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Record Nr. |
UNINA9910790412303321 |
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Autore |
Loshin David |
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Titolo |
Big data analytics : from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph / / David Loshin |
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Pubbl/distr/stampa |
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Waltham, Mass., : Academic Press, 2013 |
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Waltham, Mass. : , : Morgan Kaufmann, , 2013 |
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ISBN |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (xx, 120 pages) : illustrations (some color) |
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Collana |
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Disciplina |
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Soggetti |
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Information technology - Management |
Big data |
Data mining |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Front Cover; Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph; Copyright Page; Contents; Foreword; Preface; Introduction; The Challenge of Adopting New Technology; What This Book Is; Why You Should Be Reading This Book; Our Approach to Knowledge Transfer; Contact Me; Acknowledgments; 1 Market and Business Drivers for Big Data Analytics; 1.1 Separating the Big Data Reality from Hype; 1.2 Understanding the Business Drivers; 1.3 Lowering the Barrier to Entry; 1.4 Considerations; 1.5 Thought Exercises |
2 Business Problems Suited to Big Data Analytics2.1 Validating (Against) the Hype: Organizational Fitness; 2.2 The Promotion of the Value of Big Data; 2.3 Big Data Use Cases; 2.4 Characteristics of Big Data Applications; 2.5 Perception and Quantification of Value; 2.6 Forward Thinking About Value; 2.7 Thought Exercises; 3 Achieving Organizational Alignment for Big Data Analytics; 3.1 Two Key Questions; 3.2 The Historical Perspective to Reporting and Analytics; 3.3 The Culture Clash Challenge; 3.4 Considering Aspects of Adopting Big Data Technology; 3.5 Involving the Right Decision Makers |
3.6 Roles of Organizational Alignment3.7 Thought Exercises; 4 Developing a Strategy for Integrating Big Data Analytics into the Enterprise; 4.1 Deciding What, How, and When Big Data Technologies |
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Are Right for You; 4.2 The Strategic Plan for Technology Adoption; 4.3 Standardize Practices for Soliciting Business User Expectations; 4.4 Acceptability for Adoption: Clarify Go/No-Go Criteria; 4.5 Prepare the Data Environment for Massive Scalability; 4.6 Promote Data Reuse; 4.7 Institute Proper Levels of Oversight and Governance; 4.8 Provide a Governed Process for Mainstreaming Technology |
4.9 Considerations for Enterprise Integration4.10 Thought Exercises; 5 Data Governance for Big Data Analytics: Considerations for Data Policies and Processes; 5.1 The Evolution of Data Governance; 5.2 Big Data and Data Governance; 5.3 The Difference with Big Datasets; 5.4 Big Data Oversight: Five Key Concepts; 5.4.1 Managing Consumer Data Expectations; 5.4.2 Identifying the Critical Dimensions of Data Quality; 5.4.3 Consistency of Metadata and Reference Data for Entity Extraction; 5.4.4 Repurposing and Reinterpretation; 5.4.5 Data Enrichment and Enhancement; 5.5 Considerations |
5.6 Thought Exercises6 Introduction to High-Performance Appliances for Big Data Management; 6.1 Use Cases; 6.2 Storage Considerations: Infrastructure Bedrock for the Data Lifecycle; 6.3 Big Data Appliances: Hardware and Software Tuned for Analytics; 6.4 Architectural Choices; 6.5 Considering Performance Characteristics; 6.6 Row- Versus Column-Oriented Data Layouts and Application Performance; 6.7 Considering Platform Alternatives; 6.8 Thought Exercises; 7 Big Data Tools and Techniques; 7.1 Understanding Big Data Storage; 7.2 A General Overview of High-Performance Architecture; 7.3 HDFS |
7.4 Mapreduce and Yarn |
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Sommario/riassunto |
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Big Data Analytics will assist managers in providing an overview of the drivers for introducing big data technology into the organization and for understanding the types of business problems best suited to big data analytics solutions, understanding the value drivers and benefits, strategic planning, developing a pilot, and eventually planning to integrate back into production within the enterprise. Guides the reader in assessing the opportunities and value propositionOverview of big data hardware and software architecturesPresents a variety of te |
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