Client configuration

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 localhost. To point to a different host, set the host argument when instantiating the Client.

For example, to connect to

tamr = Client(auth, host='')

Top-level collections

The Python Client exposes 2 top-level collections: Projects and Datasets.

You can access these collections through the client and loop over their members with simple for-loops.


for project in tamr.projects:

for dataset in tamr.datasets:

Fetch a specific resource

If you know the identifier for a specific resource, you can ask for it directly via the by_resource_id methods exposed by collections.

E.g. To fetch the project with ID '1':

project = tamr.projects.by_resource_id('1')

Resource relationships

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.