DVC vs lakeFS vs Delta Lake

Three versioning jobs

"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 (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 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 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

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

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)

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.

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