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Python Connector Libraries for Tableau CRM Analytics Data Connectivity. Integrate Tableau CRM Analytics with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

How to use SQLAlchemy ORM to access Tableau CRM Analytics Data in Python

Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Tableau CRM Analytics data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Tableau CRM Analytics and the SQLAlchemy toolkit, you can build Tableau CRM Analytics-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Tableau CRM Analytics data to query, update, delete, and insert Tableau CRM Analytics data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Tableau CRM Analytics data in Python. When you issue complex SQL queries from Tableau CRM Analytics, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Tableau CRM Analytics and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Tableau CRM Analytics Data

Connecting to Tableau CRM Analytics data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Tableau CRM Analytics uses the OAuth 2 authentication standard. You will need to obtain the OAuthClientId and OAuthClientSecret by registering an app with Tableau CRM Analytics.

See the Getting Started section of the Help documentation for an authentication guide.

Follow the procedure below to install SQLAlchemy and start accessing Tableau CRM Analytics through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker

Model Tableau CRM Analytics Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Tableau CRM Analytics data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("tableaucrm:///?OAuthClientId=MyConsumerKey&OAuthClientSecret=MyConsumerSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Tableau CRM Analytics Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Dataset_Opportunity table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base() class Dataset_Opportunity(base): __tablename__ = "Dataset_Opportunity" Name = Column(String,primary_key=True) CloseDate = Column(String) ...

Query Tableau CRM Analytics Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("tableaucrm:///?OAuthClientId=MyConsumerKey&OAuthClientSecret=MyConsumerSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Dataset_Opportunity).filter_by(StageName="Closed Won"): print("Name: ", instance.Name) print("CloseDate: ", instance.CloseDate) print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Dataset_Opportunity_table = Dataset_Opportunity.metadata.tables["Dataset_Opportunity"] for instance in session.execute( == "Closed Won")): print("Name: ", instance.Name) print("CloseDate: ", instance.CloseDate) print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Insert Tableau CRM Analytics Data

To insert Tableau CRM Analytics data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Tableau CRM Analytics.

new_rec = Dataset_Opportunity(Name="placeholder", StageName="Closed Won") session.add(new_rec) session.commit()

Update Tableau CRM Analytics Data

To update Tableau CRM Analytics data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Tableau CRM Analytics.

updated_rec = session.query(Dataset_Opportunity).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.StageName = "Closed Won" session.commit()

Delete Tableau CRM Analytics Data

To delete Tableau CRM Analytics data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Dataset_Opportunity).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Tableau CRM Analytics to start building Python apps and scripts with connectivity to Tableau CRM Analytics data. Reach out to our Support Team if you have any questions.