Using Apache Hadoop and related technologies as a data warehouse has been an area of interest since the early days of Hadoop. In recent years Hive has made great strides towards enabling data warehousing by expanding its SQL coverage, adding transactions, and enabling sub-second queries with LLAP. But data warehousing requires more than a full powered SQL engine. Security, governance, data movement, workload management, monitoring, and user tools are required as well. These functions are being addressed by other Apache projects such as Ranger, Atlas, Falcon, Ambari, and Zeppelin. This talk will examine how these projects can be assembled to build a data warehousing solution. It will also discuss features and performance work going on in Hive and the other projects that will enable more data warehousing use cases. These include use cases like data ingestion using merge, support for OLAP cubing queries via Hive’s integration with Druid, expanded SQL coverage, replication of data between data warehouses, advanced access control options, data discovery, and user tools to manage, monitor, and query the warehouse.
Alan Gates, Co-founder, Hortonworks