Lakehouse Table Formats in 2026: Iceberg, Delta Lake, Hudi, Paimon, and DuckLake

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<p><em>There are two ways to run a data lake. You can manage every table by hand — writing Airflow DAGs, scheduling Spark compaction jobs, building monitoring dashboards, and

<p>Three things ran through the dev lists this week. Projects kept redrawing their own boundaries, with the Iceberg Rust DataFusion integration voted out of Iceberg and into

<p>El <strong>13 de abril de 2026</strong>, DuckDB Labs liberó la versión <strong>1.0 de <a href="https://ducklake.select/" rel="noopener noreferrer">DuckLake</a></strong>, un

<p><em>Dipankar Mazumdar is the Director of Developer Relations at Cloudera, leading global developer initiatives across lakehouse architecture and AI. He previously held advocacy

<h1> Query one Apache Iceberg table from Trino, Spark, and DuckDB </h1> <p>Iceberg is useful because it lets multiple engines read the same table without copying data into

<p>Here is a fact that surprises almost everyone the first time they hear it: in a data lakehouse, deleting a single row is one of the hardest things you can do.</p> <p>Inserting

<p>Here is one of the strangest and most consequential plot twists in the history of data infrastructure: over the past decade, the analytics industry deliberately moved its data