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Bridge Dynamics GP Connectivity with Apache Airflow



Access and process Dynamics GP 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 Dynamics GP, Airflow can work with live Dynamics GP data. This article describes how to connect to and query Dynamics GP 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 Dynamics GP data. When you issue complex SQL queries to Dynamics GP, the driver pushes supported SQL operations, like filters and aggregations, directly to Dynamics GP 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 Dynamics GP data using native data types.

Configuring the Connection to Dynamics GP

Built-in Connection String Designer

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

java -jar cdata.jdbc.dynamicsgp.jar

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

To authenticate set the User and Password connection properties.

To connect set the URL to the Web services endpoint; for example, http://{servername}:{port}/Dynamics/GPService. Additionally, set CompanyId; you can obtain this value in the company setup window: Click Tools -> Setup -> Company.

By default, data summaries are not returned to save performance. Set LookupIds to true to return details such as line items; however, note that entities must be retrieved one at a time.

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:dynamicsgp:RTK=5246...;CompanyId=mycompanyId;user=myuser;password=mypassword;URL= http://{servername}:{port}/Dynamics/GPService;
Database Driver Class Namecdata.jdbc.dynamicsgp.DynamicsGPDriver

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.: dynamicsgp_jdbc
    • Connection Type: JDBC Connection
    • Connection URL: The JDBC connection URL from above, i.e.: jdbc:dynamicsgp:RTK=5246...;CompanyId=mycompanyId;user=myuser;password=mypassword;URL= http://{servername}:{port}/Dynamics/GPService;)
    • Driver Class: cdata.jdbc.dynamicsgp.DynamicsGPDriver
    • Driver Path: PATH/TO/cdata.jdbc.dynamicsgp.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 Dynamics GP 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 dynamics gp_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="dynamics gp_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 "dynamics gp_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 dynamics gp_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 Dynamics GP 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 JDBC Driver for Dynamics GP and start working with your live Dynamics GP data in Apache Airflow. Reach out to our Support Team if you have any questions.