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

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

Install the CData JDBC Driver in Databricks

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

Access Salesloft Data in your Notebook: Python

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

Configure the Connection to Salesloft

Connect to Salesloft 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.salesloft.SalesLoftDriver"
url = "jdbc:salesloft:RTK=5246...;AuthScheme=OAuth;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackUrl=http://localhost:33333;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

java -jar cdata.jdbc.salesloft.jar

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

SalesLoft authenticates using the OAuth authentication standard or an API Key. OAuth requires the authenticating user to interact with SalesLoft using the browser.

Using OAuth

For OAuth authentication, create an OAuth app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the OAuth section in the Help documentation for an authentication guide.

Using APIKey

Alternatively, you can authenticate with an APIKey. Provision an API key from the SalesLoft user interface: https://accounts.salesloft.com/oauth/applications/. You will receive a Key which will be used when issuing requests.

Load Salesloft Data

Once you configure the connection, you can load Salesloft 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 Salesloft Data

Check the loaded Salesloft data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

Analyze Salesloft 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 Salesloft data for reporting, visualization, and analysis.

% sql

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

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