Process & Analyze Sage X3 Cloud Data in Databricks (AWS)

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Sage X3 Cloud data. This article explains how to host the CData JDBC Driver in AWS, as well as connect to and process live Sage X3 Cloud data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Sage X3 Cloud data. When you issue complex SQL queries to Sage X3 Cloud, the driver pushes supported SQL operations, like filters and aggregations, directly to Sage X3 Cloud 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 Sage X3 Cloud data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Sage X3 Cloud data in Databricks, install the driver on your Databricks cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.sagex3cloud.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Sage X3 Cloud\lib).

Access Sage X3 Cloud Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Sage X3 Cloud data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Sage X3 Cloud, and create a basic report.

Configure the Connection to Sage X3 Cloud

Connect to Sage X3 Cloud by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Step 1: Connection Information

driver = "cdata.jdbc.sagex3cloud.SageX3CloudDriver"
url = "jdbc:sagex3cloud:RTK=5246...;AuthScheme=OAuth;URL=https://x3server/;OAuthAccessTokenUrl=https://auth-domain/oauth/token;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;Audience=https://api-audience;XAPIKey=your_api_key;Folder=SEED;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Sage X3 Cloud JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.sagex3cloud.jar

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

Sage X3 Cloud uses the OAuth 2.0 Client Credentials flow, and an X-API-Key is also required for API access. Set AuthScheme to OAuth and specify the following connection properties:

• URL: The base URL of your Sage X3 Cloud instance.
• OAuthAccessTokenUrl: The OAuth token endpoint (e.g., https://your-auth-domain/oauth/token).
• OAuthClientId: Your OAuth application client ID.
• OAuthClientSecret: Your OAuth application client secret.
• Audience: The API audience value for the token request.
• XAPIKey: The X-API-Key provided by your Sage X3 Cloud administrator.
• Folder: The Sage X3 folder name (e.g., SEED). This folder is used as the default schema.
• Folders (optional): A comma-separated list of Sage X3 folders (e.g., SEED,PERF). Each folder is exposed as a separate schema, so you can query across folders with the Schema.Table syntax.

The driver obtains an access token with the Client Credentials flow and sends it with the X-API-Key on every API request. With InitiateOAuth set to GETANDREFRESH (the default), the driver acquires and refreshes the token automatically.

Load Sage X3 Cloud Data

Once you configure the connection, you can load Sage X3 Cloud data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "BPCUSTOMER") \
	.load ()

Display Sage X3 Cloud Data

Check the loaded Sage X3 Cloud data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("BPCNUM"))

Analyze Sage X3 Cloud Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Sage X3 Cloud data for reporting, visualization, and analysis.

% sql

SELECT BPCNUM, BPCNAM FROM SAMPLE_VIEW ORDER BY BPCNAM DESC LIMIT 5

The data from Sage X3 Cloud is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for Sage X3 Cloud and start working with your live Sage X3 Cloud data in Databricks. Reach out to our Support Team if you have any questions.