NewUnity Catalog 0.7: native Apache Iceberg™ tables

The universal catalog for data & AI

Open source governance for tables, multimodal data, functions, and models. Unity Catalog works across Delta Lake, Apache Iceberg™, every major cloud, and the engines you already run.

Get started View on GitHub

Apache 2.0
LF AI & Data project
Delta Lake + Iceberg REST

Interoperable with the engines, clouds, and AI tools you already use

DuckDB

StarRocks

Daft

PuppyGraph

Apache XTable

LanceDB

AWS

Microsoft Azure

Google Cloud

Databricks

Salesforce

NVIDIA

LangChain

LlamaIndex

Unstructured

Tecton

Fivetran

dbt Labs

Confluent

Informatica

Immuta

Granica

01Benefits

Why teams choose Unity Catalog

One open catalog that sits underneath every engine, format, and cloud, so governance follows your data instead of your vendor.

Interoperable

Delta Lake, Apache Iceberg™, Parquet, CSV, and JSON behind one interface, with an Iceberg REST Catalog API so the broader ecosystem connects natively.

Open by default

Apache 2.0 licensed, including the OpenAPI specification, server, and clients. No proprietary lock-in, and a community that shapes the roadmap.

Unified governance

Strong authentication, secure credential vending, and asset-level access control protect data and AI assets with one consistent policy model.

Multimodal

Tables, unstructured volumes, functions, and ML models live side by side in one namespace, ready for analytics and generative AI workloads.

02Try it

Bring your own engine

Unity Catalog speaks open APIs, so the tools your team already uses can discover, read, and write governed data without a rewrite.

Read the docs

spark_quickstart.pypython

from pyspark.sql import SparkSession

spark = (
    SparkSession.builder.appName("uc-quickstart")
    .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension")
    .config("spark.sql.catalog.unity", "io.unitycatalog.spark.UCSingleCatalog")
    .config("spark.sql.catalog.unity.uri", "http://localhost:8080")
    .config("spark.sql.defaultCatalog", "unity")
    .getOrCreate()
)

spark.sql("SHOW SCHEMAS IN unity").show()

(
    spark.table("unity.default.numbers")
    .where("as_int > 500")
    .show()
)

View tutorial

duckdb_tables.pypython

import duckdb

con = duckdb.connect()
con.execute("INSTALL delta")
con.execute("LOAD delta")
con.execute("INSTALL unity_catalog")
con.execute("LOAD unity_catalog")

con.execute("""
    CREATE SECRET (
        TYPE unity_catalog,
        TOKEN 'not-used',
        ENDPOINT 'http://localhost:8080'
    )
""")
con.execute("ATTACH 'retail' AS retail (TYPE unity_catalog, DEFAULT_SCHEMA 'sales')")

con.sql("SELECT database, schema, name FROM (SHOW ALL TABLES)").show()

con.sql("""
    SELECT customer, amount
    FROM retail.sales.orders
    WHERE amount > 5
    ORDER BY order_id
""").show()

View tutorial

unitycatalog_pyiceberg.pypython

import pyarrow as pa
from pyiceberg.catalog import load_catalog
from pyiceberg.expressions import EqualTo
from pyiceberg.schema import Schema
from pyiceberg.types import BooleanType, IntegerType, NestedField, StringType

catalog = load_catalog(
    "unity",
    type="rest",
    uri="http://localhost:8080/api/2.1/unity-catalog/iceberg",
    warehouse="unity",
)
catalog.create_namespace_if_not_exists("quickstart")

pets = catalog.create_table_if_not_exists(
    "quickstart.pets",
    schema=Schema(
        NestedField(1, "name", StringType(), required=True),
        NestedField(2, "age", IntegerType(), required=True),
        NestedField(3, "adopted", BooleanType(), required=True),
    ),
)
pets.append(pa.Table.from_pylist(
    [{"name": "Biscuit", "age": 11, "adopted": False},
     {"name": "Pepper", "age": 3, "adopted": True}],
    schema=pets.schema().as_arrow(),
))

print(pets.scan(row_filter=EqualTo("adopted", False)).to_pandas())

first_catalog.shbash

# Browse the sample data
uc catalog list --output jsonPretty
uc schema list --catalog unity --output jsonPretty
uc table read --full_name unity.default.numbers --max_results 5

# Create a catalog, a schema, and a Delta table
uc catalog create --name quickstart --comment "My first catalog"
uc schema create --catalog quickstart --name sales --comment "Order data"
uc table create --full_name quickstart.sales.orders \
  --columns "order_id INT, customer STRING, amount DOUBLE" \
  --format DELTA \
  --storage_location file:///tmp/uc/orders

# Write sample rows, then read them back
uc table write --full_name quickstart.sales.orders
uc table read --full_name quickstart.sales.orders --max_results 5

Getting started guide

03Features

Everything your data and AI stack needs, in one catalog

Unity Catalog governs far more than tables. Bring structured data, unstructured files, AI tools, and models under a single, open model of access.

