Iceberg is the champion of engine-neutral table formats. Hudi is the veteran winner for high-scale, low-latency upserts and incremental processing.
Introduction
### The Foundation of the Lakehouse
To build a Data Lakehouse, you need a **Table Format** to manage files in S3. **Apache Iceberg** and **Apache Hudi** (Hadoop Upserts Deletes and Incrementals) are the two battle-hardened open standards.
**Apache Iceberg** (from Netflix) was built for massive datasets and engine neutrality. It focuses on correctness and preventing the problems of Hive (like slow metadata listing). It's winning the adoption war with support from Snowflake and AWS.
**Apache Hudi** (from Uber) was built for a specific, difficult problem: high-volume incremental updates (upserts). It offers unique features like 'Merge on Read' (MoR) and 'Copy on Write' (CoW) to balance write and read performance for streaming data.
Feature Comparison
Feature
Apache Iceberg
Apache Hudi
Winner
Core Focus
Reliability & Engine Independence
High-freq Upserts & Incremental Processing
Apache Hudi
Integration
Trino, Snowflake, Spark, Athena
Spark, Flink, Presto
Apache Iceberg
Schema Evolution
Full (Add, Drop, Rename, Reorder)
Excellent (but some engines vary)
Apache Iceberg
Complexity
Low (Simple metadata approach)
High (Many knobs for MoR/CoW)
Apache Iceberg
Query Speed
Excellent for analytical scans
Excellent for incremental/point queries
Tie
✅ Apache Iceberg Pros
The standard for 'Open Data Architecture'
Hidden partitioning—users don't need to know how data is stored
Snapshot isolation ensures fast, correct time travel
Massive ecosystem momentum in 2024-2025
⚠️ Apache Iceberg Cons
Historically slower for high-frequency row-level updates
Partition evolution can sometimes confuse older engines
Implementation varies slightly between cloud providers
✅ Apache Hudi Pros
The best tool for handling CDC (Change Data Capture) feeds
Native support for incremental processing (only process new data)
Excellent concurrency control (Multi-Writer)
Best for super-low latency 'fresh' data
⚠️ Apache Hudi Cons
Steep learning curve due to configurational complexity
Historically perceived as 'Spark-heavy'
Metadata management can become heavy for millions of small files
Final Verdict
### Verdict
**Choose Apache Iceberg if:**
* You want a future-proof, engine-agnostic data lakehouse.
* You use multiple query engines (Snowflake, Trino, Athena).
* Your primary use case is large-scale analytical scanning.
**Choose Apache Hudi if:**
* You are building a real-time CDC pipeline from a database.
* You need to process data incrementally (The 'Incremental Data Lake' vision).
* You have high-frequency updates and deletes in your data lake.