Hudi vs Delta Lake vs Iceberg: Deep Technical Comparison of Data Lake Table Formats

Introduction

With the rise of Lakehouse architectures, interest in the three open‑source table formats—Apache Hudi, Delta Lake, and Apache Iceberg—has grown. Most existing comparisons treat them merely as append‑only file formats, overlooking capabilities needed for heavy update workloads that require continuous table management.

Feature Comparison

The community around Hudi invests heavily in building a comprehensive platform on top of lake storage formats, offering a rich toolbox for developing and managing data‑lake deployments.

Feature Highlights

Beyond basic functionality, the article examines several differentiating features in simple English, focusing on real use cases and benefits.

Incremental Pipeline

Hudi introduces an "incremental pipeline" paradigm that tracks all changes (inserts, updates, deletes) and exposes them as a change stream. Record‑level indexing enables efficient incremental processing, reducing recomputation and latency compared to traditional batch ETL. Databricks later added a similar Change Data Feed in Delta Lake 2.0, while Iceberg only supports incremental appends without updates or deletes.

Concurrency Control

All three formats support optimistic concurrency control (OCC). In OCC, writers check for overlapping files and retry on conflict. Delta Lake implements OCC at the JVM level, which can be limiting for multi‑cluster workloads. Hudi’s concurrency control is finer‑grained than file‑level approaches, optimized for many small updates/deletes, dramatically reducing conflict likelihood in real‑world scenarios.

Merge on Read

While all three store data in Parquet files and use copy‑on‑write (CoW) for updates, Hudi also supports Merge‑on‑Read (MoR), combining columnar Parquet files with row‑based Avro log files. Updates are first written to log files and later compacted into Parquet, balancing query performance with reduced write amplification. This enables near‑real‑time streaming workloads to benefit from row‑oriented formats while batch jobs use vectorized columnar formats.

Partition Evolution

Iceberg emphasizes hidden partitioning to allow partition evolution without rewriting existing data. Hudi takes a different approach, allowing coarse‑grained or no partitioning and fine‑grained clustering within partitions, which can evolve asynchronously without data rewrites, comparable to Snowflake's micro‑partitioning.

Multi‑Mode Index

Hudi introduces a high‑performance multi‑mode indexing subsystem that builds asynchronous indexes (e.g., Bloom, Hash, Bitmap, R‑tree) without impacting write latency. The index files stored in Hudi metadata tables improve point‑lookup performance by 10‑100× and overall query performance by 10‑30× on real‑world workloads.

Ingestion Tools

Hudi provides a robust ingestion utility called DeltaStreamer, a standalone tool that incrementally ingests changes from sources such as DFS, Kafka, CDC logs, S3 events, and JDBC. Iceberg lacks a managed ingestion tool, and Delta Lake’s Autoloader remains a proprietary Databricks feature.

User Cases: Community Examples

Various organizations have evaluated the three formats and chosen Hudi for its openness, global index support, and customizable storage logic. Examples include Amazon's package delivery system handling PB‑scale data with real‑time inserts/updates/deletes, ByteDance/​TikTok processing 400 PB+ tables with high‑throughput and complex schemas, Walmart managing thousands of tables across hundreds of sources, Robinhood requiring low‑latency data‑lake refreshes, and Zendesk using Hudi with AWS DMS and EMR for CDC pipelines.

Conclusion

When workloads go beyond simple append‑only inserts, Apache Hudi typically offers technical advantages in performance and feature set. Its support for frequent updates, high concurrency, and low end‑to‑end pipeline latency makes it a leading choice for modern Lakehouse deployments.