anchor.diff:  All functionality between Spark and HBase will be supported both in Scala and in
   Java, with the exception of SparkSQL which will support any language that is
   supported by Spark. For the remaining of this documentation we will focus on
  -Scala examples for now.
  +Scala examples.
   
   The examples above illustrate how to do a foreachPartition with a connection. A
   number of other Spark base functions  are supported out of the box:
  
positive.code: After loading df DataFrame, users can query data. registerTempTable registers df DataFrame
  as a temporary table using the table name avrotable. `sqlContext.sql` function allows the
  user to execute SQL queries.
  ====
negative.code: There are two options for bulk loading data into HBase with Spark.  There is the
  basic bulk load functionality that will work for cases where your rows have
  millions of columns and cases where your columns are not consolidated and
  partitioned before the map side of the Spark bulk load process.
  
  There is also a thin record bulk load option with Spark. This second option is
  designed for tables that have less then 10k columns per row.  The advantage
  of this second option is higher throughput and less over-all load on the Spark
  shuffle operation.
  
  Both implementations work more or less like the MapReduce bulk load process in
  