This release is a pre-release and may not be stable for production use.
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.
Links
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