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Version: 0.13.1

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. Acryl Data maintains the pypi package for datahub.


Using pip

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

python3 -m venv venv             # create the environment
source venv/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 3.8+
python3 -m pip install --upgrade pip wheel setuptools
python3 -m pip install --upgrade acryl-datahub
# validate that the install was successful
datahub version
# If you see "command not found", try running this instead: python3 -m datahub version
datahub init
# authenticate your datahub CLI with your datahub instance

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

Other installation options such as installation from source and running the cli inside a container are available further below in the guide here

Starter Commands

The datahub cli allows you to do many things, such as quick-starting a DataHub docker instance locally, ingesting metadata from your sources into a DataHub server or a DataHub lite instance, as well as retrieving, modifying and exploring 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.

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

--debug / --no-debug Enable debug logging.
--log-file FILE Enable debug logging.
--debug-vars / --no-debug-vars Show variable values in stack traces. Implies --debug. While we try to avoid
printing sensitive information like passwords, this may still happen.
--version Show the version and exit.
-dl, --detect-memory-leaks Run memory leak detection.
--help Show this message and exit.

actions <disabled due to missing dependencies>
check Helper commands for checking various aspects of DataHub.
dataproduct A group of commands to interact with the DataProduct entity in 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.
exists A group of commands to check existence of entities in DataHub.
get A group of commands to get metadata from DataHub.
group A group of commands to interact with the Group entity in DataHub.
ingest Ingest metadata into DataHub.
init Configure which datahub instance to connect to
lite A group of commands to work with a DataHub Lite instance
migrate Helper commands for migrating metadata within DataHub.
put A group of commands to put metadata in DataHub.
state Managed state stored in DataHub by stateful ingestion.
telemetry Toggle telemetry.
timeline Get timeline for an entity based on certain categories
user A group of commands to interact with the User entity in DataHub.
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. Source specific crawlers are provided by plugins and might sometimes need additional extras to be installed. See installing plugins for more information. Removing Metadata from DataHub contains detailed instructions about how you can use the ingest command to perform operations like rolling-back previously ingested metadata through the rollback sub-command and listing all runs that happened through list-runs sub-command.

Usage: datahub [datahub-options] ingest [command-options]

Command Options:
-c / --config Config file in .toml or .yaml format
-n / --dry-run Perform a dry run of the ingestion, essentially skipping writing to sink
--preview Perform limited ingestion from the source to the sink to get a quick preview
--preview-workunits The number of workunits to produce for preview
--strict-warnings If enabled, ingestion runs with warnings will yield a non-zero error code
--test-source-connection When set, ingestion will only test the source connection details from the recipe
--no-progress If enabled, mute intermediate progress ingestion reports

ingest --dry-run

The --dry-run option of the ingest command performs all of the ingestion steps, except writing to the sink. This is useful to validate that the ingestion recipe is producing the desired metadata events before ingesting them into datahub.

# Dry run
datahub ingest -c ./examples/recipes/example_to_datahub_rest.dhub.yaml --dry-run
# Short-form
datahub ingest -c ./examples/recipes/example_to_datahub_rest.dhub.yaml -n

ingest --preview

The --preview option of the ingest command performs all of the ingestion steps, but limits the processing to only the first 10 workunits produced by the source. This option helps with quick end-to-end smoke testing of the ingestion recipe.

# Preview
datahub ingest -c ./examples/recipes/example_to_datahub_rest.dhub.yaml --preview
# Preview with dry-run
datahub ingest -c ./examples/recipes/example_to_datahub_rest.dhub.yaml -n --preview

By default --preview creates 10 workunits. But if you wish to try producing more workunits you can use another option --preview-workunits

# Preview 20 workunits without sending anything to sink
datahub ingest -c ./examples/recipes/example_to_datahub_rest.dhub.yaml -n --preview --preview-workunits=20

ingest deploy

The ingest deploy command instructs the cli to upload an ingestion recipe to DataHub to be run by DataHub's UI Ingestion. This command can also be used to schedule the ingestion while uploading or even to update existing sources. It will upload to the remote instance the CLI is connected to, not the sink of the recipe. Use datahub init to set the remote if not already set.

