Iceberg vs Delta Lake vs Hudi: Open Table Formats Compared

Why Open Table Formats Matter

Traditional data warehouses (Snowflake, BigQuery, Redshift) store data in proprietary formats. This creates vendor lock-in: your data lives in the vendor's format and can only be queried by the vendor's engine.

Open table formats separate storage from compute. Your data lives in Parquet files in cloud object storage (S3, GCS, Azure Blob). The table format provides metadata layers on top: schema, partitioning, transaction logs, and file organization. Any compute engine that supports the table format can query the data.

This matters for three reasons: portability (you can change compute engines without moving data), cost (compute and storage scale independently), and interoperability (multiple tools can operate on the same data without duplication).

Iceberg, Delta Lake, and Hudi are the three leading open table formats. All three provide ACID transactions, schema evolution, time travel queries, and efficient file compaction. The differences are in ecosystem support, performance characteristics, and the corporate sponsors behind each.

Apache Iceberg

Iceberg was created at Netflix and is now governed by the Apache Software Foundation. It has the broadest engine support in 2026: Snowflake, BigQuery, Databricks, DuckDB, Trino, Spark, Flink, and many others have native Iceberg read and write support.

The critical moment for Iceberg came when Snowflake, Databricks, and AWS all committed to native Iceberg support within the same year. This signaled that Iceberg was becoming the de facto standard for open table formats.

Iceberg's design philosophy emphasizes engine-agnostic access. The metadata layer is stored in Parquet and JSON files that any engine can read. This contrasts with Delta Lake, which historically privileged Spark-based access.

Iceberg catalogs (the layer that tracks which tables exist and where their metadata lives) include AWS Glue, Nessie, REST catalogs, and vendor-specific implementations. The choice of catalog affects interoperability.

Performance is strong for analytic queries. Iceberg's manifest file structure allows efficient pruning and partition filtering. Write performance is competitive with Delta Lake and Hudi.

The main trade-off: Iceberg's governance as an Apache project means decisions happen by community consensus, which is slower than corporate-backed development. For teams that value governance and stability over rapid feature development, this is a positive.

Delta Lake

Delta Lake was created at Databricks and is closely tied to the Databricks ecosystem. It provides ACID transactions, schema enforcement, time travel, and optimized Spark-based access.

Delta Lake's strength is deep integration with Databricks' compute engine. Photon, Databricks' native query engine, is optimized for Delta Lake and delivers the best performance for Delta tables. Teams already using Databricks get the best experience with Delta Lake.

Delta Lake open-sourced its specification in 2022, which improved interoperability with non-Databricks engines. Current support includes Spark (native), Trino, Presto, Flink, and various read-only clients. Write support outside Databricks has improved but is still less mature than Iceberg.

Delta's schema evolution and schema enforcement features are mature. Delta Sharing provides a protocol for sharing Delta tables across organizations without data duplication.

The main trade-off: Delta Lake is governance-tied to Databricks. Databricks drives the roadmap, and while the format is open source, the practical best experience requires Databricks' compute. Teams that want multi-vendor portability often prefer Iceberg.

Apache Hudi

Hudi was created at Uber and is now an Apache project. It targets a different use case than Iceberg and Delta: incremental data processing and record-level updates at scale.

Hudi's core feature is its support for upsert and delete operations at the record level. This matters for use cases like change data capture (CDC) from operational databases, GDPR deletion requirements, and late-arriving data. Iceberg and Delta support these operations too, but Hudi's architecture is optimized for them.

Hudi supports two storage types: Copy on Write (COW) and Merge on Read (MOR). COW produces full file rewrites on updates (slower writes, faster reads). MOR writes deltas and merges at read time (faster writes, slower reads). The choice depends on workload.

Hudi has strong Spark and Flink integration but smaller adoption outside these engines. Compared to Iceberg's broader ecosystem, Hudi's reach is narrower.

Hudi is the right choice for CDC-heavy workloads, high-frequency updates, or use cases where row-level operations dominate. For traditional analytic workloads, Iceberg and Delta are usually simpler choices.

Direct Comparison Matrix

Engine support: Iceberg has the broadest native support across Snowflake, BigQuery, Databricks, DuckDB, Trino, Spark, and Flink. Delta has deep Databricks integration and improving support elsewhere. Hudi has strong Spark and Flink support with less adoption in other engines.

Performance for analytic queries: Iceberg and Delta are comparable on typical analytic workloads. Delta may have a slight edge when running on Databricks' Photon engine. Iceberg has broader performance consistency across engines.

Update and delete operations: Hudi is optimized for these use cases. Iceberg and Delta support them but Hudi's architecture is purpose-built. For CDC and record-level update workloads, Hudi is the clearest winner.

Governance and neutrality: Iceberg is vendor-neutral under Apache. Delta is governed by Databricks. Hudi is under Apache but primarily used by Spark-centric teams.

Writer concurrency: Iceberg supports optimistic concurrency across engines. Delta supports it within Databricks clusters. Hudi supports it for specific write patterns.

Maturity: Delta has been in production longest at Databricks scale. Iceberg is battle-tested at Netflix, Apple, and LinkedIn. Hudi is proven at Uber and other high-volume CDC workloads.

For most new builds in 2026, the practical answer is Iceberg unless you have a specific reason to pick one of the others.

When to Pick Each

Pick Iceberg when: you want maximum vendor portability, you plan to use multiple compute engines, you prioritize a neutral governance model, or you're building fresh on cloud infrastructure without legacy commitments. Iceberg is the default choice for new builds in 2026.

Pick Delta Lake when: you're committed to Databricks as your primary compute engine, you need Delta Sharing for cross-organization data sharing, or you have existing Delta tables and switching costs are high. Delta inside Databricks is still the best-performing combination.

Pick Hudi when: your workload is dominated by upserts, deletes, and CDC from operational databases; you need record-level operations at scale; or you're on a Spark-heavy stack where Hudi's design provides specific advantages.

For most teams starting fresh, Iceberg is the safer long-term choice because of broad engine support and neutral governance. The ecosystem momentum in 2026 is clearly behind Iceberg.

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