Iceberg vs Delta vs Hudi: Open Table Formats Compared

Apache Iceberg, Delta Lake, and Apache Hudi are the three open table formats that turn Parquet files on object storage into ACID-compliant warehouse tables. Iceberg is the most engine-agnostic and is winning the 2026 standards war. Delta is the most polished on Databricks. Hudi is the best for high-frequency upserts and streaming.

This guide compares the three head to head — governance, performance, ecosystem, and where each is strongest — so you can pick the format for your lakehouse without running three pilots in parallel. The choice locks in two years of engineering investment; getting it right up front matters.

Why Table Formats Matter

Raw Parquet files on S3 are cheap but primitive. You cannot update a row, roll back a bad load, or query consistent snapshots during writes. Table formats add a metadata layer on top of the files that provides ACID transactions, time travel, schema evolution, and hidden partitioning — turning a file dump into a proper warehouse.

All three formats solve the same problem. They differ in governance (Iceberg is in Apache, Delta is LF under Linux Foundation, Hudi is Apache), ecosystem breadth, and which workload they optimize for. The differences matter less at small scale and a lot at petabyte scale.

Head-to-Head Comparison

Why Iceberg Is Winning 2026

Iceberg has become the default open table format because every major engine now reads and writes it: Snowflake, BigQuery, Databricks (via Uniform), Trino, Spark, Flink, DuckDB, and ClickHouse. The REST catalog spec lets any of them share the same tables, eliminating vendor lock-in. Netflix, Apple, and AWS bet on Iceberg and their scale forced the ecosystem to follow.

Snowflake's Polaris catalog and AWS S3 Tables both use Iceberg natively. If you are starting fresh in 2026, Iceberg is the safe pick unless you are deeply committed to Databricks. See apache iceberg explained for the full breakdown.

When Delta Lake Still Wins

Delta is still the right choice for teams on Databricks. The runtime is deeply optimized for Delta, Unity Catalog governs it natively, and features like deletion vectors and liquid clustering roll out there first. Databricks' Delta Uniform now provides Iceberg-compatible metadata so you can read Delta from Snowflake, closing the ecosystem gap that otherwise pushed teams toward Iceberg.

If 80 percent of your workload already runs on Databricks, there is no compelling reason to switch — Delta plus Uniform gets you interop without abandoning Databricks-native features.

When Hudi Still Wins

Hudi is the best choice when your workload is dominated by high-frequency upserts and streaming ingest. Its Merge-on-Read storage type amortizes updates across read time, so you can commit thousands of records per second without blowing out your compaction schedule. Uber built it for exactly that workload — billions of ride events with status updates applied in near real time.

Hudi's ecosystem is narrower than Iceberg or Delta outside of Spark and Flink, but for the specific workload of streaming CDC into a lakehouse, it is still the performance leader.

Catalog and Governance

Table format choice also shapes catalog strategy. Iceberg uses the REST catalog spec (Polaris, Unity, Nessie, AWS S3 Tables); Delta uses Unity Catalog (Databricks) or Delta Sharing; Hudi typically piggybacks on Hive Metastore or Glue. Catalog choice locks in governance features, so factor it into the decision.

REST catalogs are the 2026 winner because they decouple storage from compute. Any engine that speaks the REST spec can read and write the same tables with consistent access control. Unity Catalog is the most feature-rich but Databricks-centric; Polaris is Snowflake's vendor-neutral answer; Nessie adds Git-like branching for data versioning. None of them are strictly better — they match different governance styles.

Performance at Scale

Benchmarks published in 2024-2025 show all three formats are within 10-20 percent of each other on standard TPC-DS workloads. The real performance differences show up on workload edges: Hudi wins on streaming upserts at Uber scale, Delta wins on Databricks-tuned queries, Iceberg wins on multi-engine reads because every engine optimizes against it. Do not pick based on micro-benchmarks — pick based on your workload shape.

Maintenance overhead also matters at scale. All three formats require periodic compaction to keep small files under control and snapshot expiration to reclaim storage. Delta's predictive optimization in Databricks automates this; Iceberg requires scheduled maintenance jobs; Hudi has inline clustering. The operational cost is easy to underestimate during the initial evaluation.

Making the Decision

If multi-engine portability matters, pick Iceberg. If you live inside Databricks, pick Delta (and enable Uniform for cross-engine reads). If you need streaming upserts at Uber scale, pick Hudi. For most new projects in 2026, the answer is Iceberg — it has the broadest ecosystem and fewest vendor lock-in concerns.

Migration Between Formats

Once you pick a format, switching later is painful but possible. Iceberg-to-Delta and Delta-to-Iceberg migrations typically rewrite the data into the target format via CTAS; there are also in-place migration tools for simpler layouts. Hudi-to-Iceberg is the most common 2026 migration path because teams that picked Hudi for streaming upserts now want Iceberg's broader engine support. Plan for a full rewrite and test readers against the new tables in parallel before cutting over.

Agent-Managed Lakehouse

Data Workers' pipeline agents manage table format maintenance — compaction, vacuum, snapshot expiration — across Iceberg, Delta, and Hudi without human intervention. See autonomous data engineering or book a demo.

Iceberg, Delta, and Hudi all deliver ACID lakehouse tables on object storage. The standards war is mostly over — Iceberg has the ecosystem — but Delta still wins inside Databricks and Hudi still wins for streaming upserts. Pick based on your engine mix, not the marketing.