How to integrate Claude Compliance with Apache Airflow

Jerod Johnson
Jerod Johnson
Director, Technology Evangelism
Access and process Claude Compliance data in Apache Airflow using the CData JDBC Driver.

Apache Airflow supports the creation, scheduling, and monitoring of data engineering workflows. When paired with the CData JDBC Driver for Claude Compliance, Airflow can work with live Claude Compliance data. This article describes how to connect to and query Claude Compliance data from an Apache Airflow instance and store the results in a CSV file.

With built-in optimized data processing, the CData JDBC driver offers unmatched performance for interacting with live Claude Compliance data. When you issue complex SQL queries to Claude Compliance, the driver pushes supported SQL operations, like filters and aggregations, directly to Claude Compliance and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Claude Compliance data using native data types.

Configuring the Connection to Claude Compliance

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Claude Compliance JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.


java -jar cdata.jdbc.claudecompliance.jar

Fill in the connection properties and copy the connection string to the clipboard.

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.

Using the built-in connection string designer to generate a JDBC URL (claude compliance is shown.)

To host the JDBC driver in clustered environments or in the cloud, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), contact our sales team.

The following are essential properties needed for our JDBC connection.

PropertyValue
Database Connection URLjdbc:claudecompliance:RTK=5246...;APIKey=your_compliance_access_key;
Database Driver Class Namecdata.jdbc.claudecompliance.ClaudeComplianceDriver

Establishing a JDBC Connection within Airflow

  1. Log into your Apache Airflow instance.
  2. On the navbar of your Airflow instance, hover over Admin and then click Connections. Clicking connections
  3. Next, click the + sign on the following screen to create a new connection.
  4. In the Add Connection form, fill out the required connection properties:
    • Connection Id: Name the connection, i.e.: claudecompliance_jdbc
    • Connection Type: JDBC Connection
    • Connection URL: The JDBC connection URL from above, i.e.: jdbc:claudecompliance:RTK=5246...;APIKey=your_compliance_access_key;)
    • Driver Class: cdata.jdbc.claudecompliance.ClaudeComplianceDriver
    • Driver Path: PATH/TO/cdata.jdbc.claudecompliance.jar
    Add JDBC connection form
  5. Test your new connection by clicking the Test button at the bottom of the form.
  6. After saving the new connection, on a new screen, you should see a green banner saying that a new row was added to the list of connections: New connection added

Creating a DAG

A DAG in Airflow is an entity that stores the processes for a workflow and can be triggered to run this workflow. Our workflow is to simply run a SQL query against Claude Compliance data and store the results in a CSV file.

  1. To get started, in the Home directory, there should be an "airflow" folder. Within there, we can create a new directory and title it "dags". In here, we store Python files that convert into Airflow DAGs shown on the UI.
  2. Next, create a new Python file and title it claude compliance_hook.py. Insert the following code inside of this new file:
    	import time
    	from datetime import datetime
    	from airflow.decorators import dag, task
    	from airflow.providers.jdbc.hooks.jdbc import JdbcHook
    	import pandas as pd
    
    	# Declare Dag
    	@dag(dag_id="claude compliance_hook", schedule_interval="0 10 * * *", start_date=datetime(2022,2,15), catchup=False, tags=['load_csv'])
    	
    	# Define Dag Function
    	def extract_and_load():
    	# Define tasks
    		@task()
    		def jdbc_extract():
    			try:
    				hook = JdbcHook(jdbc_conn_id="jdbc")
    				sql = """ select * from Account """
    				df = hook.get_pandas_df(sql)
    				df.to_csv("/{some_file_path}/{name_of_csv}.csv",header=False, index=False, quoting=1)
    				# print(df.head())
    				print(df)
    				tbl_dict = df.to_dict('dict')
    				return tbl_dict
    			except Exception as e:
    				print("Data extract error: " + str(e))
                
    		jdbc_extract()
        
    	sf_extract_and_load = extract_and_load()
    
  3. Save this file and refresh your Airflow instance. Within the list of DAGs, you should see a new DAG titled "claude compliance_hook". New DAG added
  4. Click on this DAG and, on the new screen, click on the unpause switch to make it turn blue, and then click the trigger (i.e. play) button to run the DAG. This executes the SQL query in our claude compliance_hook.py file and export the results as a CSV to whichever file path we designated in our code. Run the DAG
  5. After triggering our new DAG, we check the Downloads folder (or wherever you chose within your Python script), and see that the CSV file has been created - in this case, account.csv. CSV created
  6. Open the CSV file to see that your Claude Compliance data is now available for use in CSV format thanks to Apache Airflow. CSV file with Claude Compliance data.

More Information & Free Trial

Download a free, 30-day trial of the CData JDBC Driver for Claude Compliance and start working with your live Claude Compliance data in Apache Airflow. Reach out to our Support Team if you have any questions.

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Rapidly create and deploy powerful Java applications that integrate with Claude Compliance data including Activities, Chats, ChatMessages, CodeArtifacts, ComplianceRoles, Organizations, OrganizationUsers, Projects, and more!