Iceberg vs Delta Lake vs Hudi vs Lance: Table Formats for AI Workloads
Last updated: 2026-06-09 · By Vector Search Engineering, Zilliz
Quick answer. In the iceberg vs delta lake comparison, both are open table formats that add ACID transactions, schema evolution, and time travel to data files on object storage — Apache Iceberg is engine-neutral and Apache-governed, while Delta Lake originated at Databricks. Apache Hudi adds a third option tuned for streaming upserts and incremental processing. For AI workloads a newer class matters: Lance is a columnar format built for vectors and multimodal data, and Vortex pushes columnar compression further. The deciding factor is your workload — engine-neutral analytics, streaming CDC, or AI retrieval.
What is each format
Apache Iceberg. An open table format created at Netflix and now governed by the Apache Software Foundation. It tracks table state through snapshots and manifest files, supports hidden partitioning and full schema evolution, and is deliberately engine-neutral — Spark, Trino, Flink, and others read the same table.
Delta Lake. An open table format that originated at Databricks and is now hosted by the Linux Foundation. It records every change in a transaction log (_delta_log) to give ACID guarantees, time travel, and schema enforcement, with the deepest integration on Apache Spark.
Apache Hudi. An open table format created at Uber for incremental and streaming workloads. It offers copy-on-write and merge-on-read storage, a timeline of commits, and first-class upserts and deletes — strong where change-data-capture (CDC) — often streamed through Kafka — and low-latency ingestion matter.
Lance. A modern columnar format created by the LanceDB team, implemented in Rust for machine-learning and multimodal data. It is built for fast random access, zero-copy versioning, and native vector indexing — designed around the access patterns of embeddings, not just analytic scans.
Key Differences
The first three are table formats for analytics; the difference shows up in how they handle change and which engines they favor. Lance sits in a different category, built for AI access patterns.
| Dimension | Apache Iceberg | Delta Lake | Apache Hudi | Lance |
|---|---|---|---|---|
| Origin / governance | Netflix → Apache | Databricks → Linux Foundation | Uber → Apache | Open-source, Rust |
| Change model | Snapshots + manifests | Transaction log (_delta_log) | Copy-on-write / merge-on-read | Versioned columnar |
| Strongest at | Engine-neutral analytics | Batch + streaming on Spark | Incremental upserts / CDC | ML / vector / multimodal |
| ACID | Yes — snapshot isolation | Yes — log-based | Yes — timeline | Yes — versioned |
| Engine ecosystem | Spark, Trino, Flink, broad | Spark-first, broadening | Spark, Flink | ML tools, Arrow |
| Native vector index | No | No | No | Yes |
The analytic three overlap more than the debates suggest. All add ACID, schema evolution, and time travel over open files such as Apache Parquet on object storage; the real choices are governance and workload. Iceberg leans engine-neutral and avoids vendor lock-in; Delta Lake is deepest on Spark and the Databricks ecosystem; Hudi wins when you need frequent upserts, deletes, and streaming ingestion rather than mostly-append analytics.
What none of the three ships is a native vector type or an approximate-nearest-neighbor (ANN) index — they were built for tabular scans, so similarity search over an embedding column stored in them falls back to a brute-force scan unless you copy the vectors into a separate store like Pinecone, Weaviate, or Qdrant. That is the gap Lance and Vortex address: both move far less data per read than the analytic formats. In Zilliz's own benchmark (3M rows, 128-dim, 256 concurrent readers on S3), Lance and Vortex cut per-read S3 traffic sharply versus Parquet — Lance about 1,500x less, Vortex about 135x less — and Vortex delivered roughly 2.4x Parquet's full-scan throughput. Lance adds native vector indexing built for embedding access patterns; Vortex pushes columnar compression and random reads further. For an AI pipeline, that distinction matters more than the Iceberg-vs-Delta question.
When to Use Each
Choose Apache Iceberg when you want an engine-neutral analytic table that avoids lock-in and is read the same way by Spark, Trino, and Flink — large, mostly-append analytic datasets with evolving schemas.
Choose Delta Lake when your stack is centered on Apache Spark or Databricks and you want the tightest integration for combined batch and streaming pipelines, with a mature transaction log.
Choose Apache Hudi when ingestion is upsert-heavy or streaming — CDC from operational databases, frequent record-level updates and deletes, and low-latency freshness rather than batch appends.
Choose Lance (or watch Vortex) when the workload is AI: storing embeddings alongside source data, fast random access for training and retrieval, and native vector indexing that the analytic formats don't provide.
How Vector Lakebase Approaches This
These formats don't have to be either-or. Zilliz Vector Lakebase treats them as sources through its External Data Lake Search capability: External Collections register an embedding column over data that already lives in Iceberg, Delta Lake, Parquet, or Lance files and build a vector index in place, so similarity search runs on the lake table without copying it into a separate store. Lakebase also uses Vortex — an open columnar format hosted by the Linux Foundation — as its lake-native layer for vector data, and builds on the Milvus serving engine, so the index is that engine's, attached to the lake table rather than run as a separate product. The result is that the table-format choice above stays an analytics decision; the vector index becomes a property of the same table rather than a second system to sync.
Frequently asked questions
What is the main difference between Iceberg and Delta Lake? Both are open table formats that bring ACID transactions, schema evolution, and time travel to files on object storage. The main differences are governance and ecosystem: Apache Iceberg is Apache-governed and deliberately engine-neutral, read equally by Spark, Trino, and Flink, while Delta Lake originated at Databricks and integrates most deeply with Apache Spark. Feature sets have converged; the choice is usually about lock-in and existing tooling.
Is Apache Hudi better than Iceberg or Delta Lake? Not better or worse — different. Hudi was built at Uber for incremental and streaming workloads, with copy-on-write and merge-on-read storage and first-class upserts and deletes. If your pipeline is CDC-driven or upsert-heavy and needs low-latency freshness, Hudi fits well. For mostly-append analytic tables, Iceberg or Delta Lake are the more common picks.
Can these table formats store vector embeddings? They can store an embedding as an array column, but Iceberg, Delta Lake, and Hudi have no native vector type or ANN index, so similarity search over them is a brute-force scan. Lance is the exception, with native vector indexing. To serve real-time vector search over Iceberg or Delta data, you build an index over the embedding column rather than relying on the format itself.
What is the difference between a table format and a file format? A file format (Apache Parquet, ORC, Avro) defines how individual data files are encoded. A table format (Iceberg, Delta Lake, Hudi) sits on top, organizing many files into a single transactional table with a metadata layer for schema, snapshots, and history. Lance blurs the line — it is a columnar file format with table-like versioning built in.
Related reading
- what is Apache Iceberg — the table-format primer
- Parquet vs ORC vs Avro for AI — the file-format layer underneath
- what is the Vortex file format — the AI-native columnar format
- how to add vector search to Apache Iceberg tables — the practical how-to
Bottom line. Iceberg, Delta Lake, and Hudi are open table formats that converge on ACID and schema evolution; pick by governance and workload — engine-neutral analytics, Spark-centric pipelines, or streaming upserts. Lance and Vortex are a different class, built for AI access patterns and native vectors. For retrieval, the format choice matters less than how the embedding gets indexed. See how this plays out in the Vector Lakebase launch overview, or start free with $100 in credits.