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Unlocking Faster Iceberg Queries: The Writer Optimizations You’re Missing
Apache Iceberg query performance is often limited long before a query engine gets involved. In a joint post with Firebolt, we break down why writer configuration, file layout, and continuous table maintenance matter most.
The post walks through practical best practices for writing and maintaining Iceberg tables, and shows how combining optimized writers with continuous optimization from Ryft enables consistently fast queries across Firebolt, Athena, Trino, Spark SQL, and more.
Read the full article on Firebolt’s blog: Unlocking Faster Iceberg Queries: The Writer Optimizations You’re Missing
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Unlocking Faster Iceberg Queries: The Writer Optimizations You’re Missing
Apache Iceberg query performance is often limited long before a query engine gets involved. In a joint post with Firebolt, we break down why writer configuration, file layout, and continuous table maintenance matter most.
Apache Iceberg V3: Is It Ready?
Apache Iceberg V3 is a huge step forward for the lakehouse ecosystem. The V3 specification was finalized and ratified earlier this year, bringing several long-awaited capabilities into the core of the format: efficient row-level deletes, built-in row lineage, better handling of semi-structured data, and the beginnings of native encryption. This post breaks down the major features, the current state of implementation, and what this means for real adoption.
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