Build SQL Server-Connected ETL Processes in Google Data Fusion

Google Data Fusion allows users to perform self-service data integration to consolidate disparate data. Uploading the CData JDBC Driver for SQL Server enables users to access live SQL Server data from within their Google Data Fusion pipelines. While the CData JDBC Driver enables piping SQL Server data to any data source natively supported in Google Data Fusion, this article walks through piping data from SQL Server to Google BigQuery,

Upload the CData JDBC Driver for SQL Server to Google Data Fusion

Upload the CData JDBC Driver for SQL Server to your Google Data Fusion instance to work with live SQL Server data. Due to the naming restrictions for JDBC drivers in Google Data Fusion, create a copy or rename the JAR file to match the following format driver-version.jar. For example: cdatasql-2020.jar

1. Open your Google Data Fusion instance
2. Click the to add an entity and upload a driver
3. On the "Upload driver" tab, drag or browse to the renamed JAR file.
4. On the "Driver configuration" tab:
   • Name: Create a name for the driver (cdata.jdbc.sql) and make note of the name
   • Class name: Set the JDBC class name: (cdata.jdbc.sql.SQLDriver)
5. Click "Finish"

Connect to SQL Server Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live SQL Server data in Google Data Fusion Pipelines.

1. Navigate to the Pipeline Studio to create a new Pipeline
2. From the "Source" options, click "Database" to add a source for the JDBC Driver
3. Click "Properties" on the Database source to edit the properties

   NOTE: To use the JDBC Driver in Google Data Fusion, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), contact our sales team.

   • Set the Label
   • Set Reference Name to a value for any future references (i.e.: cdata-sql)
   • Set Plugin Type to "jdbc"
   • Set Connection String to the JDBC URL for SQL Server. For example:
     
     jdbc:sql:RTK=5246...;User=myUser;Password=myPassword;Database=NorthWind;Server=myServer;Port=1433;

     Connecting to Microsoft SQL Server

     Connect to Microsoft SQL Server using the following properties:

     • Server: The name of the server running SQL Server.
     • User: The username provided for authentication with SQL Server.
     • Password: The password associated with the authenticating user.
     • Database: The name of the SQL Server database.

     Connecting to Azure SQL Server and Azure Data Warehouse

     You can authenticate to Azure SQL Server or Azure Data Warehouse by setting the following connection properties:

     • Server: The server running Azure. You can find this by logging into the Azure portal and navigating to "SQL databases" (or "SQL data warehouses") -> "Select your database" -> "Overview" -> "Server name."
     • User: The name of the user authenticating to Azure.
     • Password: The password associated with the authenticating user.
     • Database: The name of the database, as seen in the Azure portal on the SQL databases (or SQL warehouses) page.

     Built-in Connection String Designer

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

     java -jar cdata.jdbc.sql.jar

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

   • Set Import Query to a SQL query that will extract the data you want from SQL Server, i.e.:
     SELECT * FROM Orders
4. From the "Sink" tab, click to add a destination sink (we use Google BigQuery in this example)
5. Click "Properties" on the BigQuery sink to edit the properties
   • Set the Label
   • Set Reference Name to a value like sql-bigquery
   • Set Project ID to a specific Google BigQuery Project ID (or leave as the default, "auto-detect")
   • Set Dataset to a specific Google BigQuery dataset
   • Set Table to the name of the table you wish to insert SQL Server data into

With the Source and Sink configured, you are ready to pipe SQL Server data into Google BigQuery. Save and deploy the pipeline. When you run the pipeline, Google Data Fusion will request live data from SQL Server and import it into Google BigQuery.

While this is a simple pipeline, you can create more complex SQL Server pipelines with transforms, analytics, conditions, and more. Download a free, 30-day trial of the CData JDBC Driver for SQL Server and start working with your live SQL Server data in Google Data Fusion today.