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

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

Install the CData JDBC Driver in Databricks

To work with live Postmark 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 Postmark Data in your Notebook: Python

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

Configure the Connection to Postmark

Connect to Postmark 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\Postmark.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your-server-api-token""

Built-in Connection String Designer

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

Using API Key Authentication

Postmark uses server API tokens to authenticate requests. Each Postmark server has its own API token, which controls access to messages, bounces, templates, and statistics associated with that server.

To obtain your Server API Token, log in to your Postmark account and navigate to the server you want to connect to. Go to API Tokens under the server settings and copy the token labeled Server API token.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Postmark Server API Token. This value is sent as the X-Postmark-Server-Token header on every request.

Example connection string:

Profile=C:\profiles\Postmark.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your-server-api-token"

Connecting to Postmark

Once the authentication is configured, you can connect to Postmark and query data from any of the available tables such as OutboundMessages, Bounces, and Templates.

Load Postmark Data

Once you configure the connection, you can load Postmark 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" , "Bounces") \
	.load ()

Display Postmark Data

Check the loaded Postmark data by calling the display function.

Step 3: Checking the result

display (remote_table.select (""))

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

% sql

SELECT ,  FROM SAMPLE_VIEW ORDER BY  DESC LIMIT 5

The data from Postmark 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 Postmark data in Databricks. Reach out to our Support Team if you have any questions.