--- source_url: "https://iceberglakehouse.com/iceberg/open-table-format-comparison/" title: "Open Table Format Comparison (Iceberg, Delta Lake, Hudi, Paimon) | Apache Iceberg Knowledge Base" mirrored_at: 2026-08-11T01:31:41.888Z host: iceberglakehouse.com cited_in_42a: true mirror_canonical: "https://index.42a.ai/iceberglakehouse.com/iceberg/open-table-format-comparison/index" --- > **Original source:** https://iceberglakehouse.com/iceberg/open-table-format-comparison/ Core Concepts Last updated: May 14, 2026 A comprehensive comparison of the four major open table formats: Apache Iceberg, Delta Lake, Apache Hudi, and Apache Paimon, across multi-engine support, streaming capabilities, catalog design, governance, and ideal use cases to guide lakehouse architecture decisions. open table format comparisoniceberg delta lake hudi paimonlakehouse table format guidebest open table formaticeberg vs delta vs hudi ## Open Table Format Comparison The **open table format** landscape has four primary contenders in 2025: **Apache Iceberg**, **Delta Lake**, **Apache Hudi**, and **Apache Paimon**. Each brings a distinct design philosophy, set of strengths, and primary use case. This page provides a comprehensive, vendor-neutral comparison to inform architectural decisions. ## The Four Formats at a Glance Format Origin Governance Primary Design Goal **Apache Iceberg** Netflix (2017) Apache Foundation Multi-engine interoperability **Delta Lake** Databricks (2019) Linux Foundation Reliable data lake on Spark **Apache Hudi** Uber (2016) Apache Foundation Streaming upserts + incrementals **Apache Paimon** Alibaba/Flink (2022) Apache Foundation Streaming lakehouse (LSM-tree) ## Core Architecture Architecture Iceberg Delta Lake Hudi Paimon Metadata format JSON + Avro manifests JSON transaction log Avro timeline Manifest + changelog Data format Parquet, ORC, Avro Parquet Parquet ORC, Parquet Snapshot model Immutable snapshots Log-based versions Timeline commits Snapshots + changelog Native storage Object storage Object storage Object / HDFS Object storage Index built-in Bloom filter (Puffin) Bloom filter, Z-order Bloom, HBase, Bucket LSM-based indexes ## Multi-Engine Support This is where the formats diverge most significantly: Engine Iceberg Delta Lake Hudi Paimon Apache Spark ✅ Full ✅ Native (best) ✅ Full ✅ Full Apache Flink ✅ Full ✅ (limited write) ✅ Full ✅ Native (best) Apache Trino ✅ Full ✅ Good ✅ Connector 🚧 Limited Dremio ✅ Native 🔄 Delta connector 🔄 Limited ❌ BigQuery ✅ BigLake ❌ ❌ ❌ Athena ✅ Full ✅ Full ✅ Full ❌ Snowflake ✅ Open Catalog ❌ ❌ ❌ DuckDB ✅ Extension ❌ ❌ ❌ PyIceberg ✅ Native ❌ ❌ ❌ StarRocks / Doris ✅ Full ✅ Full ✅ Full 🚧 Limited **Verdict**: Iceberg has the broadest multi-engine read/write support. Delta Lake has the best Spark/Databricks integration. Hudi is strong in Spark + Flink. Paimon is Flink-native with growing Spark support. ## Streaming and Real-Time Capability Iceberg Delta Lake Hudi Paimon Streaming reads ✅ Snapshot-based ✅ CDC stream ✅ Incremental pull ✅ Native Streaming writes ✅ Flink sink ✅ Spark Streaming, DLT ✅ Flink, Spark ✅ Flink native Native CDC 🔄 Via Flink 🔄 Via Spark ✅ Built-in ✅ Built-in Key-based upserts ✅ MoR EqDelete ✅ Delta merge ✅ Native (indexed) ✅ LSM-native Incremental query ✅ Snapshot diff ✅ CDF ✅ Native incremental ✅ Changelog **Hudi** and **Paimon** have the strongest native streaming and incremental semantics. **Iceberg** handles streaming well via Flink but doesn’t have as native a CDC story. **Delta Lake** provides CDC via Delta Change Data Feed (CDF). ## Catalog and Governance Catalog Iceberg Delta Lake Hudi Paimon Open catalog spec REST Catalog (standard) Proprietary (Unity) HMS / REST HMS / REST Credential vending ✅ Full (Polaris) 🔄 Unity only ❌ ❌ Multi-engine RBAC ✅ Polaris RBAC ✅ Unity RBAC ❌ ❌ Open-source catalog Apache Polaris, Nessie None (Unity proprietary) None None Cloud managed catalog S3 Tables, BigLake, Glue Unity (Databricks Cloud) Glue (limited) None yet **Iceberg** has the most mature open, multi-engine catalog ecosystem. **Delta Lake’s** Unity Catalog is powerful but proprietary to Databricks. ## Apache Paimon: The Emerging Contender **Apache Paimon** (graduated to top-level Apache project in 2024) is the newest entry, originally designed as the “Flink Table Store”: - **LSM-tree (Log-Structured Merge-tree) architecture**: Like a database, Paimon uses LSM trees for efficient key-based upserts and point lookups. - **Native Flink integration**: Best-in-class Flink write performance. - **Changelog mode**: Produces both data and change records simultaneously. - **Lookup joins**: Efficient streaming lookup joins against Paimon tables. Paimon is particularly well-suited for **streaming + real-time query** scenarios where you need both sub-second streaming writes and efficient point lookups. ## Decision Framework Requirement Recommended Format Maximum multi-engine portability Apache Iceberg All-in Databricks ecosystem Delta Lake High-frequency key-based upserts (Spark) Apache Hudi Flink-native streaming + lookups Apache Paimon AI analytics + semantic layer Apache Iceberg (Dremio) Open catalog governance Apache Iceberg (Polaris) Cloud-managed (AWS) Apache Iceberg (S3 Tables) Cloud-managed (GCP) Apache Iceberg (BigLake) ## The Industry Direction The industry has broadly converged on Apache Iceberg as the **interoperability standard**: - All cloud providers (AWS, GCP, Azure) have launched native managed Iceberg services. - Snowflake (competitor to Databricks) co-created Apache Polaris with Dremio. - Databricks itself added UniForm (Delta → Iceberg compatibility), acknowledging Iceberg’s ecosystem reach. - The Iceberg REST Catalog specification is the emerging de facto multi-engine catalog API. **For new lakehouse projects in 2025, Apache Iceberg is the default choice** for teams that want maximum optionality, open governance, and the broadest engine ecosystem. Other formats are viable in specific, well-defined contexts. ## 📚 Go Deeper on Apache Iceberg Alex Merced has authored three hands-on books covering Apache Iceberg, the Agentic Lakehouse, and modern data architecture. Pick up a copy to master the full ecosystem. ✉️ **Stay Current on Data & AI** Subscribe to Alex Merced's weekly newsletter — _Data, Lakehouse & AI_ — for deep dives, tutorials, and industry insights delivered straight to your inbox. [Subscribe for Free on Substack →](https://amdatalakehouse.substack.com/)