Analyzing Big Data with Hive
WHAT’S IN THIS CHAPTER?
- Introducing Apache Hive, a data warehousing infrastructure built on top of Hadoop
- Learning Hive with the help of examples
- Exploring Hive commands syntax and semantics
- Using Hive to query the MovieLens data set
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, ...