Process & Analyze SparkPost 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 SparkPost data. This article explains how to host the CData JDBC Driver in AWS, as well as connect to and process live SparkPost data in Databricks.

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

Install the CData JDBC Driver in Databricks

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

Access SparkPost Data in your Notebook: Python

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

Configure the Connection to SparkPost

Connect to SparkPost 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.api.APIDriver"
url = "jdbc:api:RTK=5246...;Profile=C:\profiles\SparkPost.apip;ProfileSettings='APIKey=your_api_key';"

Built-in Connection String Designer

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

java -jar cdata.jdbc.api.jar

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

Start by setting the Profile connection property to the location of the SparkPost Profile on disk (e.g. C:\profiles\SparkPost.apip). Next, set the ProfileSettings connection property to the connection string for SparkPost (see below).

SparkPost API Profile Settings

Generate an API key by navigating to Configuration > API Keys > Create API Key in your SparkPost account.

Load SparkPost Data

Once you configure the connection, you can load SparkPost 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" , "ABTests") \
	.load ()

Display SparkPost Data

Check the loaded SparkPost data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

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

% sql

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

The data from SparkPost 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 API Driver for JDBC and start working with your live SparkPost data in Databricks. Reach out to our Support Team if you have any questions.