Too Big to Ignore by Simon Phil

Author:Simon, Phil
Language: eng
Format: epub
ISBN: 9781118641866
Publisher: Wiley
Published: 2013-03-04T16:00:00+00:00

With these disclaimers out of the way, let’s move on to the specific Big Data solutions that many organizations are currently using.

PROJECTS, APPLICATIONS, AND PLATFORMS

It’s hard to think of Big Data solutions as applications in the traditional sense. For instance, Microsoft Excel and Outlook seem to better fit the definition of an application. Yes, each can do some pretty amazing things, but to compare them to Big Data software is analogous to saying that the Eiffel Tower is a just another building. It just doesn’t seem right.

Irrespective of moniker, though, Big Data doesn’t just happen by itself. Even an individual Big Data technique like A/B testing or sentiment analysis still necessitates some type of service, project, software program, or platform. This section examines some of the more mainstream ones.

Hadoop

Any conversation today about Big Data tools has to start with Apache Hadoop, the large collection of open-source projects that distributes and processes data. Collectively, the Hadoop stack and its different components allow organizations to store and make sense of vast amounts of semi-structured and unstructured data. GigaOM calls Hadoop “the world’s de facto Big Data platform.”4 Today, Yahoo!, Facebook57, LinkedIn, American Airlines, IBM, Twitter, and scores of other companies use Hadoop. Its popularity can be attributed to a number of factors, including these:

It can handle many different types and source of data, including structured, unstructured, log files, pictures, audio files, communications records, and e-mail.

It scales easily and across multiple servers (i.e., it is schema-less).

It has high fault tolerance.

It’s extremely flexible.

It’s an open-source project that has spawned its own ecosystem, a community that seeks to improve the product.

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