How to integrate Salesforce with Apache Airflow

Apache Airflow supports the creation, scheduling, and monitoring of data engineering workflows. When paired with the CData JDBC Driver for Salesforce, Airflow can work with live Salesforce data. This article describes how to connect to and query Salesforce 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 Salesforce data. When you issue complex SQL queries to Salesforce, the driver pushes supported SQL operations, like filters and aggregations, directly to Salesforce 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 Salesforce data using native data types.

Getting Started

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Configuring the Connection to Salesforce

Built-in Connection String Designer

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

java -jar cdata.jdbc.salesforce.jar

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

There are several authentication methods available for connecting to Salesforce: OAuth, Login (or basic), and SSO. The Login method requires you to have the username, password, and security token of the user.

OAuth Authentication (default)

The default authentication mechanism (and the one preferred by Salesforce) is OAuth. To use OAuth with CData's embedded OAuth application, leave the connection properties blank. If you have configured your own custom OAuth application with Salesforce (see the Help documentation for more information), set OAuthClientId, OAuthClientSecret, and CallbackURL to the properties for you application. Set InitiateOAuth to the desired OAuth flow ("GETANDREFRESH" will have the connector manage the entire OAuth flow).

Login (or Basic) Authentication

If you do not wish do not wish to use OAuth authentication, you can use Login (or basic) authentication. Set AuthScheme to Basic, and set the User, Password, and SecurityToken properties. You can configure your security token in Salesforce.

SSO (single sign-on) Authentication

SSO (single sign-on) can be used by setting the SSOProperties, SSOLoginUrl, and SSOExchangeURL connection properties, which allow you to authenticate to an identity provider. See the "Getting Started" chapter in the Help documentation for more information.

Multi-Factor Authentication (MFA)

If your Salesforce org has MFA enforcement enabled, set MFACode to the time-based one-time passcode (TOTP) generated by your authenticator app (such as Salesforce Authenticator or Google Authenticator). MFACode applies to both OAuth and Login authentication flows.

👁 Using the built-in connection string designer to generate a JDBC URL (salesforce 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.

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.: salesforce_jdbc
   • Connection Type: JDBC Connection
   • Connection URL: The JDBC connection URL from above, i.e.: jdbc:salesforce:RTK=5246...;InitiateOAuth=GETANDREFRESH;MFACode=YourMFACode)
   • Driver Class: cdata.jdbc.salesforce.SalesforceDriver
   • Driver Path: PATH/TO/cdata.jdbc.salesforce.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 Salesforce 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 salesforce_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="salesforce_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 "salesforce_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 salesforce_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 Salesforce data is now available for use in CSV format thanks to Apache Airflow. 👁 CSV file with Salesforce data.
   

More Information & Free Trial

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