Apache Hive on Apache Spark: The First Demo by Brock Noland.
From the post:
Apache Spark is quickly becoming the programmatic successor to MapReduce for data processing on Apache Hadoop. Over the course of its short history, it has become one of the most popular projects in the Hadoop ecosystem, and is now supported by multiple industry vendors—ensuring its status as an emerging standard.
Two months ago Cloudera, Databricks, IBM, Intel, MapR, and others came together to port Apache Hive and the other batch processing engines to Spark. In October at Strata + Hadoop World New York, the Hive on Spark project lead Xuefu Zhang shared the project status and a provided a demo of our work. The same week at the Bay Area Hadoop User Group, Szehon Ho discussed the project and demo’ed the work completed. Additionally, Xuefu and Suhas Satish will be speaking about Hive on Spark at the Bay Area Hive User Group on Dec. 3.
The community has committed more than 140 changes to the Spark branch as part of HIVE-7292 – Hive on Spark. We are proud to say that queries are now functionally able to run, as you can see in the demo below of a multi-node Hive-on-Spark query (query 28 from TPC-DS with a scale factor of 20 on a TPC-DS derived dataset).
…
After seeing the demo, you will want to move Spark up on your technology to master list!