DataHub Great Expectations (GX) Plugin

Send Great Expectations data quality results into DataHub — surface assertion outcomes alongside your dataset metadata so teams can see data health at a glance.

What you can do

Emit assertion results from GX Checkpoints directly into DataHub as data quality assertions
Link quality checks to datasets — results appear on the dataset's profile in DataHub
Track pass/fail history over time for every expectation suite
Works with any DataHub deployment — self-hosted or DataHub Cloud

Compatibility

GX version	Module
0.17.x / 0.18.x	`datahub_gx_plugin.action.DataHubValidationAction`
1.x	`datahub_gx_plugin.action_v1.DataHubValidationAction`

Both APIs are supported additively in the same package. Use the module that matches your installed GX major version.

Installation

pip install acryl-datahub-gx-plugin

Quickstart (GX 0.17 / 0.18)

Add the DataHub action to your GX Checkpoint:

from datahub_gx_plugin.action import DataHubValidationAction
from great_expectations.checkpoint import Checkpoint

checkpoint = Checkpoint(
    name="my_checkpoint",
    data_context=context,
    action_list=[
        {
            "name": "datahub",
            "action": {
                "class_name": "DataHubValidationAction",
                "module_name": "datahub_gx_plugin.action",
                "server_url": "http://localhost:8080",
            },
        }
    ],
)

Quickstart (GX Core 1.x)

import great_expectations as gx
from datahub_gx_plugin.action_v1 import DataHubValidationAction

context = gx.get_context()
# ... create validation_definitions ...

checkpoint = context.checkpoints.add(
    gx.Checkpoint(
        name="my_checkpoint",
        validation_definitions=validation_definitions,
        actions=[
            DataHubValidationAction(
                name="datahub",
                server_url="http://localhost:8080",
                # Optional when batch_spec meta is incomplete:
                # platform="postgres",
                # dataset_name="public.my_table",
            )
        ],
    )
)
checkpoint.run()

Results from every Checkpoint run will appear in DataHub under the dataset's Validation tab.

Metadata

Release files for acryl-datahub-gx-plugin 1.7.0.9rc1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for acryl-datahub-gx-plugin 1.7.0.9rc1
File	Size	Uploaded
acryl_datahub_gx_plugin-1.7.0.9rc1.tar.gz	19.6 kB	Sep 3, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for acryl-datahub-gx-plugin 1.7.0.9rc1
File	Interpreter	ABI	Platform	Reset
acryl_datahub_gx_plugin-1.7.0.9rc1-py3-none-any.whl 20.0 kB Sep 3, 2026	Python 3	none	any	Details

Total release size: 39.6 kB

Release files / acryl_datahub_gx_plugin-1.7.0.9rc1.tar.gz

Download URL	acryl_datahub_gx_plugin-1.7.0.9rc1.tar.gz
Size	19.6 kB
Tags	Source
SHA-256 checksum How to use checksums	`c374b298ae55cf86f0bd37d590825edabe75cb793711eb54bf28e30c3f26bf2e`
BLAKE2b-256 checksum How to use checksums	`d4c176e2a158d899d9c5c7c0b7520ae40fdea8dcef83ebf7bf4c3175db5881f2`
Upload date	Sep 3, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.10.21`

Release files / acryl_datahub_gx_plugin-1.7.0.9rc1-py3-none-any.whl

Download URL	acryl_datahub_gx_plugin-1.7.0.9rc1-py3-none-any.whl
Size	20.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`5fbda0fef62755116ea85e327be4a50214a8f2f8f4581b7f248e94c46ca64e6e`
BLAKE2b-256 checksum How to use checksums	`7b001af576d7f92b01db4ef72c76c52bc378436d9c6598044c7dee19b9a97480`
Upload date	Sep 3, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.10.21`