Integrate Spark with External Services using SnapLogic

SnapLogic is an integration platform-as-a-service (iPaaS) that allows users to create data integration flows with no code. When paired with the CData JDBC Drivers, users get access to live data from more than 250+ SaaS, Big Data and NoSQL sources, including Spark, in their SnapLogic workflows.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Spark data. When platforms issue complex SQL queries to Spark, the driver pushes supported SQL operations, like filters and aggregations, directly to Spark and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying lets you work with Spark data using native data types.

Connect to Spark in SnapLogic

To connect to Spark data in SnapLogic, download and install the CData Spark JDBC Driver. Follow the installation dialog. When the installation is complete, the JAR file can be found in the installation directory (C:/Program Files/CData/CData JDBC Driver for Apache Spark/lib by default).

Upload the Spark JDBC Driver

After installation, upload the JDBC JAR file to a location in SnapLogic (for example, projects/Jerod Johnson) from the Manager tab.

Configure the Connection

Once the JDBC Driver is uploaded, we can create the connection to Spark.

1. Navigate to the Designer tab
2. Expand "JDBC" from Snaps and drag a "Generic JDBC - Select" snap onto the designer
3. Click Add Account (or select an existing one) and click "Continue"
4. In the next form, configure the JDBC connection properties:
   • Under JDBC JARs, add the JAR file we previously uploaded
   • Set JDBC Driver Class to cdata.jdbc.sparksql.SparkSQLDriver

   • Set JDBC URL to a JDBC connection string for the Spark JDBC Driver, for example:

     jdbc:sparksql:Server=127.0.0.1;RTK=XXXXXX;

     NOTE: RTK is a trial or full key. Contact our Support team for more information.

     Built-In Connection String Designer

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

     java -jar cdata.jdbc.sparksql.jar

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

     Set the Server, Database, User, and Password connection properties to connect to SparkSQL.

5. After entering the connection properties, click "Validate" and "Apply"

Read Spark Data

In the form that opens after validating and applying the connection, configure your query.

• Set Schema name to "SparkSQL"
• Set Table name to a table for Spark using the schema name, for example: "SparkSQL"."Customers" (use the drop-down to see the full list of available tables)
• Add Output fields for each item you wish to work with from the table

Save the Generic JDBC - Select snap.

With connection and query configured, click the end of the snap to preview the data (highlighted below).

Once you confirm the results are what you expect, you can add additional snaps to funnel your Spark data to another endpoint.

Piping Spark Data to External Services

For this article, we will load data in a Google Spreadsheet. You can use any of the supported snaps, or even use a Generic JDBC snap with another CData JDBC Driver, to move data into an external service.

1. Start by dropping a "Worksheet Writer" snap onto the end of the "Generic JDBC - Select" snap.
2. Add an account to connect to Google Sheets
3. Configure the Worksheet Writer snap to write your Spark data to a Google Spreadsheet

You can now execute the fully configured pipeline to extract data from Spark and push it into a Google Spreadsheet.

Piping External Data to Spark

As mentioned above, you can also use the JDBC Driver for Spark in SnapLogic to write data to Spark. Start by adding a Generic JDBC - Insert or Generic JDBC - Update snap to the dashboard.

1. Select the existing "Account" (connection) or create a new one
2. Configure the query:
   • Set Schema name to "SparkSQL"
   • Set Table name to a table for Spark using the schema name, for example: "SparkSQL"."Customers" (use the drop-down to see the full list of available tables)
3. Save the Generic JDBC - Insert/Update snap

At this point, you have configured a snap to write data to Spark, inserting new records or updating existing ones.

More Information & Free Trial

Using the CData JDBC Driver for Apache Spark you can create a pipeline in SnapLogic for integrating Spark data with external services. For more information about connecting to Spark, check at our CData JDBC Driver for Apache Spark page. Download a free, 30 day trial of the CData JDBC Driver for Apache Spark and get started today.