How to work with Snowflake 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 Snowflake, Spark can work with live Snowflake data. This article describes how to connect to and query Snowflake data from a Spark shell.

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

Getting Started

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Install the CData JDBC Driver for Snowflake

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

Start a Spark Shell and Connect to Snowflake Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Snowflake/lib/cdata.jdbc.snowflake.jar
   

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

   To connect to Snowflake:

   1. Set User and Password to your Snowflake credentials and set the AuthScheme property to PASSWORD or OKTA.
   2. Set URL to the URL of the Snowflake instance (i.e.: https://myaccount.snowflakecomputing.com).
   3. Set Warehouse to the Snowflake warehouse.
   4. (Optional) Set Account to your Snowflake account if your URL does not conform to the format above.
   5. (Optional) Set Database and Schema to restrict the tables and views exposed.
   6. (Optional) If MFA is enabled on your Snowflake account (via Duo Security), set MFACode to the passcode generated by your Duo authenticator app.

   See the Getting Started guide in the CData driver documentation for more information.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.snowflake.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 Snowflake, using the connection string generated above.

   scala> val snowflake_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:snowflake:Authscheme=Password;URL=https://myaccount.snowflakecomputing.com;User=Admin;Password=test123;Server=localhost;Database=Northwind;Warehouse=TestWarehouse;Account=Tester1;MFACode=YourMFACode").option("dbtable","Products").option("driver","cdata.jdbc.snowflake.SnowflakeDriver").load()
   

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

4. Register the Snowflake data as a temporary table:

   scala> snowflake_df.registerTable("products")

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

   scala> snowflake_df.sqlContext.sql("SELECT Id, ProductName FROM Products WHERE Id = 1").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 Snowflake in Apache Spark, you are able to perform fast and complex analytics on Snowflake 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.