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Text Analytics Toolkit) are supported for use on Cloudera's distribution of
Hadoop components. (Other distributions are currently under consideration.)
Sometimes we field questions about IBM's commitment to open source.
This kind of catches us off-guard, not because the question is hard—on the
contrary, we're caught off-guard because we realize that perhaps IBM hasn't
done a good enough job of evangelizing what it does in this space, so we
decided to do it here (albeit briefly). For the record, IBM is 100 percent com-
is 100 percent com-
mitted to open source, and IBM is 100 percent committed to Hadoop.
A number of our engineers contribute pieces of code to the Apache Hadoop
open source project and its associated ecosystem (they are called
committers
in the open source world). IBM has a long history of inventing technologies
and donating them to the open source community as well; examples include
Apache Derby, Apache Geronimo, Apache Jakarta, DRDA, XERCES; the list
goes on and on. The
de facto
tool set for open source, Eclipse, came from IBM.
Text analytics is a major use case around Big Data, and IBM contributed the
Unstructured Information Management Architecture (UIMA). Search is a Big
Data prerequisite enabler, and IBM is a major contributor to Lucene (search
technology that was showcased in the winning Watson technology on the
television program
Jeopardy!
). We don't have the space to detail IBM's commit-
ment to open source in this topic, but we think we made our point.
IBM has also built a strong ecosystem of solution providers in the Big Data
space. Currently, its partners—including technology vendors and system
integrators that are trained and certified on the IBM Big Data platform—
number in the triple digits.
3. Support Multiple Entry Points to Big Data
Big Data technologies can solve multiple business problems for an organiza-
tion. As a result, organizations often grapple with the best approach to adop-
tion. As practitioners, we sometimes see IT organizations embark on a Big
Data initiative as though it were a science experiment in search of a problem;
and we'll tell you, in our experience, lack of focus and unclear expectations
typically lead to unfavorable outcomes (it's not good for job security). In our
opinion, the most successful Big Data projects start with a clear identification
of a business problem, or a pain point, followed by the application of appro-
priate technology to address that problem. After all, we've yet to see a client
