How to work with Teradata Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Teradata, Spark can work with live Teradata data. This article describes how to connect to and query Teradata data from a Spark shell.

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

Install the CData JDBC Driver for Teradata

Download the CData JDBC Driver for Teradata installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Teradata Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Teradata JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Teradata/lib/cdata.jdbc.teradata.jar
   

2. With the shell running, you can connect to Teradata with a JDBC URL and use the SQL Context load() function to read a table.

   To connect to Teradata, provide authentication information and specify the database server name.

   • User: Set this to the username of a Teradata user.
   • Password: Set this to the password of the Teradata user.
   • DataSource: Specify the Teradata server name, DBC Name, or TDPID.
   • Port: Specify the port the server is running on.
   • Database: Specify the database name. If not specified, the default database is used.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.teradata.jar
   

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

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

   Configure the connection to Teradata, using the connection string generated above.

   scala> val teradata_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:teradata:User=myuser;Password=mypassword;Server=localhost;Database=mydatabase;").option("dbtable","NorthwindProducts").option("driver","cdata.jdbc.teradata.TeradataDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Teradata data as a temporary table:

   scala> teradata_df.registerTable("northwindproducts")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> teradata_df.sqlContext.sql("SELECT ProductId, ProductName FROM NorthwindProducts WHERE CategoryId = 5").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for Teradata in Apache Spark, you are able to perform fast and complex analytics on Teradata data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.