How to integrate Claude Compliance with Apache Airflow
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
- Enable the Compliance API for your organization.
- Sign in to claude.ai as a primary owner or organization owner and open Organization settings > API > Keys.
- 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.
- 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.
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.
| Property | Value |
|---|---|
| Database Connection URL | jdbc:claudecompliance:RTK=5246...;APIKey=your_compliance_access_key; |
| Database Driver Class Name | cdata.jdbc.claudecompliance.ClaudeComplianceDriver |
Establishing a JDBC Connection within Airflow
- Log into your Apache Airflow instance.
- On the navbar of your Airflow instance, hover over Admin and then click Connections.
- Next, click the + sign on the following screen to create a new connection.
- 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
- Test your new connection by clicking the Test button at the bottom of the form.
- 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:
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.
- 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.
- 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() - Save this file and refresh your Airflow instance. Within the list of DAGs, you should see a new DAG titled "claude compliance_hook".
- 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.
- 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.
- Open the CSV file to see that your Claude Compliance data is now available for use in CSV format thanks to Apache Airflow.