Integrating Hadoop - Paperback
$24.95
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Integrating Hadoop - Paperback
$24.95
by William McKnight (Author), Jake Dolezal (Author)
Integrating Hadoop leverages the discipline of data integration and applies it to the Hadoop open-source software framework for storing data on clusters of commodity hardware. It is packed with the need-to-know for managers, architects, designers, and developers responsible for populating Hadoop in the enterprise, allowing you to harness big data and do it in such a way that the solution:
- Complies with (and even extends) enterprise standards
- Integrates seamlessly with the existing information infrastructure
- Fills a critical role within enterprise architecture.
Integrating Hadoop covers the gamut of the setup, architecture and possibilities for Hadoop in the organization, including:
- Supporting an enterprise information strategy
- Organizing for a successful Hadoop rollout
- Loading and extracting of data in Hadoop
- Managing Hadoop data once it's in the cluster
- Utilizing Spark, streaming data, and master data in Hadoop processes - examples are provided to reinforce concepts.
Front Jacket
Integrating Hadoop leverages the discipline of data integration and applies it to the Hadoop open-source software framework for storing data on clusters of commodity hardware. It is packed with the need-to-know for managers, architects, designers, and developers responsible for populating Hadoop in the enterprise, allowing you to harness big data and do it in such a way that the solution: ¢¢Complies with (and even extends) enterprise standards ¢¢Integrates seamlessly with the existing information infrastructure ¢¢Fills a critical role within enterprise architecture. Integrating Hadoop covers the gamut of the setup, architecture and possibilities for Hadoop in the organization, including: ¢¢Supporting an enterprise information strategy ¢¢Organizing for a successful Hadoop rollout ¢¢Loading and extracting of data in Hadoop ¢¢Managing Hadoop data once it's in the cluster ¢¢Utilizing Spark, streaming data, and master data in Hadoop processes - examples are provided to reinforce concepts.
Number of Pages: 126
Dimensions: 0.3 x 8.9 x 5.9 IN
Publication Date: September 30, 2016
Details
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