Creating RDDs with Spark 2.0 using internal data sources

There are four ways to create RDDs in Spark. They range from the parallelize() method for simple testing and debugging within the client driver code to streaming RDDs for near-realtime responses. In this recipe, we provide you with several examples to demonstrate RDD creation using internal sources. 

How to do it...

1. Start a new project in IntelliJ or in an IDE of your choice. Make sure the necessary JAR files are included.

2. Set up the package location where the program will reside:

package spark.ml.cookbook.chapter3

3. Import the necessary packages:

import breeze.numerics.pow 
import org.apache.spark.sql.SparkSession 
import Array._

4. Import the packages for setting up logging level for log4j. This step is optional, but we highly recommend it (change the level appropriately as you move through the development cycle).

import org.apache.log4j.Logger 
import org.apache.log4j.Level 

5. Set up the logging level to warning and error to cut down on output...