anchor.diff:  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
  -partitions before the on the map side of the Spark bulk load process.
  -
  -There is also a thin record bulk load option with Spark, this second option is
  +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
  +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
  
positive.code: 
  == SparkSQL/DataFrames
  
  HBase-Spark Connector (in HBase-Spark Module) leverages
  link:https://databricks.com/blog/2015/01/09/spark-sql-data-sources-api-unified-data-access-for-the-spark-platform.html[DataSource API]
  (link:https://issues.apache.org/jira/browse/SPARK-3247[SPARK-3247])
  introduced in Spark-1.2.0, bridges the gap between simple HBase KV store and complex
  relational SQL queries and enables users to perform complex data analytical work
  on top of HBase using Spark. HBase Dataframe is a standard Spark Dataframe, and is able to
  interact with any other data sources such as Hive, Orc, Parquet, JSON, etc.
  HBase-Spark Connector applies critical techniques such as partition pruning, column pruning,
  predicate pushdown and data locality.
  
  To use HBase-Spark connector, users need to define the Catalog for the schema mapping
  between HBase and Spark tables, prepare the data and populate the HBase table,
  then load HBase DataFrame. After that, users can do integrated query and access records
  in HBase table with SQL query. Following illustrates the basic procedure.
  
  === Define catalog
  
  
negative.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.
  ====
  