masterly 0.1.1

Python client for Masterly — extract governed data products and ingest records from your lakehouse

masterly — Python client

Extract governed data products and ingest records from Python — notebooks on Databricks, Microsoft Fabric, or anywhere else your pipelines run.

from masterly import Client

client = Client(
    base_url="https://app.example.com",       # your Masterly install
    token="<session or service-account token>",
    environment="env_prod_eu",
)

# Extract: page through a data product (cursor-managed for you)
for row in client.products.read("Customer 360"):
    ...

# Or straight into a DataFrame
df = client.products.read("Customer 360").to_pandas()

# Follow the change feed with a resumable cursor
feed = client.products.changes("Customer 360", cursor=saved_cursor)
for change in feed:
    ...
save(feed.cursor)  # persist for the next run

# Ingest: deliver records to a source (chunked automatically)
report = client.sources.ingest("crm", records)
print(report.records, "records in", report.batches, "batches")

Tokens: two personas

• Consumer (extract in a Databricks/Fabric job): use a service-account token (minted under Users → Service accounts). It pins its Environment and consumes published products — reference them by product id (dp_…), since listing products needs a session. Access policies (row rules, masks) apply per consumer automatically.
• Integrator (ingest + golden reads): use a session token. Sources and products can be referenced by name.

The client is a thin, typed wrapper over the stable /v1 REST contract — the same API every other channel uses. Errors raise masterly.ApiError carrying the server's error code and message. Re-delivery is idempotent: records upsert by their source key, so running the same notebook twice never duplicates data.

pandas is an optional dependency: pip install masterly[pandas].

License

Apache-2.0 — see LICENSE.