SparkContext, though still supported, was more relevant in the case of RDD (covered in the next recipe). As you will see in the rest of the book, different libraries have different wrappers around SparkContext, for example, HiveContext/SQLContext for Spark SQL, StreamingContext for Streaming, and so on. As all the libraries are moving toward DataSet/DataFrame, it makes sense to have a unified entry point for all these libraries as well, and that is SparkSession. SparkSession is available as spark in the spark-shell. Here's how you do it:

import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
val sparkSession = SparkSession.builder.master("master url").appName("my app").getOrCreate()