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DataHub CLI

DataHub comes with a friendly cli called datahub that allows you to perform a lot of common operations using just the command line.

Release Notes

You can find the release notes in github releases. If you wish release notes for each bug-fix release you can find them in acryldata releases.


Using pip

We recommend python virtual environments (venv-s) to namespace pip modules. Here's an example setup:

python3 -m venv datahub-env             # create the environment
source datahub-env/bin/activate # activate the environment

NOTE: If you install datahub in a virtual environment, that same virtual environment must be re-activated each time a shell window or session is created.

Once inside the virtual environment, install datahub using the following commands

# Requires Python 3.6+
python3 -m pip install --upgrade pip wheel setuptools
python3 -m pip install --upgrade acryl-datahub
datahub version
# If you see "command not found", try running this instead: python3 -m datahub version

If you run into an error, try checking the common setup issues.

Using docker

You can use the datahub-ingestion docker image as explained in Docker Images. In case you are using Kubernetes you can start a pod with the datahub-ingestion docker image, log onto a shell on the pod and you should have the access to datahub CLI in your kubernetes cluster.

User Guide

The datahub cli allows you to do many things, such as quickstarting a DataHub docker instance locally, ingesting metadata from your sources, as well as retrieving and modifying metadata. Like most command line tools, --help is your best friend. Use it to discover the capabilities of the cli and the different commands and sub-commands that are supported.

Usage: datahub [OPTIONS] COMMAND [ARGS]...

--debug / --no-debug
--version Show the version and exit.
--help Show this message and exit.

check Helper commands for checking various aspects of DataHub.
delete Delete metadata from datahub using a single urn or a combination of filters
docker Helper commands for setting up and interacting with a local DataHub instance using Docker.
get Get metadata for an entity with an optional list of aspects to project
ingest Ingest metadata into DataHub.
init Configure which datahub instance to connect to
put Update a single aspect of an entity
telemetry Toggle telemetry.
version Print version number and exit.

The following top-level commands listed below are here mainly to give the reader a high-level picture of what are the kinds of things you can accomplish with the cli. We've ordered them roughly in the order we expect you to interact with these commands as you get deeper into the datahub-verse.


The docker command allows you to start up a local DataHub instance using datahub docker quickstart. You can also check if the docker cluster is healthy using datahub docker check.


The ingest command allows you to ingest metadata from your sources using ingestion configuration files, which we call recipes. The main ingestion page contains detailed instructions about how you can use the ingest command and perform advanced operations like rolling-back previously ingested metadata through the rollback sub-command.


The datahub package is composed of different plugins that allow you to connect to different metadata sources and ingest metadata from them. The check command allows you to check if all plugins are loaded correctly as well as validate an individual MCE-file.


The init command is used to tell datahub about where your DataHub instance is located. The CLI will point to localhost DataHub by default. Running datahub init will allow you to customize the datahub instance you are communicating with.

Note: Provide your GMS instance's host when the prompt asks you for the DataHub host.

Alternatively, you can set the following env variables if you don't want to use a config file

DATAHUB_GMS_TOKEN= # Used for communicating with DataHub Cloud
The env variables take precedence over what is in the config.


To help us understand how people are using DataHub, we collect anonymous usage statistics on actions such as command invocations via Google Analytics. We do not collect private information such as IP addresses, contents of ingestions, or credentials. The code responsible for collecting and broadcasting these events is open-source and can be found within our GitHub.

Telemetry is enabled by default, and the telemetry command lets you toggle the sending of these statistics via telemetry enable/disable. You can also disable telemetry by setting DATAHUB_TELEMETRY_ENABLED to false.


The delete command allows you to delete metadata from DataHub. Read this guide to understand how you can delete metadata from DataHub.

datahub delete --urn "urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)" --soft


The get command allows you to easily retrieve metadata from DataHub, by using the REST API. For example the following command gets the ownership aspect from the dataset urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)

datahub get --urn "urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)" --aspect ownership | jq                                                                       put_command
"value": {
"com.linkedin.metadata.snapshot.DatasetSnapshot": {
"aspects": [
"com.linkedin.metadata.key.DatasetKey": {
"name": "SampleHiveDataset",
"origin": "PROD",
"platform": "urn:li:dataPlatform:hive"
"com.linkedin.common.Ownership": {
"lastModified": {
"actor": "urn:li:corpuser:jdoe",
"time": 1581407189000
"owners": [
"owner": "urn:li:corpuser:jdoe",
"type": "DATAOWNER"
"owner": "urn:li:corpuser:datahub",
"type": "DATAOWNER"
"urn": "urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)"


The put command allows you to write metadata into DataHub. This is a flexible way for you to issue edits to metadata from the command line. For example, the following command instructs datahub to set the ownership aspect of the dataset urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD) to the value in the file ownership.json. The JSON in the ownership.json file needs to conform to the Ownership Aspect model as shown below.

"owners": [
"owner": "urn:li:corpuser:jdoe",
"type": "DEVELOPER"
"owner": "urn:li:corpuser:jdub",
"type": "DATAOWNER"
datahub --debug put --urn "urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)" --aspect ownership -d ownership.json

[DATE_TIMESTAMP] DEBUG {datahub.cli.cli_utils:340} - Attempting to emit to DataHub GMS; using curl equivalent to:
curl -X POST -H 'User-Agent: python-requests/2.26.0' -H 'Accept-Encoding: gzip, deflate' -H 'Accept: */*' -H 'Connection: keep-alive' -H 'X-RestLi-Protocol-Version: 2.0.0' -H 'Content-Type: application/json' --data '{"proposal": {"entityType": "dataset", "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)", "aspectName": "ownership", "changeType": "UPSERT", "aspect": {"contentType": "application/json", "value": "{\"owners\": [{\"owner\": \"urn:li:corpuser:jdoe\", \"type\": \"DEVELOPER\"}, {\"owner\": \"urn:li:corpuser:jdub\", \"type\": \"DATAOWNER\"}]}"}}}' 'http://localhost:8080/aspects/?action=ingestProposal'
Update succeeded with status 200