Apache Iceberg vs Delta Lake vs Apache Hudi: Table Format Comparison (2026)

The three leading open table formats for data lakehouses are Apache Iceberg, Delta Lake, and Apache Hudi. In 2026, Iceberg has emerged as the industry standard due to its vendor-neutral governance, partition evolution, and the broadest multi-engine support (Spark, Flink, Trino, Snowflake, BigQuery, DuckDB). Hudi leads for pure streaming/CDC ingestion workloads. Delta Lake remains strong in Databricks-centric environments.

This guide compares all three formats across architecture, performance, streaming support, and ecosystem.

Architecture Comparison

Apache Iceberg

Iceberg uses a hierarchical metadata architecture: a central metadata.json file points to manifest lists, which point to manifest files, which track individual data files. Each manifest stores column-level statistics for efficient query planning.

• Partition evolution: Change partitioning schemes without rewriting data (unique to Iceberg)
• Hidden partitioning: Queries don't need to reference partition columns
• Snapshot isolation: Row-level concurrent reads and writes
• Write mode: Primarily Copy-on-Write, with emerging Merge-on-Read support

Delta Lake

Delta Lake uses a sequential transaction log (JSON files in _delta_log/) to track all changes to a table.

• Transaction log: Append-only log ensuring ACID compliance
• Write mode: Primarily Copy-on-Write with deletion vectors
• Partitioning: Standard Hive-style partitioning (less flexible than Iceberg)
• Time travel: Via transaction log commit history

Apache Hudi

Hudi uses a timeline-based architecture with directories for metadata, organized by timestamp and operation type.

• Two table types: Copy-on-Write (CoW) and Merge-on-Read (MoR)
• MoR advantage: Stores only changed columns in delta logs, reducing write amplification
• Record-level indexing: Deterministic key-based lookups for efficient upserts
• DeltaStreamer: Purpose-built streaming ingestion tool

Feature Comparison

Performance Characteristics

Write Performance

Hudi leads for streaming writes. Hudi's Merge-on-Read mode writes only changed columns to delta log files (Avro format), minimizing write amplification. For high-frequency upsert workloads, this is significantly more efficient than Iceberg or Delta Lake's full-row rewrite approach.

Iceberg and Delta Lake are comparable for append-only writes. Both write Parquet files with similar overhead.

Read Performance

Iceberg leads for analytical reads. Iceberg's manifest files store column-level statistics, enabling efficient predicate pushdown and partition pruning without listing files in object storage. This scales better than Delta Lake's transaction log scanning or Hudi's metadata indexing for large tables.

Hudi provides 10-30x improvements for point lookups with its record-level indexing subsystem, but analytical scan performance trails Iceberg.

Real-World Streaming Benchmark

In real-world streaming ingestion tests with concurrent compaction:

• Hudi: Handled demanding workloads successfully
• Delta Lake: Failed background compaction during ingestion (OCC conflicts)
• Iceberg: Experienced write failures under concurrent load

These results highlight that benchmark performance and production behavior can differ significantly.

Streaming Support

Iceberg + RisingWave (SQL-Based Streaming)

RisingWave provides the simplest streaming ingestion path to Iceberg:

CREATE SINK orders_to_iceberg AS
SELECT * FROM orders_stream
WITH (
  connector = 'iceberg',
  type = 'append-only',
  catalog.type = 'rest',
  catalog.uri = 'http://rest-catalog:8181',
  database.name = 'analytics',
  table.name = 'orders'
);

Advantages: Pure SQL, automatic compaction, Rust-based performance, supports both MoR and CoW write modes, 5 catalog types supported.

Iceberg + Flink (Dynamic Sink)

Flink 2.0's Dynamic Iceberg Sink writes streaming Kafka data into multiple Iceberg tables with automatic schema evolution.

Delta Lake + Spark Structured Streaming

Native integration with Spark, micro-batch latency. Strong for Databricks users.

Hudi DeltaStreamer

Purpose-built for streaming ingestion from Kafka and database changelogs. Most mature streaming support among all three formats.

Ecosystem and Community

Apache Iceberg

• Momentum: Nearly double the unique PR creators compared to Delta Lake
• Adoption: Netflix, Apple, LinkedIn, Google contributing
• Engine support: Broadest — Spark, Flink, Trino, Snowflake, BigQuery, Athena, DuckDB, Dremio
• Governance: Apache Software Foundation (truly vendor-neutral)
• 2025 milestone: Iceberg 1.10 with V3 spec maturity and REST API standardization

Delta Lake

• Backing: Databricks (primary contributor)
• Ecosystem: Strong in Spark/Databricks environments
• Governance: Linux Foundation
• Recent: Delta Lake 3.0 "Universal Format" offering Iceberg/Hudi compatibility

Apache Hudi

• Community: 500+ GitHub contributors, 5,000+ Slack members
• Strengths: Streaming/CDC focus, DeltaStreamer production-proven
• Recent: Hudi 1.1 with 800+ commits, native Iceberg format support added
• Governance: Apache Software Foundation

How to Choose

Choose Apache Iceberg if:

• You want the broadest multi-engine support (Snowflake, BigQuery, Trino, DuckDB)
• You need partition evolution (change partitioning without rewriting data)
• Vendor neutrality matters — no single vendor controls the project
• You're building a streaming lakehouse with RisingWave or Flink

Choose Delta Lake if:

• You're primarily using Databricks and Spark
• You want the simplest Spark integration
• Delta's Universal Format provides sufficient cross-engine compatibility

Choose Apache Hudi if:

• Your primary workload is streaming CDC ingestion with high-frequency upserts
• You need partial column updates for wide schemas
• You need the most mature streaming ingestion tooling (DeltaStreamer)

The Convergence Trend

In 2026, the boundaries between formats are blurring:

• Delta Lake's Universal Format writes Iceberg-compatible metadata
• Hudi added native Iceberg format support
• All three are converging on similar feature sets

The practical winner is Iceberg — not because it's technically superior in every dimension, but because it has the broadest engine support and the strongest vendor-neutral governance. When Snowflake, BigQuery, Databricks, Trino, and DuckDB all support Iceberg natively, the interoperability advantage is decisive.

Frequently Asked Questions

Which table format is best for streaming data?

For pure streaming ingestion with high-frequency upserts, Apache Hudi has the most mature tooling. For unified streaming and analytics with the broadest engine support, Apache Iceberg is the best choice — especially when combined with RisingWave or Flink for the streaming layer. Delta Lake works well for Spark Structured Streaming in Databricks environments.

Is Apache Iceberg replacing Delta Lake and Hudi?

Iceberg is gaining the most momentum and broadest adoption, but it's not replacing the others entirely. Delta Lake remains strong in Databricks environments, and Hudi maintains advantages for streaming-heavy CDC workloads. The trend is toward convergence — Delta Lake's Universal Format and Hudi's native Iceberg support suggest Iceberg is becoming the common denominator.

Can RisingWave write to all three table formats?

RisingWave has native Iceberg sink support (most complete, with automatic compaction and 5 catalog types) and Delta Lake sink support. Apache Hudi is not directly supported as a RisingWave sink. Iceberg is the recommended choice for RisingWave-based streaming lakehouses.

What is partition evolution in Iceberg?

Partition evolution allows you to change the partitioning scheme of an Iceberg table without rewriting existing data. For example, you can switch from daily to hourly partitioning as data volume grows. Only new data is written with the new scheme; old data retains its original partitioning. This is unique to Iceberg — Delta Lake and Hudi require full table rewrites to change partitioning.