How to Build an ETL App for Claude Compliance Data in Python with CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK and the petl framework, you can build Claude Compliance-connected applications and pipelines for extracting, transforming, and loading Claude Compliance data. This article shows how to connect to Connect AI and use petl to extract, transform, and load Claude Compliance data.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so petl can read directly from the SDK connection with etl.fromdb. There is no driver to install per source: connect with a Personal Access Token and build your pipeline.

Connect to Claude Compliance in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "Claude Compliance" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Claude Compliance.

   The driver connects to Claude Compliance using a Compliance Access Key, the only supported authentication method. Set the following connection property:

   • APIKey: The Compliance Access Key you created.

   Creating a Compliance Access Key

   1. Enable the Compliance API for your organization.
   2. Sign in to claude.ai as a primary owner or organization owner and open Organization settings > API > Keys.
   3. Select Create Key and assign only the scopes your integration needs:
      • read:compliance_activities for Activities and the typed activity views.
      • read:compliance_org_data for organizations, roles, groups, and settings.
      • read:compliance_user_data for users, chats, files, projects, and downloads.
      • delete:compliance_user_data for the explicit Delete stored procedures.
   4. Create the key and copy it when it appears. The key starts with sk-ant-api01- and is shown only once.

   Optionally, set OrganizationUUID to limit organization-scoped views (such as OrganizationUsers and ComplianceRoles) to a single linked organization. If it is left empty, those views default to the first available organization and do not include data from all linked organizations.

   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK and the petl framework using the pip utility:

pip install cdata-connect-ai
pip install petl

Build an ETL App for Claude Compliance Data in Python

Once the required modules are installed, you are ready to build the ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, import the modules and connect to Connect AI with your account email and PAT:

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Create a SQL Statement to Query Claude Compliance

Use SQL to create a statement for querying Claude Compliance. In this article, we read data from the Projects entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, ClaudeCompliance1).

sql = (
    "SELECT Id, Name "
    "FROM [ClaudeCompliance1].[ClaudeCompliance].[Projects] "
    "WHERE OrganizationUuid = 'abcdef01-2345-6789-abcd-ef0123456789'"
)

Extract, Transform, and Load the Claude Compliance Data

With a connection and query in hand, use petl to extract, transform, and load the Claude Compliance data. In this example, we extract Claude Compliance data, sort the data by the Name column, and load the data into a CSV file.

table1 = etl.fromdb(conn, sql)

table2 = etl.sort(table1, 'Name')

etl.tocsv(table2, 'projects_data.csv')

Claude Compliance is a read-only source in Connect AI, so this pipeline can extract and transform Claude Compliance data but not load rows back. Close the connection when the extract is complete:

conn.close()

With the CData Connect AI Python SDK, you can work with Claude Compliance data just like you would with any database, including direct access to data in ETL packages like petl.

More Information and Free Trial

Now you can pipe live Claude Compliance data through petl using the CData Connect AI Python SDK. For more information on connecting to Claude Compliance (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live Claude Compliance data in Python.

Full Source Code

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

sql = (
    "SELECT Id, Name "
    "FROM [ClaudeCompliance1].[ClaudeCompliance].[Projects] "
    "WHERE OrganizationUuid = 'abcdef01-2345-6789-abcd-ef0123456789'"
)

table1 = etl.fromdb(conn, sql)

table2 = etl.sort(table1, 'Name')

etl.tocsv(table2, 'projects_data.csv')
conn.close()