To schedule a recipe called "test", to run at 5am everyday, London time with the recipe configured in a local recipe.yaml file:

datahub ingest deploy --name "test" --schedule "5 * * * *" --time-zone "Europe/London" -c recipe.yaml

To update an existing recipe please use the --urn parameter to specify the id of the recipe to update.

Note: Updating a recipe will result in a replacement of the existing options with what was specified in the cli command. I.e: Not specifying a schedule in the cli update command will remove the schedule from the recipe to be updated.

ingest --no-default-report

By default, the cli sends an ingestion report to DataHub, which allows you to see the result of all cli-based ingestion in the UI. This can be turned off with the --no-default-report flag.

# Running ingestion with reporting to DataHub turned off
datahub ingest -c ./examples/recipes/example_to_datahub_rest.dhub.yaml --no-default-report

The reports include the recipe that was used for ingestion. This can be turned off by adding an additional section to the ingestion recipe.

# source configs

# sink configs

# Add configuration for the datahub reporter
- type: datahub
report_recipe: false

# Optional log to put failed JSONs into a file
# Helpful in case you are trying to debug some issue with specific ingestion failing
enabled: false
filename: ./path/to/failure.json


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. It has an optional --use-password option which allows to initialise the config using username, password. We foresee this mainly being used by admins as majority of organisations will be using SSO and there won't be any passwords to use.

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

# locally hosted example
datahub init
/Users/user/.datahubenv already exists. Overwrite? [y/N]: y
Configure which datahub instance to connect to
Enter your DataHub host [http://localhost:8080]: http://localhost:8080
Enter your DataHub access token (Supports env vars via `{VAR_NAME}` syntax) []:

# acryl example
datahub init
/Users/user/.datahubenv already exists. Overwrite? [y/N]: y
Configure which datahub instance to connect to
Enter your DataHub host [http://localhost:8080]: https://<your-instance-id>
Enter your DataHub access token (Supports env vars via `{VAR_NAME}` syntax) []: <token generated from https://<your-instance-id>>

Environment variables supported

The environment variables listed below take precedence over the DataHub CLI config created through the init command.

  • DATAHUB_SKIP_CONFIG (default false) - Set to true to skip creating the configuration file.
  • DATAHUB_GMS_URL (default http://localhost:8080) - Set to a URL of GMS instance
  • DATAHUB_GMS_HOST (default localhost) - Set to a host of GMS instance. Prefer using DATAHUB_GMS_URL to set the URL.
  • DATAHUB_GMS_PORT (default 8080) - Set to a port of GMS instance. Prefer using DATAHUB_GMS_URL to set the URL.
  • DATAHUB_GMS_PROTOCOL (default http) - Set to a protocol like http or https. Prefer using DATAHUB_GMS_URL to set the URL.
  • DATAHUB_GMS_TOKEN (default None) - Used for communicating with DataHub Cloud.
  • DATAHUB_TELEMETRY_ENABLED (default true) - Set to false to disable telemetry. If CLI is being run in an environment with no access to public internet then this should be disabled.
  • DATAHUB_TELEMETRY_TIMEOUT (default 10) - Set to a custom integer value to specify timeout in secs when sending telemetry.
  • DATAHUB_DEBUG (default false) - Set to true to enable debug logging for CLI. Can also be achieved through --debug option of the CLI. This exposes sensitive information in logs, enabling on production instances should be avoided especially if UI ingestion is in use as logs can be made available for runs through the UI.
  • DATAHUB_VERSION (default head) - Set to a specific version to run quickstart with the particular version of docker images.
  • ACTIONS_VERSION (default head) - Set to a specific version to run quickstart with that image tag of datahub-actions container.
  • DATAHUB_ACTIONS_IMAGE (default acryldata/datahub-actions) - Set to -slim to run a slimmer actions container without pyspark/deequ features.


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 delete command allows you to delete metadata from DataHub.

The metadata deletion guide covers the various options for the delete command.


🤝 Version compatibility : acryl-datahub>=

The exists command can be used to check if an entity exists in DataHub.

