Process & Analyze SendGrid Data in Azure Databricks

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 SendGrid data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live SendGrid data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live SendGrid data in Databricks, install the driver on your Azure 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.sendgrid.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for SendGrid\lib).

Connect to SendGrid from Databricks

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

Configure the Connection to SendGrid

Connect to SendGrid by referencing the class for the JDBC Driver 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.

driver = "cdata.jdbc.sendgrid.SendGridDriver"
url = "jdbc:sendgrid:RTK=5246...;User=admin;Password=abc123;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the SendGrid 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.

To make use of all the available features, provide the User and Password connection properties.

To connect with limited features, you can set the APIKey connection property instead. See the "Getting Started" chapter of the help documentation for a guide to obtaining the API key.

Load SendGrid Data

Once the connection is configured, you can load SendGrid data as a dataframe using the CData JDBC Driver and the connection information.

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

Display SendGrid Data

Check the loaded SendGrid data by calling the display function.

display (remote_table.select ("Name"))

Analyze SendGrid Data in Azure Databricks

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

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the SendGrid data for analysis.

% sql

SELECT Name, Clicks FROM AdvancedStats

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