Process & Analyze Sugar CRM 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 Sugar CRM data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Sugar CRM data in Databricks.

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

Install the CData JDBC Driver in Databricks

To work with live Sugar CRM 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.sugarcrm.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access Sugar CRM Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Sugar CRM 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 Sugar CRM, and create a basic report.

Configure the Connection to Sugar CRM

Connect to Sugar CRM 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.sugarcrm.SugarCRMDriver"
url = "jdbc:sugarcrm:RTK=5246...;User=MyUser;Password=MyPassword;URL=MySugarCRMAccountURL;CacheMetadata=True;"

Built-in Connection String Designer

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

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

The User and Password properties, under the Authentication section, must be set to valid SugarCRM user credentials. This will use the default OAuth token created to allow client logins. OAuthClientId and OAuthClientSecret are required if you do not wish to use the default OAuth token.

You can generate a new OAuth consumer key and consumer secret in Admin -> OAuth Keys. Set the OAuthClientId to the OAuth consumer key. Set the OAuthClientSecret to the consumer secret.

Additionally, specify the URL to the SugarCRM account.

Note that retrieving SugarCRM metadata can be expensive. It is advised that you store the metadata locally as described in the "Caching Metadata" chapter of the help documentation.

Load Sugar CRM Data

Once you configure the connection, you can load Sugar CRM 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" , "Accounts") \
	.load ()

Display Sugar CRM Data

Check the loaded Sugar CRM data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Name"))

Analyze Sugar CRM 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 Sugar CRM data for reporting, visualization, and analysis.

% sql

SELECT Name, AnnualRevenue FROM SAMPLE_VIEW ORDER BY AnnualRevenue DESC LIMIT 5

The data from Sugar CRM 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 Sugar and start working with your live Sugar CRM data in Databricks. Reach out to our Support Team if you have any questions.