<ul><li>Traditional data lake formats lack native support for upserts, deletes, and incremental processing, leading to inefficient pipelines and “data swamp” conditions.</li><li>Apache Hudi is a practical solution to these challenges, providing features like Copy-on-Write and Merge-on-Read storage, incremental pulls, and transactional capabilities in a Spark ecosystem.</li><li>Integrating Hudi enables the creation of efficient, real-time, and mutation-friendly data pipelines, simplifying ingestion and querying at scale while reducing processing overhead.</li><li>Apache Hudi bridges the gap between the flexibility of data lakes and the reliability of data warehouses, bringing structure and control to messy data workflows.</li></ul>

From Swamp to Stream: How Apache Hudi Transforms the Modern Data Lake

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