Tuesday, March 20, 2012

Analyzing Big Data with Hive

Solutions to big data-centric problems involve relaxed schemas, column-family-centric
storage, distributed filesystems, replication, and sometimes eventual consistency. The focus of these solutions is managing large, spare, denormalized data volumes, which is typically over a few terabytes in size. Often, when you are working with these big data stores you have specific, predefined ways of analyzing and accessing the data. Therefore, ad-hoc querying and rich query expressions aren’t a high priority and usually are not a part of the currently available solutions. In addition, many of these big data solutions involve products that are rather new and still rapidly evolving. These products haven’t matured to a point where they have been tested across a wide range of use cases and are far from being feature-complete. That said, they are good at what they are designed to do: manage big data.

In contrast to the new emerging big data solutions, the world of RDBMS has a repertoire of robust and mature tools for administering and querying data. The most prominent and important of these is SQL. It’s a powerful and convenient way to query data: to slice, dice, aggregate, and relate data points within a set. Therefore, as ironic as it may sound, the biggest missing piece in NoSQL is something like SQL.

In wake of the need to have SQL-like syntax and semantics and the ease of higher level abstractions, Hive and Pig come to the rescue. Apache Hive is a data-warehousing infrastructure built on top of Hadoop, and Apache Pig is a higher-level language for analyzing large amounts of data.

Before you start learning Hive, you need to install and set it up. Hive leverages a working Hadoop installation so install Hadoop first, if you haven’t already. Hadoop can be downloaded from hadoop.apache.org (read Appendix A if you need help with installing Hadoop). Currently, Hive works well with Java 1.6 and Hadoop 0.20.2 so make sure to get the right versions for these pieces of software. Hive works without problems on Mac OS X and any of the Linux variants. You may be able to run Hive using Cygwin on Windows but I do not cover any of that in this chapter. If you are on Windows and do not have access to a Mac OS X or Linux environment, consider using a virtual machine with VMware Player to get introduced to Hive.

Source of Information : NoSQL
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