How to integrate UKG Pro WFM with Apache Airflow

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

Configuring the Connection to UKG Pro WFM

Built-in Connection String Designer

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

java -jar cdata.jdbc.api.jar

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

Start by setting the Profile connection property to the location of the UKGProWFM Profile on disk (e.g. C:\profiles\UKGProWFM.apip). Next, set the ProfileSettings connection property to the connection string for UKGProWFM (see below).

UKGProWFM API Profile Settings

UKG Pro Workforce Management uses OAuth 2.0 with the Resource Owner Password Credentials grant (grant_type=password) to authorize access to the API. Unlike most OAuth-based profiles, this does not use a browser redirect/authorization-code step: the driver exchanges your UKG Pro WFM username, password, client ID, and client secret directly for an access token.

Set the following connection properties to authenticate:

• AuthScheme: Set this to OAuthPassword.
• Host: Set this to the hostname of your UKG Pro Workforce Management datacenter/tenant (for example, yourcompany.mykronos.com). Do not include the protocol (https://) or a trailing slash.
• OAuthClientId: Set this to the client ID issued for your UKG Pro Workforce Management OAuth application.
• OAuthClientSecret: Set this to the client secret issued for your UKG Pro Workforce Management OAuth application.
• User: Set this to your UKG Pro Workforce Management username.
• Password: Set this to your UKG Pro Workforce Management password.

Access tokens are obtained from https://{Host}/api/authentication/access_token. Refresh tokens issued by UKG Pro Workforce Management expire after 7 days; if a refresh attempt fails because the refresh token itself has expired, the driver must re-authenticate with your User/Password credentials to obtain a new access token.

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.
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.: api_jdbc
   • Connection Type: JDBC Connection
   • Connection URL: The JDBC connection URL from above, i.e.: jdbc:api:RTK=5246...;Profile=C:\profiles\UKGProWFM.apip;AuthScheme=OAuthPassword;ProfileSettings='Host=yourcompany.mykronos.com;User=your_username;Password=your_password;';OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;)
   • Driver Class: cdata.jdbc.api.APIDriver
   • Driver Path: PATH/TO/cdata.jdbc.api.jar
   
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:

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 UKG Pro WFM 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 ukg pro wfm_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="ukg pro wfm_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 "ukg pro wfm_hook".
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 ukg pro wfm_hook.py file and export the results as a CSV to whichever file path we designated in our code.
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.
6. Open the CSV file to see that your UKG Pro WFM data is now available for use in CSV format thanks to Apache Airflow.

More Information & Free Trial

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