---
title: "DVC vs lakeFS vs Delta Lake"
description: "DVC vs lakeFS vs Delta Lake: Git-tracked files, Git-like lake branches, and ACID Parquet tables with time travel. Last reviewed October 2026."
url: "https://www.pixeltable.com/learn/dvc-vs-lakefs-vs-delta-lake"
updated: "2026-10-28"
vertical: "Versioning"
doc: "https://docs.pixeltable.com/overview/quick-start"
---

# DVC vs lakeFS vs Delta Lake

DVC, lakeFS, and Delta Lake all version data, but they version different things. DVC versions files next to Git. lakeFS versions a data lake with Git-like branches on object storage. Delta Lake is an ACID table format on Parquet with time travel. They are not interchangeable.

Updated: 2026-10-28
Part of [What is dataset versioning?](https://www.pixeltable.com/learn/what-is-dataset-versioning).

## On this page

- [Three versioning jobs](https://www.pixeltable.com/learn/dvc-vs-lakefs-vs-delta-lake#what-it-is)
- [DVC](https://www.pixeltable.com/learn/dvc-vs-lakefs-vs-delta-lake#dvc)
- [lakeFS](https://www.pixeltable.com/learn/dvc-vs-lakefs-vs-delta-lake#lakefs)
- [Delta Lake](https://www.pixeltable.com/learn/dvc-vs-lakefs-vs-delta-lake#delta-lake)
- [How to choose](https://www.pixeltable.com/learn/dvc-vs-lakefs-vs-delta-lake#how-to-choose)
- [Last reviewed](https://www.pixeltable.com/learn/dvc-vs-lakefs-vs-delta-lake#review)
- [Comparison](https://www.pixeltable.com/learn/dvc-vs-lakefs-vs-delta-lake#comparison)
- [Where Pixeltable fits](https://www.pixeltable.com/learn/dvc-vs-lakefs-vs-delta-lake#where-pixeltable-fits)
- [Questions](https://www.pixeltable.com/learn/dvc-vs-lakefs-vs-delta-lake#questions)

## Three versioning jobs {#what-it-is}

"Git for data" is a slogan that covers unlike products. One pins files for an ML experiment. One branches a lake without copying objects. One is a table format with transactions.

## DVC {#dvc}

DVC (Data Version Control) versions datasets and models beside Git. You install it as a CLI, a VS Code extension, or a Python library. It stores hashes in Git and large files in remote storage you configure. It is a workflow around files and pipelines, not a lakehouse table format. Docs: https://dvc.org/doc

## lakeFS {#lakefs}

lakeFS is version control over a data lake using Git-like semantics: branch, commit, merge, revert, tag, and hooks. Initial branch creation is a metadata operation (zero-copy). Data stays in S3, GCS, Azure, or compatible storage. It speaks the S3 API so existing tools can read lakefs://repository/branch/path. Teams use it for isolated experiments, write-audit-publish, and agent branches gated by pull requests. Docs: https://docs.lakefs.io/

## Delta Lake {#delta-lake}

Delta Lake is an open-source storage layer that enables a lakehouse on data lakes: ACID transactions, scalable metadata, unified streaming and batch, schema enforcement, time travel, and upserts/deletes (MERGE). Tables live as Parquet plus a transaction log on S3, ADLS, GCS, or HDFS. Engines include Apache Spark, Flink, Hive, Trino, and others. Docs: https://docs.delta.io/latest/delta-intro.html

## How to choose {#how-to-choose}

Pick the unit you need to pin: a file set, a lake branch, or a table version.

- Pick DVC when Git already versions code and you need to pin dataset files and pipeline stages for an experiment.
- Pick lakeFS when many tools share a bucket and you need isolated, zero-copy branches and atomic merges on that lake.
- Pick Delta Lake when Spark (or another connector) should read and write ACID tables with time travel on Parquet.

## Last reviewed {#review}

Facts on this page were checked against vendor documentation on 2026-10-28. Product surfaces move; treat version names and APIs as directional and follow the official links in Related.

## DVC vs lakeFS vs Delta Lake (last reviewed: Oct 2026) {#comparison}

|  | DVC | lakeFS | Delta Lake |
| --- | --- | --- | --- |
| What it versions | Files and pipeline stages next to Git | A data lake: branches, commits, merges | Tables: Parquet files plus a transaction log |
| Unit of isolation | A Git commit plus DVC remotes | A branch (zero-copy metadata) | A table version (time travel) |
| Transactions | Git commit of hashes, not lake ACID | Atomic merge of a branch | ACID on Spark and other connectors |
| Where bytes live | A remote you configure (S3, local, and others) | Your existing bucket; lakeFS is a metadata layer | The lake path you give the table |
| Best for | ML experiments that must pin files | Git-like collaboration on a shared lake | Lakehouse tables with MERGE and time travel |
| Official docs | https://dvc.org/doc | https://docs.lakefs.io/ | https://docs.delta.io/latest/delta-intro.html |

## Where Pixeltable fits {#where-pixeltable-fits}

Use Pixeltable when the unit you need to pin is a table of typed media and computed columns, with history() and revert on that catalog, not a lake branch and not a Git-tracked file hash. It is not DVC, not lakeFS, and not Delta Lake. Keep DVC for experiment files beside Git. Keep lakeFS when the company lake needs Git-like branches. Keep Delta Lake when Spark ACID tables are the contract. Export to a lake when a warehouse or lakehouse must read a snapshot.

```python
import pixeltable as pxt
from pixeltable.functions.openai import chat_completions

TableModel = pxt.model_base()

class Docs(TableModel, name='docs'):
    text: pxt.String
    summary = chat_completions(
        messages=[{'role': 'user', 'content': text}],
        model='gpt-4o-mini',
    ).choices[0].message.content

# pxt schema update app.py docs -f
# Insert recomputes that row. Unchanged rows keep the cached summary.
# pxt history
# pxt revert --steps 1
```

## Questions {#questions}

### Is lakeFS the same as Delta Lake time travel? {#faq-1}

No. lakeFS versions a lake with branches and commits over objects, format-agnostic. Delta Lake versions a table format (Parquet plus log) with ACID and time travel. You can store Delta tables in a lake that lakeFS versions.

### Is DVC a lakehouse? {#faq-2}

No. DVC versions files and pipelines next to Git. It does not provide Spark ACID tables or lake branches.

### Can I use more than one? {#faq-3}

Yes. DVC for the training repo's data files, lakeFS for isolated lake branches, Delta Lake for the tables Spark reads. They overlap only in the slogan "version data."

### Where do computed media columns fit? {#faq-4}

In an application catalog that versions rows and expressions. Pixeltable does that job. It does not replace lake-scale Git semantics or Delta MERGE.

## Related

- [What is dataset versioning?](https://www.pixeltable.com/learn/what-is-dataset-versioning)
- [What is time travel?](https://www.pixeltable.com/learn/what-is-time-travel)
- [What is data lineage?](https://www.pixeltable.com/learn/what-is-data-lineage)
- [What is reproducibility in a data pipeline?](https://www.pixeltable.com/learn/what-is-reproducibility)
- [DVC documentation](https://dvc.org/doc)
- [lakeFS documentation](https://docs.lakefs.io/)
- [Delta Lake introduction](https://docs.delta.io/latest/delta-intro.html)