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Viser: Big Data Fundamentals - Concepts, Drivers and Techniques

Big Data Fundamentals, 1. udgave

Big Data Fundamentals Vital Source e-bog

Thomas Erl, Wajid Khattak og Paul Buhler
(2015)
Pearson International
169,00 kr.
Leveres umiddelbart efter køb
Big Data Fundamentals, 1. udgave

Big Data Fundamentals Vital Source e-bog

Thomas Erl, Wajid Khattak og Paul Buhler
(2015)
Pearson International
139,00 kr.
Leveres umiddelbart efter køb
Big Data Fundamentals, 1. udgave

Big Data Fundamentals Vital Source e-bog

Thomas Erl, Wajid Khattak og Paul Buhler
(2015)
Pearson International
199,00 kr.
Leveres umiddelbart efter køb
Big Data Fundamentals - Concepts, Drivers and Techniques

Big Data Fundamentals

Concepts, Drivers and Techniques
Thomas Erl, Wajid Khattak og Paul Buhler
(2016)
Sprog: Engelsk
Pearson Education
339,00 kr.
Print on demand. Leveringstid vil være ca 2-3 uger.

Detaljer om varen

  • 1. Udgave
  • Vital Source 180 day rentals (dynamic pages)
  • Udgiver: Pearson International (December 2015)
  • Forfattere: Thomas Erl, Wajid Khattak og Paul Buhler
  • ISBN: 9780134291208R180
“This text should be required reading for everyone in contemporary business.” --Peter Woodhull, CEO, Modus21 “The one book that clearly describes and links Big Data concepts to business utility.” --Dr. Christopher Starr, PhD “Simply, this is the best Big Data book on the market!” --Sam Rostam, Cascadian IT Group “...one of the most contemporary approaches I’ve seen to Big Data fundamentals...” --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data science Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation Planning strategic, business-driven Big Data initiatives Addressing considerations such as data management, governance, and security Recognizing the 5 “V” characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value Clarifying Big Data’s relationships with OLTP, OLAP, ETL, data warehouses, and data marts Working with Big Data in structured, unstructured, semi-structured, and metadata formats Increasing value by integrating Big Data resources with corporate performance monitoring Understanding how Big Data leverages distributed and parallel processing Using NoSQL and other technologies to meet Big Data’s distinct data processing requirements Leveraging statistical approaches of quantitative and qualitative analysis Applying computational analysis methods, including machine learning
Licens varighed:
Bookshelf online: 180 dage fra købsdato.
Bookshelf appen: 180 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • 1. Udgave
  • Vital Source 90 day rentals (dynamic pages)
  • Udgiver: Pearson International (December 2015)
  • Forfattere: Thomas Erl, Wajid Khattak og Paul Buhler
  • ISBN: 9780134291208R90
“This text should be required reading for everyone in contemporary business.” --Peter Woodhull, CEO, Modus21 “The one book that clearly describes and links Big Data concepts to business utility.” --Dr. Christopher Starr, PhD “Simply, this is the best Big Data book on the market!” --Sam Rostam, Cascadian IT Group “...one of the most contemporary approaches I’ve seen to Big Data fundamentals...” --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data science Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation Planning strategic, business-driven Big Data initiatives Addressing considerations such as data management, governance, and security Recognizing the 5 “V” characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value Clarifying Big Data’s relationships with OLTP, OLAP, ETL, data warehouses, and data marts Working with Big Data in structured, unstructured, semi-structured, and metadata formats Increasing value by integrating Big Data resources with corporate performance monitoring Understanding how Big Data leverages distributed and parallel processing Using NoSQL and other technologies to meet Big Data’s distinct data processing requirements Leveraging statistical approaches of quantitative and qualitative analysis Applying computational analysis methods, including machine learning
Licens varighed:
Bookshelf online: 90 dage fra købsdato.
Bookshelf appen: 90 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • 1. Udgave
  • Vital Source 365 day rentals (dynamic pages)
  • Udgiver: Pearson International (December 2015)
  • Forfattere: Thomas Erl, Wajid Khattak og Paul Buhler
  • ISBN: 9780134291208R365
“This text should be required reading for everyone in contemporary business.” --Peter Woodhull, CEO, Modus21 “The one book that clearly describes and links Big Data concepts to business utility.” --Dr. Christopher Starr, PhD “Simply, this is the best Big Data book on the market!” --Sam Rostam, Cascadian IT Group “...one of the most contemporary approaches I’ve seen to Big Data fundamentals...” --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data science Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation Planning strategic, business-driven Big Data initiatives Addressing considerations such as data management, governance, and security Recognizing the 5 “V” characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value Clarifying Big Data’s relationships with OLTP, OLAP, ETL, data warehouses, and data marts Working with Big Data in structured, unstructured, semi-structured, and metadata formats Increasing value by integrating Big Data resources with corporate performance monitoring Understanding how Big Data leverages distributed and parallel processing Using NoSQL and other technologies to meet Big Data’s distinct data processing requirements Leveraging statistical approaches of quantitative and qualitative analysis Applying computational analysis methods, including machine learning
Licens varighed:
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: 5 år fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • Hardback: 240 sider
  • Udgiver: Pearson Education (Januar 2016)
  • Forfattere: Thomas Erl, Wajid Khattak og Paul Buhler
  • ISBN: 9780134291079
"This text should be required reading for everyone in contemporary business."
--Peter Woodhull, CEO, Modus21

