Iceberg vs Delta Lake vs Hudi: Choosing the Right Table Format

The Landscape: Three open source table formats dominate the data lakehouse space: Apache Iceberg, Delta Lake, and Apache Hudi. All three bring ACID transactions and schema evolution to data lakes, but they differ in design philosophy, engine support, and optimization focus. The choice depends on your ecosystem, workload patterns, and operational preferences. Iceberg Characteristics: Iceberg emphasizes clean separation between format specification and compute engines. The format is well documented, engine neutral, and supports native reads and writes from Spark, Flink, Trino, Presto, Hive, Impala, and more. Metadata is based on immutable snapshots with explicit manifest files. Hidden partitioning allows partition evolution without rewriting data. Catalog abstraction supports Hive Metastore, AWS Glue, Nessie, REST catalogs, and custom implementations. Operationally, you run separate compaction and maintenance jobs. Performance scales well to petabytes and millions of files when properly tuned. Companies like Netflix, Apple, and Adobe use Iceberg for multi petabyte analytical workloads with dozens of different query engines accessing the same tables. Delta Lake Characteristics: Delta Lake integrates tightly with Apache Spark and the Databricks ecosystem. The transaction log uses JSON files that list file additions and removals per commit. Originally Databricks proprietary, it opened and now has a protocol spec, but Spark remains the primary engine with best support. Other engines can read Delta tables through connectors, but write support varies. Delta excels in environments where Spark is the dominant compute engine. It has mature tooling for streaming upserts, time travel, and schema enforcement within the Spark ecosystem. Databricks provides managed Delta Lake services with automatic optimization. If you are all in on Databricks or Spark centric, Delta can be simpler to operate than Iceberg. Hudi Characteristics: Apache Hudi optimizes for streaming upsert workloads and incremental processing. It supports two table types: Copy on Write (COW) for read heavy workloads and Merge on Read (MOR) for write heavy workloads with faster ingestion. Hudi tracks record level changes efficiently, making it strong for Change Data Capture (CDC) use cases. However, adoption is narrower than Iceberg or Delta, and engine support is more limited. Decision Framework: Choose Iceberg when you need strong multi engine interoperability. If your data is queried by Spark batch jobs, Flink streaming, Trino interactive queries, and AWS Athena, Iceberg provides the most consistent experience across all of them. It is also the best choice if you expect to evolve partitioning schemes or need advanced catalog features. Choose Delta Lake when you are heavily invested in the Spark or Databricks ecosystem and value tight integration over broad engine support. If 90% of your workload runs on Databricks and you want managed services handling optimization, Delta simplifies operations. Be aware that migrating away later may be harder due to ecosystem lock in. Choose Hudi when your primary use case is high frequency upserts from streaming pipelines, especially CDC scenarios. If you are ingesting database change logs and need efficient record level updates at scale, Hudi's MOR tables can outperform Iceberg or Delta. However, expect more operational complexity and narrower tooling support. Migration and Compatibility: All three support time travel and schema evolution, but the details differ. Iceberg uses column IDs for schema evolution, making it more flexible. Delta uses column names and positions, which can complicate some evolution scenarios. Hudi requires careful configuration of keys and table types upfront. Performance at scale depends heavily on tuning. Iceberg and Delta both handle petabytes well, but Iceberg has more production deployments at that scale across diverse engines. Hudi's MOR tables can have higher query latency because they require merging base files with log files at read time, though COW tables avoid this.