Apache Iceberg and Apache Hudi are both governed by the Apache Software Foundation, and both deliver ACID transactions and versioned history on top of Parquet files in object storage. Their architectural differences reflect different original problem statements: Iceberg was designed to solve Netflix's large-scale batch analytics reliability problems, while Hudi (originally Hoodie) was designed by Uber to handle the high-frequency, record-level upsert requirements of their ride-sharing operational pipelines.
Understanding these origins helps predict where each format naturally excels.
The Metadata Model Difference
Apache Iceberg uses a snapshot-based metadata hierarchy. Each write operation creates a new snapshot pointing to a manifest list that tracks all active data files along with column-level statistics. This structure makes query planning efficient for large-scale analytical workloads: the planner uses file statistics to prune entire data files without reading them. The trade-off is that the snapshot model is inherently write-oriented toward appends and replacements of data file sets rather than individual record mutations.
Apache Hudi uses a timeline-based architecture with advanced pluggable indexing. The timeline records all write operations (commits, compaction, cleaning) as a log of events. Hudi's indexes (Bloom filter index, HBase index, or simple file-based index) allow the engine to locate the specific data file(s) containing a given record key, which makes record-level upserts efficient without scanning all files. This indexing capability is why Hudi handles high-frequency CDC updates more efficiently than Iceberg for record-level operations.
Merge-on-Read and Copy-on-Write
Both formats support Merge-on-Read (MoR) and Copy-on-Write (CoW) write strategies. In CoW mode, every write rewrites the affected data files completely, producing files that are immediately optimized for reads but making writes expensive for small updates. In MoR mode, writes append to log files that are merged with base files at read time, making writes fast but adding read-time overhead until a compaction job merges the logs into base files.
Hudi has historically been stronger for high-frequency MoR workloads because its indexing makes the "find the record to update" step fast. Iceberg v2 introduced positional and equality delete files that provide MoR capability, closing much of this gap for workloads that do not require Hudi's specific indexing strategies.
Engine Support
Apache Iceberg has broader support across non-Spark query engines. Dremio, Trino, Snowflake, BigQuery, and Athena all support Iceberg natively. Hudi's primary integration is with Apache Spark; support in other engines exists but is less mature. For organizations that need to query tables from multiple engines (not just Spark), Iceberg is the safer choice.
When to Choose Each
- Choose Apache Iceberg for large-scale batch analytics, multi-engine environments, and workloads where write patterns are predominantly append-oriented or periodic bulk updates.
- Choose Apache Hudi for high-frequency streaming upsert workloads, complex CDC pipelines where individual records are frequently updated or deleted, and Spark-centric environments where Hudi's native Spark integrations can be used fully.
