# Delta Lake

> Open storage framework with ACID transactions for data lakes

Delta Lake is an open-source table format providing ACID transactions, schema enforcement, and time travel over Parquet files, closely integrated with Spark and Databricks.

Website: https://delta.io

## Use it when

- Your platform is Spark- or Databricks-centric; Delta is the default format there and the integration is the deepest available.
- You need ACID transactions, schema enforcement, upserts, and time travel over plain Parquet with no separate catalog service.
- Downstream consumers read the Change Data Feed rather than rescanning tables.
- Update-heavy tables benefit from deletion vectors, which avoid rewriting whole files on merge.

## Think twice when

- Writes come mostly from engines other than Spark; Trino, Flink, and the standalone readers are real but less complete than the Spark path.
- Multiple clusters write to one table on S3, which needs a DynamoDB-backed LogStore or coordinated commits.
- Renaming or dropping columns is routine; both require column mapping to be enabled first.
- The rest of your stack has standardized on Iceberg; ecosystem alignment is usually the deciding factor between the two.

## How it runs

Delta is a library in the engine plus a transaction log (_delta_log) stored inside the table path, so a catalog is optional. Data is Parquet on object storage; commits append to the log with optimistic concurrency. You run OPTIMIZE to compact and VACUUM to expire files; log checkpoints are automatic. Databricks operates all of this as a managed service, and UniForm can write Iceberg and Hudi metadata alongside for outside readers.

## Details

### General

| Attribute | Value |
| --- | --- |
| License | Open source (permissive) |
| SPDX identifier | Apache-2.0 |
| Deployment | Self-hosted, Managed |
| Operational complexity | Medium |
| Pricing | Free |
| Language | Scala, Java, Rust, Python |
| Repository | https://github.com/delta-io/delta |
| Documentation | https://docs.delta.io/ |
| Stars | 9k |

### Table Formats

| Attribute | Value |
| --- | --- |
| Engine support | Spark-first; Trino, Flink and standalone readers |
| Catalog | Optional: _delta_log in the table path |
| Update model | Copy-on-write; merge-on-read with deletion vectors |
| Partitioning | Hive-style columns, or liquid clustering with CLUSTER BY, up to four keys |
| Schema evolution | Add and reorder freely; rename and drop need column mapping enabled |
| Concurrent writers | Optimistic log commits; multi-cluster S3 needs a DynamoDB LogStore or coordinated commits |
| Indexes | File stats for data skipping, Z-order and liquid clustering; bloom filters on Databricks |
| Maintenance | OPTIMIZE to compact, VACUUM to expire files; log checkpoints are automatic |
| Change feed | Change Data Feed, written to _change_data |
| Cross-format reads | UniForm writes Iceberg and Hudi metadata alongside, GA in 4.0 |

## Capabilities

- [Table Formats](https://matca.io/capabilities/table-format)

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Source: https://matca.io/delta-lake
Last updated: 2026-08-31T11:40:47.227Z