anchor.diff:  using Spark, is that they define the same API you use when dealing with Scala
   collections.
   
  -Spark SQL
  -~~~~~~~~~
  +=== Spark SQL
   
   Spark SQL is the module dedicated to structured data processing. As you might
   have guessed, it lets you interact with data using SQL, however, that's not all.
  
positive.code: | 13 | "value 1 col2" | 33.76
  |==================
  
  And we decide to index `Column 2`, we'll obtain:
  
  .An example DataFrame with Column 2 indexed
  [options="header"]
  |==================
  | Column 1 | Column 2 | Column 3 | Column 2 indexed
  
negative.code: 
  Last but not least, Spark benefits from great integration with external data
  sources like Hive or Kafka which makes integrating Spark with your existing
  data infrastructure seamless.
  
  To give you an overview of what a Spark cluster looks like:
  
  .Overview of a Spark cluster with two nodes. The _SparkSession_ interacts with the cluster manager to obtain the required computation power. It also interacts with the executors to ditribute work.
  image::../images/cluster.svg[]
  
  On the architecture side, each worker node will run its own JVM process called
  