> datahub exists --urn "urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)"
> datahub exists --urn "urn:li:dataset:(urn:li:dataPlatform:hive,NonExistentHiveDataset,PROD)"


The get command allows you to easily retrieve metadata from DataHub, by using the REST API. This works for both versioned aspects and timeseries aspects. For timeseries aspects, it fetches the latest value. 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
"ownership": {
"lastModified": {
"actor": "urn:li:corpuser:jdoe",
"time": 1680210917580
"owners": [
"owner": "urn:li:corpuser:jdoe",
"source": {
"type": "SERVICE"
"type": "NONE"


The put group of commands allows you to write metadata into DataHub. This is a flexible way for you to issue edits to metadata from the command line.

put aspect

The put aspect (also the default put) command instructs datahub to set a specific aspect for an entity to a specified value. For example, the command shown below sets 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

put platform

🤝 Version Compatibility: acryl-datahub>

The put platform command instructs datahub to create or update metadata about a data platform. This is very useful if you are using a custom data platform, to set up its logo and display name for a native UI experience.

datahub put platform --name longtail_schemas --display_name "Long Tail Schemas" --logo ""
✅ Successfully wrote data platform metadata for urn:li:dataPlatform:longtail_schemas to DataHub (DataHubRestEmitter: configured to talk to with token: eyJh**********Cics)


The timeline command allows you to view a version history for entities. Currently only supported for Datasets. For example, the following command will show you the modifications to tags for a dataset for the past week. The output includes a computed semantic version, relevant for schema changes only currently, the target of the modification, and a description of the change including a timestamp. The default output is sanitized to be more readable, but the full API output can be obtained by passing the --verbose flag and to get the raw JSON difference in addition to the API output you can add the --raw flag. For more details about the feature please see the main feature page

datahub timeline --urn "urn:li:dataset:(urn:li:dataPlatform:mysql,User.UserAccount,PROD)" --category TAG --start 7daysago
2022-02-17 14:03:42 - 0.0.0-computed
MODIFY TAG dataset:mysql:User.UserAccount : A change in aspect editableSchemaMetadata happened at time 2022-02-17 20:03:42.0
2022-02-17 14:17:30 - 0.0.0-computed
MODIFY TAG dataset:mysql:User.UserAccount : A change in aspect editableSchemaMetadata happened at time 2022-02-17 20:17:30.118

Entity Specific Commands

user (User Entity)

The user command allows you to interact with the User entity. It currently supports the upsert operation, which can be used to create a new user or update an existing one. For detailed information, please refer to Creating Users and Groups with Datahub CLI.

datahub user upsert -f users.yaml

An example of users.yaml would look like the following. You can refer to the bar.user.dhub.yaml file for the complete code.

- id:
first_name: The
last_name: Bar
slack: "@the_bar_raiser"
description: "I like raising the bar higher"
- id: datahub
slack: "@datahubproject"
phone: "1-800-GOT-META"
description: "The DataHub Project"
picture_link: ""

group (Group Entity)

The group command allows you to interact with the Group entity. It currently supports the upsert operation, which can be used to create a new group or update an existing one with embedded Users. For more information, please refer to Creating Users and Groups with Datahub CLI.

datahub group upsert -f group.yaml

An example of group.yaml would look like the following. You can refer to the file for the complete code.

display_name: Foo Group
- datahub
- # refer to a user either by id or by urn
- id: # inline specification of user
slack: "@joe_shmoe"
display_name: "Joe's Hub"

dataproduct (Data Product Entity)

🤝 Version Compatibility: acryl-datahub>=

The dataproduct group of commands allows you to manage the lifecycle of a DataProduct entity on DataHub. See the Data Products page for more details on what a Data Product is and how DataHub represents it.

datahub dataproduct --help
upsert* Upsert attributes to a Data Product in DataHub
update Create or Update a Data Product in DataHub.
add_asset Add an asset to a Data Product
add_owner Add an owner to a Data Product
delete Delete a Data Product in DataHub.
diff Diff a Data Product file with its twin in DataHub
get Get a Data Product from DataHub
remove_asset Add an asset to a Data Product
remove_owner Remove an owner from a Data Product
set_description Set description for a Data Product in DataHub

Here we detail the sub-commands available under the dataproduct group of commands:


Use this to upsert a data product yaml file into DataHub. This will create the data product if it doesn't exist already. Remember, this will upsert all the fields that are specified in the yaml file and will not touch the fields that are not specified. For example, if you do not specify the description field in the yaml file, then upsert will not modify the description field on the Data Product entity in DataHub. To keep this file sync-ed with the metadata on DataHub use the diff command. The format of the yaml file is available here.

