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Apache Iceberg vs Delta Lake
Iceberg is the neutral, multi-vendor table format; Delta Lake is Spark- and Databricks-native. The deciding factor is usually your existing compute.
Side by side
| Apache Iceberg | Delta Lake | |
|---|---|---|
| Vendor | Apache Software Foundation | Linux Foundation (originated by Databricks) |
| Pricing model | Open source + paid options | Open source + paid options |
| Free tier | Yes | Yes |
| Deployment | Self-hosted | Self-hosted |
| Open source | Yes (Apache-2.0) | Yes (Apache-2.0) |
| Best for | Teams building a multi-engine lakehouse who need one open table format multiple compute engines can share. | Spark-centric teams wanting reliable, versioned tables on a data lake without a separate warehouse. |
| Pricing | Free, open-source specification and libraries; no vendor pricing, though managed catalog and compute services built on Iceberg are sold separately by cloud vendors. Pricing has not been verified yet — see the vendor's site. | Free, open-source table format; no vendor pricing of its own, though Databricks and other lakehouse platforms charge separately for the compute that reads and writes Delta tables. Pricing has not been verified yet — see the vendor's site. |
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Verdict
Apache Iceberg and Delta Lake solve the same problem — bringing warehouse-style ACID transactions, schema evolution and time travel to Parquet files on a data lake — through different metadata mechanics: Iceberg tracks table state through a chain of immutable manifest files, Delta Lake through an ordered transaction log. In practice, the metadata mechanism matters less than the ecosystem each format grew up in. Delta Lake originated at Databricks and its deepest, most native integration remains with Spark and Databricks specifically, even though it is now governed under the Linux Foundation and readable by Trino, Flink and Presto. Iceberg was built engine-agnostic from the start under independent Apache governance, which is why it has become the common interchange format among Snowflake, Databricks, Dremio and Starburst.
Choose Apache Iceberg if
- You're running a multi-engine lakehouse and aren't committed to Databricks as the primary compute layer.
- Broad current vendor and catalog support matters for long-term flexibility.
- You need partition evolution without rewriting existing data.
- You're starting fresh with no existing Spark- or Databricks-centric investment to protect.
Choose Delta Lake if
- Your stack is already Spark-centric, particularly on Databricks, and you want the tightest native integration available.
- You want Delta Lake's Change Data Feed for incremental downstream processing built into the format.
- Your team is already comfortable with Databricks as the primary way data gets written and read.
What they share
Both are open-source under Apache-2.0, both provide ACID transactions with snapshot isolation, time travel and schema evolution, and both are readable and writable by a broadly overlapping set of engines — Spark, Trino and Flink support both formats today.
The honest caveat
A table format is a long-term commitment; migrating between formats later means rewriting metadata at minimum, and often re-architecting the pipelines that write to it. If you have no existing Databricks affinity, Iceberg's broader current cross-vendor support in the tool records here is the safer choice for optionality — but check what your primary compute or warehouse vendor actually recommends before committing, since that recommendation will shape day-to-day friction more than either format's own specification does.
Last reviewed September 22, 2026