anchor.diff:  val sc = new SparkContext(conf)         <3>
   
   val numbers = Map("one" -> 1, "two" -> 2, "three" -> 3)
  -val airports = Map("OTP" -> "Otopeni", "SFO" -> "San Fran")
  +val airports = Map("arrival" -> "Otopeni", "SFO" -> "San Fran")
   
   sc.makeRDD<4>(Seq(numbers, airports)).saveToEs<5>("spark/docs")
   ----
  
positive.code: [[spark-write]]
  ==== Writing data to {es}
  
  With {eh}, any +RDD+ can be saved to {es} as long as its content can be translated into documents. When that is not the case, one can easily _transform_ the data
  in Spark or plug-in their own customer <<configuration-serialization,+ValueWriter+>>.
  
  .Scala
  
negative.code: 
  [quote, Spark website]
  ____
  http://spark.apache.org/sql/[Spark SQL] allows relational queries expressed in SQL, HiveQL, or Scala to be executed using Spark. At the core of this component is a new type of +RDD+, SchemaRDD. A SchemaRDD is similar to a table in a traditional relational database. [...] Spark SQL is currently an alpha component. 
  ____
  On top of the core Spark support, {eh} also provides integration with Spark SQL. In other words, {es} becomes a _native_ source for Spark SQL so that data can be indexed and queried from Spark SQL _transparently_.
  
  