anchor.diff:  for examples which you can model your own integrations on. Stay tuned for more examples
   using YARN and Spark in the future.
   
  -=== MapReduce Integration Known Issues and Limitations
  -
  -- When importing or exporting Parquet via the tools in `kudu-client-tools`
  -  the `parquet-hadoop` dependency jars are expected to be on the classpath.
  -  You can read more about `parquet-hadoop` link:https://github.com/apache/parquet-mr[here].
positive.code:   `Date` and complex types are not supported.
  - Kudu tables may only be registered as temporary tables in SparkSQL.
    Kudu tables may not be queried using HiveContext.
  - When importing or exporting Avro via the tools in `kudu-spark-tools`
    the `spark-avro` dependency jars are expected to be on the classpath.
    You can read more about `spark-avro` link:https://github.com/databricks/spark-avro[here].
  
  == Kudu Python Client
  The Kudu Python client provides a Python friendly interface to the C++ client API.
  
negative.code: import collection.JavaConverters._
  
  // Read a table from Kudu
  val df = spark.read.options(Map("kudu.master" -> "kudu.master:7051",
                                  "kudu.table" -> "kudu_table")).kudu
  
  // Query using the Spark API...
  df.select("id").filter("id >= 5").show()
  
  // ...or register a temporary table and use SQL
  df.registerTempTable("kudu_table")
  val filteredDF = spark.sql("select id from kudu_table where id >= 5").show()
  
  // Use KuduContext to create, delete, or write to Kudu tables
  val kuduContext = new KuduContext("kudu.master:7051", spark.sparkContext)
  
  // Create a new Kudu table from a dataframe schema
  // NB: No rows from the dataframe are inserted into the table
  