COVER STORY BIG DATA
Donald Feinberg, a distinguished analyst at industry watcher Gartner. “Most large organisations have one or
two mathematicians or statisticians who would be capable of writing MapReduce algorithms,” but commercialised products from vendors such as DataStax and Cloudscale make these technologies a realistic proposition within the enterprise, he says. Traditional data management vendors
have been quick to spot an opportunity. Over the past year, these vendors have been on a big data acquisition spree: IBM snapped up Netezza for $1.6 billion; HP and EMC bought the privately held Vertica and GreenPlum for undisclosed fees; and in
March 2011, Teradata spent $263 million on the 89% of in-database analytics start-up Aster Data it didn’t already own. The combination of big data
technologies and established enterprise IT vendors could prove a potent one. The strength of today’s big data tools is their ability to manage and analyse data that may not fit the existing data management infrastructure, says IBM’s Bhambhri. But in order to create something valuable, they need to be able to integrate that new information with existing data repositories within the enterprise. “That way, businesses are really finding out things about their customers that they could never have known before,” she says.
Despite its industry-shaking potential,
there is one rather prosaic reason that companies are contemplating big data, Gartner’s Feinberg says. Until recently, few firms could justify the
outlay needed to store gigantic volumes of data, knowing that much of it would be essentially useless. “Today, I can go online and buy a server with 1 terabyte of RAM for less than $100,000,” says Feinberg. “Big data is suddenly affordable.”
Article by Gareth Morgan
IAeditorial@vitessemedia.co.uk Further reading:
www.information-age.com/im
THE FOUNDATIONS OF BIG DATA Hadoop
NoSQL
In December 2004, Google Labs published papers detailing its cluster computing algorithm and its file system. Software engineer Doug Cutting used these papers to create an open source framework for data- intensive, scalable, distributed computing, named Hadoop. The framework was given an additional boost in June 2009, when a prominent user, Yahoo, made the source code to the version of Hadoop it runs in its data centres publicly available. The next version of Apache Hadoop, expected later in 2011, aims to improve the utilisation, scheduling and management of resources.
MapReduce A Google-patented software framework for distributed processing of large data sets on compute clusters. Hadoop MapReduce is an open source volunteer project under the Apache Software Foundation that was inspired by Google’s original paper.
A set of non-relational database management systems, popular with many big data advocates. Cassandra is one example, and was developed by social networking giant Facebook to handle its Inbox search feature. It is designed to handle massive amounts of data spread out across many commodity servers while providing a highly available service with no single point of failure.
Hive
Another Facebook contribution to the big data toolset, Hive is a data warehouse infrastructure built on top of Hadoop. It provides tools to enable easy data extract, transform and load functions. This provides a structure for the data that makes it possible to query and analyse the volume of data stored in Hadoop files. It was originally designed to help Facebook deal with explosive growth in its multi-petabyte data warehouse.
18 INFORMATIONAGE APRIL2011
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