In this hands-on tutorial, you’ll learn how to install and use Hive for Hadoop-based data warehousing. You’ll also learn some tricks of the trade and how to handle known issues.
Writing Hive Queries
We’ll spend most of the tutorial using a series of hands-on exercises with actual Hive queries, so you can learn by doing. We’ll go over all the main features of Hive’s query language, HiveQL, and how Hive works with data in Hadoop.
Hive is very flexible about the formats of data files, the “schema” of records and so forth. We’ll discuss options for customizing these and other aspects of your Hive and data cluster setup. We’ll briefly examine how you can write Java user defined functions (UDFs) and other plugins that extend Hive for data formats that aren’t supported natively.
Hive in the Hadoop Ecosystem
We’ll learn Hive’s place in the Hadoop ecosystem, such as how it compares to other available tools. We’ll discuss installation and configuration issues that ensure the best performance and ease of use in a real production cluster. In particular, we’ll discuss how to create Hive’s separate “metadata” store in a traditional relational database, such as MySQL. We’ll offer tips on data formats and layouts that improve performance in various scenarios.
Dean Wampler is Principal Consultant at Think Big Analytics, specialists in “Big Data”, Machine Learning, and the Hadoop ecosystem. He speaks frequently at conferences on various big data and other programming topics.
Dean is the author of Functional Programming for Java Developers (O’Reilly, 2011), the co-author of Programming Scala (O’Reilly, 2009) and the co-author of the forthcoming Programming Hive, also from O’Reilly.
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