You already run DataHub. Strongly fills it. Every workflow run, model, AutoML job, and drift result lands in your catalog as it happens. No exports. No nightly job. No diagram to redraw after the work changes.
Install the plugin, point it at your DataHub, and the metadata your workflows and models produce shows up where your data team already looks.
Each workflow becomes a dataFlow. Each node becomes a dataJob. Each execution becomes a run instance with status and duration. The graph in your catalog is the graph that ran.
Registered models land as mlModel entities with training metrics, hyperparameters, and training-dataset lineage. AutoML jobs carry their leaderboard and winning model.
Drift results post as DataHub assertions with a pass or fail per run. A model going out of bounds surfaces in the same view your team checks for data-quality breaks.
A node that reads Snowflake maps to a Snowflake dataset. Postgres to Postgres. S3 to S3. The same URNs DataHub uses, so AI lineage connects to assets your other tools ingested.
No pod to provision, no service to run. Point it at your DataHub GMS with a personal access token, a DataHub Cloud token, or no auth for a local instance. Enable or disable per feature.
Uploaded a file and trained on it? Register a dataset or model the platform did not produce through the SDK. It uses the same governed path as the automatic hooks, so no extra credentials.
Every emission is an idempotent upsert keyed by URN, so re-running a workflow updates the catalog in place instead of duplicating it.
The structure and the run history of every workflow, mapped the way DataHub models orchestration. Node-to-node edges come from the execution span tree.
The model registry, AutoML runs, and drift monitors, mapped to the ML and assertion entities your governance views already understand.
Where the source is known, Strongly names the dataset under its real platform. Anything without a native home gets a first-class Strongly platform. Nothing is guessed.
# Register an uploaded dataset and the model trained on it client.datahub.emit_dataset(name="q3-claims.csv", platform="s3") client.datahub.emit_model(name="claims-triage", dataset_name="q3-claims.csv")
See the DataHub plugin emit a live workflow run, a model, and a drift result into a catalog of your own. Bring your GMS URL and we will walk it end to end.