# Usage
> datahub dataproduct upsert -f data_product.yaml


Use this to fully replace a data product's metadata in DataHub from a yaml file. This will create the data product if it doesn't exist already. Remember, this will update all the fields including ones that are not specified in the yaml file. For example, if you do not specify the description field in the yaml file, then update will set the description field on the Data Product entity in DataHub to empty. To keep this file sync-ed with the metadata on DataHub use the diff command. The format of the yaml file is available here.

# Usage
> datahub dataproduct upsert -f data_product.yaml


Pro-Tip: upsert versus update

Wondering which command is right for you? Use upsert if there are certain elements of metadata that you don't want to manage using the yaml file (e.g. owners, assets or description). Use update if you want to manage the entire data product's metadata using the yaml file.


Use this to keep a data product yaml file updated from its server-side version in DataHub.

# Usage
> datahub dataproduct diff -f data_product.yaml --update


Use this to get a data product entity from DataHub and optionally write it to a yaml file

# Usage
> datahub dataproduct get --urn urn:li:dataProduct:pet_of_the_week --to-file pet_of_the_week_dataproduct.yaml
"id": "urn:li:dataProduct:pet_of_the_week",
"domain": "urn:li:domain:dcadded3-2b70-4679-8b28-02ac9abc92eb",
"assets": [
"display_name": "Pet of the Week Campaign",
"owners": [
"id": "urn:li:corpuser:jdoe",
"description": "This campaign includes Pet of the Week data.",
"tags": [
"terms": [
"properties": {
"lifecycle": "production",
"sla": "7am every day"
Data Product yaml written to pet_of_the_week_dataproduct.yaml


Use this to add a data asset to a Data Product.

# Usage
> datahub dataproduct add_asset --urn "urn:li:dataProduct:pet_of_the_week" --asset "urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD)"


Use this to remove a data asset from a Data Product.

# Usage
> datahub dataproduct remove_asset --urn "urn:li:dataProduct:pet_of_the_week" --asset "urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD)"


Use this to add an owner to a Data Product.

# Usage
> datahub dataproduct add_owner --urn "urn:li:dataProduct:pet_of_the_week" --owner "" --owner-type BUSINESS_OWNER


Use this to remove an owner from a Data Product.

# Usage
> datahub dataproduct remove_owner --urn "urn:li:dataProduct:pet_of_the_week" --owner ""


Use this to attach rich documentation for a Data Product in DataHub.

> datahub dataproduct set_description --urn "urn:li:dataProduct:pet_of_the_week" --description "This is the pet dataset"
# For uploading rich documentation from a markdown file, use the --md-file option
# > datahub dataproduct set_description --urn "urn:li:dataProduct:pet_of_the_week" --md-file ./


Use this to delete a Data Product from DataHub. Default to --soft which preserves metadata, use --hard to erase all metadata associated with this Data Product.

> datahub dataproduct delete --urn "urn:li:dataProduct:pet_of_the_week"
# For Hard Delete see below:
# > datahub dataproduct delete --urn "urn:li:dataProduct:pet_of_the_week" --hard

Miscellaneous Admin Commands

lite (experimental)

The lite group of commands allow you to run an embedded, lightweight DataHub instance for command line exploration of your metadata. This is intended more for developer tool oriented usage rather than as a production server instance for DataHub. See DataHub Lite for more information about how you can ingest metadata into DataHub Lite and explore your metadata easily.


To help us understand how people are using DataHub, we collect anonymous usage statistics on actions such as command invocations via Mixpanel. 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.


The migrate group of commands allows you to perform certain kinds of migrations.


The dataplatform2instance migration command allows you to migrate your entities from an instance-agnostic platform identifier to an instance-specific platform identifier. If you have ingested metadata in the past for this platform and would like to transfer any important metadata over to the new instance-specific entities, then you should use this command. For example, if your users have added documentation or added tags or terms to your datasets, then you should run this command to transfer this metadata over to the new entities. For further context, read the Platform Instance Guide here.

