Apache: Big Data 2016

Apache Hive has wide use cases for batch-oriented SQL workloads for ETL and data analytics in the Hadoop ecosystem. Its rich features haven’t been matched by any other available SQL on Hadoop tools. In fact, many these tools are tied to and depend on Hive one way or the other. Apache Spark, on the other hand, offers a general data processing framework positioned to replace MapReduce with its faster data processing and efficient memory utilization. Moreover, one doesn’t have to abandon one for another or juggle between the two in order to get both sets of benefits, as Hive on Spark maintains Hive’s feature richness while providing faster SQL on Hadoop execution. As the adoption of Hive on Spark for production use, This presentation will share with you the best practice of deployment and performance tuning which enables you to gain the best out of the two projects.