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Work with AWS Management Data in Apache Spark Using SQL

Access and process AWS Management Data in Apache Spark using the CData JDBC Driver.

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

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

Install the CData JDBC Driver for AWS Management

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

Start a Spark Shell and Connect to AWS Management Data

  1. Open a terminal and start the Spark shell with the CData JDBC Driver for AWS Management JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for AWS Management/lib/cdata.jdbc.awsdatamanagement.jar
  2. With the shell running, you can connect to AWS Management with a JDBC URL and use the SQL Context load() function to read a table.

    To authorize AWSDataManagement requests, provide the credentials for an administrator account or for an IAM user with custom permissions:

    1. Set AccessKey to the access key Id.
    2. Set SecretKey to the secret access key.
    3. Set Region to the region where your AWSDataManagement data is hosted.

    Note: Though you can connect as the AWS account administrator, it is recommended to use IAM user credentials to access AWS services.

    Built-in Connection String Designer

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

    java -jar cdata.jdbc.awsdatamanagement.jar

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

    Configure the connection to AWS Management, using the connection string generated above.

    scala> val awsdatamanagement_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:awsdatamanagement:AccessKey=myAccessKey;Account=myAccountName;Region=us-east-1;").option("dbtable","NorthwingProducts").option("driver","cdata.jdbc.awsdatamanagement.AWSDataManagementDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the AWS Management data as a temporary table:

    scala> awsdatamanagement_df.registerTable("northwingproducts")
  5. Perform custom SQL queries against the Data using commands like the one below:

    scala> awsdatamanagement_df.sqlContext.sql("SELECT PartitionKey, Name FROM NorthwingProducts WHERE Id = 1").collect.foreach(println)

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

Using the CData JDBC Driver for AWS Management in Apache Spark, you are able to perform fast and complex analytics on AWS Management data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.