A few important options worth calling out:

  • --dry-run / -n : Use this to get a report for what will be migrated before running
  • --force / -F : Use this if you know what you are doing and do not want to get a confirmation prompt before migration is started
  • --keep : When enabled, will preserve the old entities and not delete them. Default behavior is to soft-delete old entities.
  • --hard : When enabled, will hard-delete the old entities.

Note: Timeseries aspects such as Usage Statistics and Dataset Profiles are not migrated over to the new entity instances, you will get new data points created when you re-run ingestion using the usage or sources with profiling turned on.

Dry Run
datahub migrate dataplatform2instance --platform elasticsearch --instance prod_index --dry-run
Starting migration: platform:elasticsearch, instance=prod_index, force=False, dry-run=True
100% (25 of 25) |####################################################################################################################################################################################| Elapsed Time: 0:00:00 Time: 0:00:00
[Dry Run] Migration Report:
[Dry Run] Migration Run Id: migrate-5710349c-1ec7-4b83-a7d3-47d71b7e972e
[Dry Run] Num entities created = 25
[Dry Run] Num entities affected = 0
[Dry Run] Num entities migrated = 25
[Dry Run] Details:
[Dry Run] New Entities Created: {'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.datahubretentionindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.schemafieldindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.system_metadata_service_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.tagindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dataset_datasetprofileaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.mlmodelindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.mlfeaturetableindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.datajob_datahubingestioncheckpointaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.datahub_usage_event,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dataset_operationaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.datajobindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dataprocessindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.glossarytermindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dataplatformindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.mlmodeldeploymentindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.datajob_datahubingestionrunsummaryaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.graph_service_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.datahubpolicyindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dataset_datasetusagestatisticsaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dashboardindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.glossarynodeindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.mlfeatureindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dataflowindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.mlprimarykeyindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.chartindex_v2,PROD)'}
[Dry Run] External Entities Affected: None
[Dry Run] Old Entities Migrated = {'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dataset_datasetusagestatisticsaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,mlmodelindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,mlmodeldeploymentindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,datajob_datahubingestionrunsummaryaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,datahubretentionindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,datahubpolicyindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dataset_datasetprofileaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,glossarynodeindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dataset_operationaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,graph_service_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,datajobindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,mlprimarykeyindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dashboardindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,datajob_datahubingestioncheckpointaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,tagindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,datahub_usage_event,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,schemafieldindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,mlfeatureindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dataprocessindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dataplatformindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,mlfeaturetableindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,glossarytermindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dataflowindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,chartindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,system_metadata_service_v1,PROD)'}
Real Migration (with soft-delete)
> datahub migrate dataplatform2instance --platform hive --instance
datahub migrate dataplatform2instance --platform hive --instance warehouse
Starting migration: platform:hive, instance=warehouse, force=False, dry-run=False
Will migrate 4 urns such as ['urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,logging_events,PROD)']
New urns will look like ['urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.logging_events,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.fct_users_created,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.SampleHiveDataset,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.fct_users_deleted,PROD)']

Ok to proceed? [y/N]:
Migration Report:
Migration Run Id: migrate-f5ae7201-4548-4bee-aed4-35758bb78c89
Num entities created = 4
Num entities affected = 0
Num entities migrated = 4
New Entities Created: {'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.SampleHiveDataset,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.fct_users_deleted,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.logging_events,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.fct_users_created,PROD)'}
External Entities Affected: None
Old Entities Migrated = {'urn:li:dataset:(urn:li:dataPlatform:hive,logging_events,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_created,PROD)'}

Alternate Installation Options

Using docker

Docker Hub datahub-ingestion docker

If you don't want to install locally, you can alternatively run metadata ingestion within a Docker container. We have prebuilt images available on Docker hub. All plugins will be installed and enabled automatically.

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.

Limitation: the convenience script assumes that the recipe and any input/output files are accessible in the current working directory or its subdirectories. Files outside the current working directory will not be found, and you'll need to invoke the Docker image directly.

