, monitoring, and incremental processing, and schema evolution, slowly changing dimensions, and governance at scale.

Contributions

Every accepted correction to this page is recorded with the exact change, so readers can see how the page improved over time.

  1. 11 July 2026 · corrected by Melik Can Sariyer

    Kafka development began ~2010 at LinkedIn and was open-sourced in 2011

    Before

    Created at LinkedIn in 2011 to handle

    After

    Created at LinkedIn around 2010 and open-sourced in 2011 to handle

    Why: Kafka development began ~2010 at LinkedIn and was open-sourced in 2011. Verified fix already live in body; no leftover instances found in faq/excerpt/meta.

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Contributors In This Page

Melik Can SariyerExpert Contributor