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DESCRIPTION:Click for Latest Location Information: http://nosql2013.dataversity.net/sessionPop.cfm?confid=74&proposalid=5630\nWhile Apache Hive is designed to allow users to leverage their SQL skills for Big Data analysis, it’s still a relatively new data warehouse infrastructure based on\nHadoop and MapReduce operations. \nIn this workshop, Shrikanth Shankar, Head of Engineering at Qubole, will explain what Hive is and how it works behind the scenes. The workshop will begin with a brief overview of Big Data and Apache Hive, its pros and cons, focusing on the key differences between Hive and traditional data warehouses built on top of relational databases. \nDuring the workshop, Shrikanth will cover data modeling in Hive, Hive Query Language constructs, features and syntax, the Hive Execution Model using MapReduce, and Hive performance optimization.\nThe workshop concludes with recommendations and examples based on best practices for Hive query optimization and for running a data warehouse on Hive.\nWorkshop Agenda:\nWhy Data Professionals Should Use Apache Hive\nThe Difference Between Hive and Traditional Data Warehousing Bulit on Relational Databases\nData Modeling in Hive (including input formats, SerDe's, partitioning etc.)\nHive Language Constructs (including transform operator, UDFs and UDTFs)\nFeatures and Syntax of the HQL Language\nThe Hive Execution Model (MapReduce)\nHive Performance Optimization (layouts, advanced execution options, etc.)\nBest Practices for Hive Query Optimization\nBest Practices for Running a Data Warehouse on Hive
DTSTART:20130822T083000
SUMMARY:Analyzing Big Data with Hive - Workshop
DTEND:20130822T114459
LOCATION: See Description
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