01
One three-level namespace
Every asset is addressed as catalog.schema.asset, so discovery, lineage, and permissions work the same way across your entire estate.
catalog.schema.asset
02
Tables in any open format
Govern Delta Lake and native Apache Iceberg™ tables, plus Parquet, CSV, and JSON. Delta UniForm exposes Delta tables to Iceberg readers.
delta · iceberg · parquet
03
Volumes for unstructured data
Images, documents, audio, and model artifacts get the same access controls and auditability as your tables.
volumes
04
Functions as AI tools
Register SQL and Python functions once, then call them from LangChain, LlamaIndex, and OpenAI agents as governed tools.
functions
05
Models with MLflow
Use Unity Catalog as an MLflow model registry to register and version models alongside the data they were trained on.
models
06
Secure by design
OAuth-based authentication, temporary credential vending for S3, ADLS, and GCS, and fine-grained grants on every securable.
authn · authz · vending

04Ecosystem

Adopting Unity Catalog doesn't mean replacing your stack

A universal interface gives you broad interoperability across cloud platforms, compute engines, AI frameworks, and the catalog and governance tools you already rely on.

Compute engines

Query the same governed tables from any engine.

DuckDB
StarRocks
Daft
PuppyGraph
Apache XTable
LanceDB

Cloud & data platforms

Run anywhere, from your laptop to every major cloud.

AWS
Microsoft Azure
Google Cloud
Databricks
Salesforce
NVIDIA

AI frameworks

Use catalog functions as tools in your agents.

LangChain
LlamaIndex
Unstructured
Tecton

Ingestion & governance

Plug into the pipelines and policies you already run.

Fivetran
dbt Labs
Confluent
Informatica
Immuta
Granica

05Endorsements

Backed by industry leaders and innovators

Jessica HawkCVP Data, AI and Digital Applications, Microsoft

Microsoft is committed to the open-source community and empowering customers with choice. Databricks has been a strategic partner for years and it's great to see them open-sourcing Unity Catalog. We believe truly open standards with broad industry participation are in customers' best interests. Our collaboration with Databricks continues to elevate Microsoft Azure as the best choice for data and AI workloads.

Chris GruszManaging Director of Technology Partnerships, AWS

AWS welcomes Databricks' move to open source Unity Catalog. AWS is committed to working with the industry on open source solutions that enable choice and interoperability for customers.

Ritika SuriDirector, Data and AI Technology Partnerships, Google Cloud

Google is committed to open, flexible solutions that empower customers to maximize the value of their data. Databricks' strategy to open up the Unity Catalog standard for data and AI aligns very well with our strategy.

Pat LeeVP of Strategic Enterprise Partnerships, NVIDIA

Enterprise data is essential to developing accurate generative AI applications. NVIDIA works closely with our partner ecosystem to support open-source offerings like Databricks Unity Catalog, which can help customers curate efficient and powerful development pipelines.

Raveendrnathan LoganathanExecutive Vice President of Software Engineering at Salesforce

Salesforce Data Cloud is built from the ground up on Open Standards with Apache Parquet and Apache Iceberg. Our zero copy innovations enable customers to unlock data, derive insights and orchestrate actions across the Customer 360. Databricks' embrace of Apache Iceberg via UniForm and Unity Catalog addresses key interoperability challenges between Delta Lake and Iceberg. We are excited to have Databricks as a member of our Zero Copy Partner Network and look forward to joint innovations with the new open Unity Catalog, delivering compelling customer value in structured data, unstructured data and AI models.

Harrison ChaseCEO & Founder, LangChain

Databricks's decision to open source Unity Catalog is an exciting development for the data and AI community. We're excited to partner with Databricks to integrate Unity Catalog with LangChain, which allows our shared users to build advanced agents using Unity Catalog functions as tools.

Brian RaymondCEO & Founder, UnstructuredIO

Unstructured is the leading unstructured data ETL solution for LLMs - helping organizations transform their data from raw to RAG-ready. Our partnership with Unity Catalog OSS makes perfect sense, as we break down data silos and accelerate AI/ML development in enterprises. We are excited to partner with Databricks to develop this open standard for AI use cases and to standardize metadata for unstructured data – helping our customers operate at the cutting edge of AI.

Sammy SidhuCEO & Founder, Eventual Computing

At Eventual, we have built Daft, the leading open source distributed query engine for multimodal data. We believe that unifying compute for tabular and unstructured data is not enough and that a multimodal catalog is crucial to build GenAI data lakehouses. We are excited to partner with Databricks and other AI innovators to develop the Unity Catalog open standard for modern data+AI workloads.

