Apache Hive is the most widely used SQL interface for Hadoop. As Hadoop usage continues its explosive growth, Hive`s performance and features do not meet the requirements and expectation of many users. This includes answering queries in human time (less than 30 seconds) and support for common analytics operations. The Hive community has risen to the challenge. Work is being done to drive down start up time of a Hive query, extend Hive to work on Tez (a Hadoop execution environment that is much faster than MapReduce), make Hive operators process records at 10x more than their current speed, add support for analytics and windowing functions such as RANK, NTILE, LEAD, LAG, etc., and add support to Hive for standard SQL datatypes. This talk will discuss the design and code changes that have been done as well as look at ongoing work and additional optimizations and features that could be added in the future.