Review: Apache Hive brings real-time queries to Hadoop

Hive's SQL-like query language and vastly improved speed on huge data sets make it the perfect partner for an enterprise data warehouse

Contributing Editor, InfoWorld |

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Organizations or departments without a data warehouse can start with Hive to get a feel for the value of data analytics while keeping startup costs to a minimum. Although Hive doesn't offer a complete data warehouse solution, it does make a great, low-cost, large-scale operational data store with a fair set of analytics tools. If your analytics needs outgrow those satisfied by Hive, many traditional data warehouse vendors offer connectors and tools to bring the data into the warehouse, preserving your investments.

Until the playing field levels, companies making the best decisions -- decisions based on data and analytics -- will have a competitive advantage. Hive offers near linear scalability in query processing, an order of magnitude better price/performance ratio than traditional enterprise data warehouses, and a low barrier to entry. With 10TB enterprise data warehouse solutions costing around $1 million, managing large unstructured data sets with Hive makes a lot of sense.

Apache Hive at a glance

 
Pros
  • Good compatibility with SQL
  • Multiple schemas can be projected on the same data -- no ETL required
  • Works with popular query tools that connect via JDBC and ODBC
  • Able to query very large data sets interactively
Cons
  • DDL requires a bit of learning and different thinking
  • Tuning Hive is a different skill set than tuning a RDBMS
Platforms Works with Hadoop 0.20.x, 0.23.x.y, 1.x.y, 2.x.y
Cost Free open source under the Apache License, Version 2.0

This article, "Review: Apache Hive brings real-time queries to Hadoop," was originally published at InfoWorld.com. Follow the latest developments in big data and open source at InfoWorld.com. For the latest business technology news, follow InfoWorld.com on Twitter.

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Steve Nuñez is technologist-turned-executive currently working as a management consultant helping senior executives apply artificial intelligence in a practical, cost effective manner. He takes an incremental approach to AI adoption, emphasizing the organizational change required for analytics and A.I. to become part of the company DNA.

Before moving to consulting Steve led the professional services and technical pre-sales organizations in Asia Pacific for MapR, a “big data unicorn” acquired by HP Enterprise. While leading the field organization, Steve served clients including Toyota, Bank of China, Philips, Samsung, and the government of India in their bio ID program.

Steve has been a contributing editor and reviewer for InfoWorld since 1999.

Copyright © 2014 IDG Communications, Inc.

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