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Big data analytics : from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph
| Big data analytics : from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph |
| Autore | Loshin David |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Waltham, Mass., : Academic Press, 2013 |
| Descrizione fisica | 1 online resource (143 p.) |
| Disciplina | 005.7565 |
| Soggetto topico |
Business intelligence
Information technology Electronic data processing Strategic planning |
| Soggetto genere / forma | Electronic books. |
| ISBN | 0-12-418664-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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 |
| Record Nr. | UNINA-9910453048003321 |
Loshin David
|
||
| Waltham, Mass., : Academic Press, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Big data analytics : from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph / / David Loshin
| Big data analytics : from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph / / David Loshin |
| Autore | Loshin David |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Waltham, Mass., : Academic Press, 2013 |
| Descrizione fisica | 1 online resource (xx, 120 pages) : illustrations (some color) |
| Disciplina | 005.7565 |
| Collana | Gale eBooks |
| Soggetto topico |
Information technology - Management
Big data Data mining |
| ISBN | 0-12-418664-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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 |
| Record Nr. | UNINA-9910790412303321 |
|
Loshin David
|
||
| Waltham, Mass., : Academic Press, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Big data analytics : from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph / / David Loshin
| Big data analytics : from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph / / David Loshin |
| Autore | Loshin David |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Waltham, Mass., : Academic Press, 2013 |
| Descrizione fisica | 1 online resource (xx, 120 pages) : illustrations (some color) |
| Disciplina | 005.7565 |
| Collana | Gale eBooks |
| Soggetto topico |
Information technology - Management
Big data Data mining |
| ISBN | 0-12-418664-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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 |
| Record Nr. | UNINA-9910821463203321 |
|
Loshin David
|
||
| Waltham, Mass., : Academic Press, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||