Rahul PonnalaCEO & Co-Founder, Granica

At Granica, we champion data democratization and freedom from vendor lock-in. Our Safe Room technology ensures privacy, trust, and safety in generative AI workflows while supporting open standards like Unity Catalog, Delta Lake, and Apache Iceberg. Unity Catalog's vendor-neutral architecture and robust governance solutions align with our vision of providing customers with flexibility and control over their data. We are excited to contribute to this open ecosystem, driving innovation and enabling customers to seamlessly work with their data across best-of-breed platforms.

Vinoth ChandarCEO & Co-Founder, Onehouse

Open sourcing Unity Catalog is a pivotal step towards a more collaborative and innovative data ecosystem. By making this technology accessible, Databricks is fostering an environment where the entire community can contribute to and benefit from enhanced data governance and management capabilities. This move aligns with our vision at Onehouse and Apache XTable (Incubating) to support open format interoperability that drives progress and innovation for all.

Shaun ClowesCPO, Confluent

Confluent's mission is to set data in motion and enable organizations to take advantage of their data everywhere. We're excited to see Databricks make a significant contribution to an open data ecosystem with Unity Catalog becoming open sourced. Tableflow on Confluent Cloud will enable easy delivery of real-time data to places like a data lake by turning data streams into Iceberg tables with a single click. By combining our industry-leading streaming capabilities with Databricks' robust data management solutions, customers will be able to put their data to work more effectively than ever.

Mark PorterCTO, dbt Labs

Together, Databricks and dbt Cloud help users break down data silos to collaborate effectively, simplify ETL to lower TCO with Delta Lake, and unify governance with Unity Catalog. We are thrilled to announce our support for Unity Catalog OSS and the open APIs. This partnership underscores our commitment to providing a unified data experience, empowering our community to achieve greater insights and drive innovation.

Anjan KundavaramCPO, Fivetran

We are thrilled to see the opportunity for our joint customers as Databricks open-sources Unity Catalog as an open standard for data and AI. This move will provide our customers with greater choice and flexibility in their data ecosystem, ensuring seamless integration and maximizing interoperability with Fivetran's platform as they ingest critical data to Databricks.

Matthew CarrollCEO, Immuta

The exposure of native access patterns within Unity Catalog has transformed how our business is able to streamline access to data and apply governance rules at scale - with no performance impact. Databricks continued investment in a community to accelerate services to make data controls easier to build allows our customers to govern with greater ease and manage the massive volume of new data consumers being onboarded in the age of AI.

Brett RoscoeGM and SVP Cloud Data Governance and Cloud Operations, Informatica

We are excited to see the opportunity for our joint customers as Databricks open-sources Unity Catalog as an open standard for data and AI. With Unity Catalog OSS and the Informatica intelligent Data Management Cloud, customers can gain greater choice, flexibility and interoperability in their data ecosystems.

06Learn

Learn, build, and connect

Documentation

Everything you need to run Unity Catalog

Quickstart, deployment guides, integrations, and the server and CLI reference.

Read the docs API reference

An open, documented REST API

Browse the REST API reference for catalogs, schemas, tables, volumes, functions, and models.

Explore the API Community

Build it with us

Ask questions, share what you're building, and help shape the roadmap with contributors worldwide.

Join Slack

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07FAQ

Frequently asked questions

Can't find what you're looking for? Ask the community on Slack or open an issue on GitHub.

Is Unity Catalog really open source?

Yes. Unity Catalog is licensed under Apache 2.0, including the server, the OpenAPI specification, and the clients. It is hosted by the LF AI & Data Foundation and developed in the open on GitHub.

How does it relate to Databricks Unity Catalog?

Databricks open-sourced Unity Catalog in 2024 and contributed it to the LF AI & Data Foundation. The open-source project is now built by a community of contributors from many companies, with open APIs that any vendor or engine can implement.

Which table formats are supported?

Delta Lake and native Apache Iceberg™ tables (as of 0.7), plus Parquet, CSV, JSON, and more. Delta UniForm tables are readable by Iceberg clients, and the Iceberg REST Catalog API lets Iceberg engines connect directly.

Which engines and tools can I use?

Apache Spark, DuckDB, Daft, StarRocks, PuppyGraph, and any Iceberg REST client such as PyIceberg. Functions registered in the catalog can be used as tools in LangChain, LlamaIndex, and OpenAI agents.

Can it govern AI assets, not just tables?

Yes. Volumes govern unstructured files like documents and images, functions can be exposed as AI tools, and Unity Catalog can act as an MLflow model registry, all under the same permissions model.

How do I get started?

Clone the GitHub repository and run bin/start-uc-server (Java 17+), or pull the official unitycatalog/unitycatalog Docker image. The quickstart walks you through creating and querying your first tables.

Govern every asset.
Lock in nothing.

Spin up a Unity Catalog server in under a minute, then join the community building the open standard for data and AI governance.

$git clone https://github.com/unitycatalog/unitycatalog.git && cd unitycatalog && bin/start-uc-server

Read the quickstart Star on GitHub Join Slack