Analytics for Enterprise Class Hadoop and Streaming Data
Analytics for Enterprise Class Hadoop and Streaming Data
Analytics for Enterprise Class Hadoop and Streaming Data
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xxviii About this Book<br />
world-class researchers, mathematicians, statisticians, <strong>and</strong> more: there’s lots<br />
of this caliber talent in the halls of IBM, many working on Big <strong>Data</strong> problems.<br />
Think Watson (famous <strong>for</strong> its winning Jeopardy! per<strong>for</strong>mance) as a proof<br />
point of what IBM is capable of providing. Of course, you’re going to want<br />
support <strong>for</strong> your Big <strong>Data</strong> plat<strong>for</strong>m, <strong>and</strong> who can provide direct-to-engineer<br />
support, around the world, in a 24×7 manner? What are you going to do with<br />
your Big <strong>Data</strong>? Analyze it! The lineage of IBM’s data analysis plat<strong>for</strong>ms (SPSS,<br />
Cognos, Smart <strong>Analytics</strong> Systems, Netezza, text annotators, speech-to-text,<br />
<strong>and</strong> so much more—IBM has spent over $14 billion in the last five years on<br />
analytic acquisitions alone) offer immense opportunity <strong>for</strong> year-after-year<br />
extensions to its Big <strong>Data</strong> plat<strong>for</strong>m.<br />
Of course we would be remiss not to mention how dedicated IBM is to the<br />
open source community in general. IBM has a rich heritage of supporting<br />
open source. Contributions such as the de facto st<strong>and</strong>ard integrated development<br />
environment (IDE) used in open source—Eclipse, Unstructured In<strong>for</strong>mation<br />
Management Architecture (UIMA), Apache Derby, Lucene, XQuery,<br />
SQL, <strong>and</strong> Xerces XML processor—are but a few of the too many to mention.<br />
We want to make one thing very clear—IBM is committed to <strong>Hadoop</strong> open<br />
source. In fact, Jaql (you will learn about this in Chapter 4) was donated to<br />
the open source <strong>Hadoop</strong> community by IBM. Moreover, IBM is continually<br />
working on additional technologies <strong>for</strong> potential <strong>Hadoop</strong>-related donations.<br />
Our development labs have <strong>Hadoop</strong> committers that work alongside other<br />
<strong>Hadoop</strong> committers from Facebook, LinkedIn, <strong>and</strong> more. Finally, you are<br />
likely to find one of our developers on any <strong>Hadoop</strong> <strong>for</strong>um. We believe IBM’s<br />
commitment to open source <strong>Hadoop</strong>, combined with its vast intellectual<br />
property <strong>and</strong> research around enterprise needs <strong>and</strong> analytics, delivers a true<br />
Big <strong>Data</strong> plat<strong>for</strong>m.<br />
Part II—Big <strong>Data</strong>: From the Technology Perspective starts by giving you<br />
some basics about Big <strong>Data</strong> open source technologies in Chapter 4. This chapter<br />
lays the “ground floor” with respect to open source technologies that are<br />
synonymous with Big <strong>Data</strong>—the most common being <strong>Hadoop</strong> (an Apache toplevel<br />
project whose execution engine is behind the Big <strong>Data</strong> movement).<br />
You’re not going to be a <strong>Hadoop</strong> expert after reading this chapter, but you’re<br />
going to have a basis <strong>for</strong> underst<strong>and</strong>ing such terms as Pig, Hive, HDFS,<br />
MapReduce, <strong>and</strong> ZooKeeper, among others.
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