Start by importing the Python Client and authentication provider:
from tamr_unify_client import Client from tamr_unify_client.auth import UsernamePasswordAuth
Next, create an authentication provider and use that to create an authenticated client:
import os username = os.environ['TAMR_USERNAME'] password = os.environ['TAMR_PASSWORD'] auth = UsernamePasswordAuth(username, password) tamr = Client(auth)
For security, it’s best to read your credentials in from environment variables or secure files instead of hardcoding them directly into your code.
For more, see User Guide > Secure Credentials .
By default, the client tries to find the Tamr instance on
To point to a different host, set the host argument when instantiating the Client.
For example, to connect to
tamr = Client(auth, host='10.20.0.1')
The Python Client exposes 2 top-level collections: Projects and Datasets.
You can access these collections through the client and loop over their members
for project in tamr.projects: print(project.name) for dataset in tamr.datasets: print(dataset.name)
Fetch a specific resource¶
If you know the identifier for a specific resource, you can ask for it directly
by_resource_id methods exposed by collections.
E.g. To fetch the project with ID
project = tamr.projects.by_resource_id('1')
Related resources (like a project and its unified dataset) can be accessed through specific methods.
E.g. To access the Unified Dataset for a particular project:
ud = project.unified_dataset()
Kick-off Tamr Operations¶
Some methods on Model objects can kick-off long-running Tamr operations.
Here, kick-off a “Unified Dataset refresh” operation:
operation = project.unified_dataset().refresh() assert op.succeeded()
By default, the API Clients expose a synchronous interface for Tamr operations.