Managed Iceberg Data Lakes: A Guide

<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

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<p>It was Monday morning. Our team was sitting through a technical architecture meeting. I could see Sarah's face — confused, frustrated, checking her watch.</p> <p>When the

<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>The table format war is over, and the table formats are not. Both halves of that sentence are true, both matter, and the tension between them is exactly why this article needs

<p>PostgreSQL → DuckDB → SQL Engine → Parquet →And beyond ....</p> <p><strong>A Local-First Analytics Pipeline</strong></p> <p>Data teams often spend more time operating

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