# Assumes the DataHub repo is cloned locally.
./metadata-ingestion/scripts/ ingest -c ./examples/recipes/example_to_datahub_rest.yml

Install from source

If you'd like to install from source, see the developer guide.

Installing Plugins

We use a plugin architecture so that you can install only the dependencies you actually need. Click the plugin name to learn more about the specific source recipe and any FAQs!


Please see our Integrations page if you want to filter on the features offered by each source.

Plugin NameInstall CommandProvides
fileincluded by defaultFile source and sink
athenapip install 'acryl-datahub[athena]'AWS Athena source
bigquerypip install 'acryl-datahub[bigquery]'BigQuery source
datahub-lineage-fileno additional dependenciesLineage File source
datahub-business-glossaryno additional dependenciesBusiness Glossary File source
dbtno additional dependenciesdbt source
druidpip install 'acryl-datahub[druid]'Druid Source
feastpip install 'acryl-datahub[feast]'Feast source (0.26.0)
gluepip install 'acryl-datahub[glue]'AWS Glue source
hanapip install 'acryl-datahub[hana]'SAP HANA source
hivepip install 'acryl-datahub[hive]'Hive source
kafkapip install 'acryl-datahub[kafka]'Kafka source
kafka-connectpip install 'acryl-datahub[kafka-connect]'Kafka connect source
ldappip install 'acryl-datahub[ldap]' (extra requirements)LDAP source
lookerpip install 'acryl-datahub[looker]'Looker source
lookmlpip install 'acryl-datahub[lookml]'LookML source, requires Python 3.7+
metabasepip install 'acryl-datahub[metabase]'Metabase source
modepip install 'acryl-datahub[mode]'Mode Analytics source
mongodbpip install 'acryl-datahub[mongodb]'MongoDB source
mssqlpip install 'acryl-datahub[mssql]'SQL Server source
mysqlpip install 'acryl-datahub[mysql]'MySQL source
mariadbpip install 'acryl-datahub[mariadb]'MariaDB source
openapipip install 'acryl-datahub[openapi]'OpenApi Source
oraclepip install 'acryl-datahub[oracle]'Oracle source
postgrespip install 'acryl-datahub[postgres]'Postgres source
redashpip install 'acryl-datahub[redash]'Redash source
redshiftpip install 'acryl-datahub[redshift]'Redshift source
sagemakerpip install 'acryl-datahub[sagemaker]'AWS SageMaker source
snowflakepip install 'acryl-datahub[snowflake]'Snowflake source
sqlalchemypip install 'acryl-datahub[sqlalchemy]'Generic SQLAlchemy source
supersetpip install 'acryl-datahub[superset]'Superset source
tableaupip install 'acryl-datahub[tableau]'Tableau source
trinopip install 'acryl-datahub[trino]'Trino source
starburst-trino-usagepip install 'acryl-datahub[starburst-trino-usage]'Starburst Trino usage statistics source
nifipip install 'acryl-datahub[nifi]'NiFi source
powerbipip install 'acryl-datahub[powerbi]'Microsoft Power BI source
powerbi-report-serverpip install 'acryl-datahub[powerbi-report-server]'Microsoft Power BI Report Server source


Plugin NameInstall CommandProvides
fileincluded by defaultFile source and sink
consoleincluded by defaultConsole sink
datahub-restpip install 'acryl-datahub[datahub-rest]'DataHub sink over REST API
datahub-kafkapip install 'acryl-datahub[datahub-kafka]'DataHub sink over Kafka

These plugins can be mixed and matched as desired. For example:

pip install 'acryl-datahub[bigquery,datahub-rest]'

Check the active plugins

datahub check plugins

Release Notes and CLI versions

The server release notes can be found in github releases. These releases are done approximately every week on a regular cadence unless a blocking issue or regression is discovered.

CLI release is made through a different repository and release notes can be found in acryldata releases. At least one release which is tied to the server release is always made alongwith the server release. Multiple other bigfix releases are made in between based on amount of fixes that are merged between the server release mentioned above.

If server with version 0.8.28 is being used then CLI used to connect to it should be 0.8.28.x. Tests of new CLI are not ran with older server versions so it is not recommended to update the CLI if the server is not updated.