Best of Apache Iceberg — 2025

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   Article

   Supabase·24w

   Introducing iceberg-js: A JavaScript Client for Apache Iceberg

   Supabase released iceberg-js, an open-source JavaScript/TypeScript client for Apache Iceberg REST Catalog API. The library provides type-safe catalog management for namespaces and tables, works across all JavaScript environments, and is intentionally minimal—it handles only catalog operations, not data reads/writes or query execution. Built to power Supabase's Analytics Buckets feature, it's vendor-agnostic, uses native fetch API, and supports multiple authentication methods. The MIT-licensed library is available on GitHub and npm.

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2. 2
   Article

   dltHub·1y

   Why Iceberg + Python is the Future of Open Data Lakes

   Apache Iceberg, combined with Python, is revolutionizing data lakes by addressing issues like ACID transactions, schema evolution, and the need for open, vendor-agnostic solutions. Netflix, Apple, and Adobe are early adopters, and the technology is supported by Trino, Snowflake, and BigQuery. Iceberg's open ecosystem and composability allow seamless integration without overhauling existing systems. This approach is crucial for AI and machine learning, providing efficient and structured data for scalable and cost-effective workloads.

   54
3. 3
   Article

   Decube·1y

   S3 Tables with Apache Iceberg: Manage Data at Scale

   Discover how integrating S3 Tables with Apache Iceberg can enhance your data management strategy, providing reliable and scalable systems. Learn about key components like the Iceberg catalog and table, and understand the benefits of using Apache Iceberg with Amazon S3, including improved data scalability, reliability, and cost-efficiency. Explore best practices for managing large-scale deployments, optimizing resources, and ensuring secure data governance.

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4. 4
   Article

   Google Open Source Blog·35w

   Apache Iceberg 1.10: Maturing the V3 spec, the REST API and Google contributions

   Apache Iceberg 1.10.0 introduces major improvements including full Spark 4.0 and Flink 2.0 compatibility, production-ready Deletion Vectors for faster row-level updates, and a hardened REST Catalog API. The release matures the V3 specification with features like row lineage and variant types. Google contributed native BigQuery Metastore Catalog support and Google AuthManager, enabling seamless integration with BigLake-managed tables through open REST protocols.

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5. 5
   Article

   dltHub·49w

   Building Engine-Agnostic Data Stacks

   Modern data teams often use multiple engines like Spark, DuckDB, and Snowflake, but struggle with data portability and code reusability across platforms. Apache Iceberg solves the storage problem by enabling safe data sharing between engines through ACID transactions and multi-engine coordination. Tools like Ibis complement this by providing engine-agnostic analytical code that runs on any supported backend without modification. Together, these technologies create truly portable data stacks where both data and business logic are decoupled from specific compute engines, reducing vendor lock-in and integration overhead.

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