"The one book that clearly describes and links Big Data concepts to business utility."
--Dr. Christopher Starr, PhD

"Simply, this is the best Big Data book on the market!"
--Sam Rostam, Cascadian IT Group

"...one of the most contemporary approaches I've seen to Big Data fundamentals..."
--Joshua M. Davis, PhD

The Definitive Plain-English Guide to Big Data for Business and Technology Professionals

Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams.

The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages.
  • Discovering Big Data's fundamental concepts and what makes it different from previous forms of data analysis and data science
  • Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation
  • Planning strategic, business-driven Big Data initiatives
  • Addressing considerations such as data management, governance, and security
  • Recognizing the 5 "V" characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value
  • Clarifying Big Data's relationships with OLTP, OLAP, ETL, data warehouses, and data marts
  • Working with Big Data in structured, unstructured, semi-structured, and metadata formats
  • Increasing value by integrating Big Data resources with corporate performance monitoring
  • Understanding how Big Data leverages distributed and parallel processing
  • Using NoSQL and other technologies to meet Big Data's distinct data processing requirements
  • Leveraging statistical approaches of quantitative and qualitative analysis
  • Applying computational analysis methods, including machine learning

Acknowledgments xvii Reader Services xviii
PART I: THE FUNDAMENTALS OF BIG DATA
Chapter 1: Understanding Big Data 3 Concepts and Terminology 5 Datasets 5 Data Analysis 6 Data Analytics 6 Descriptive Analytics 8 Diagnostic Analytics 9 Predictive Analytics 10 Prescriptive Analytics 11 Business Intelligence (BI) 12 Key Performance Indicators (KPI) 12 Big Data Characteristics 13 Volume 14 Velocity 14 Variety 15 Veracity 16 Value 16 Different Types of Data 17 Structured Data 18 Unstructured Data 19 Semi-structured Data 19 Metadata 20 Case Study Background 20 History 20 Technical Infrastructure and Automation Environment 21 Business Goals and Obstacles 22 Case Study Example 24 Identifying Data Characteristics 26 Volume 26 Velocity 26 Variety 26 Veracity 26 Value 27 Identifying Types of Data 27
Chapter 2: Business Motivations and Drivers for Big Data Adoption 29 Marketplace Dynamics 30 Business Architecture 33 Business Process Management 36 Information and Communications Technology 37 Data Analytics and Data Science 37 Digitization 38 Affordable Technology and Commodity Hardware 38 Social Media 39 Hyper-Connected Communities and Devices 40 Cloud Computing 40 Internet of Everything (IoE) 42 Case Study Example 43
Chapter 3: Big Data Adoption and Planning Considerations 47 Organization Prerequisites 49 Data Procurement 49 Privacy 49 Security 50 Provenance 51 Limited Realtime Support 52 Distinct Performance Challenges 53 Distinct Governance Requirements 53 Distinct Methodology 53 Clouds 54 Big Data Analytics Lifecycle 55 Business Case Evaluation 56 Data Identification 57 Data Acquisition and Filtering 58 Data Extraction 60 Data Validation and Cleansing 62 Data Aggregation and Representation 64 Data Analysis 66 Data Visualization 68 Utilization of Analysis Results 69 Case Study Example 71 Big Data Analytics Lifecycle 73 Business Case Evaluation 73 Data Identification 74 Data Acquisition and Filtering 74 Data Extraction 74 Data Validation and Cleansing 75 Data Aggregation and Representation 75 Data Analysis 75 Data Visualization 76 Utilization of Analysis Results 76
Chapter 4: Enterprise Technologies and Big Data Business Intelligence 77 Online Transaction Processing (OLTP) 78 Online Analytical Processing (OLAP) 79 Extract Transform Load (ETL) 79 Data Warehouses 80 Data Marts 81 Traditional BI 82 Ad-hoc Reports 82 Dashboards 82 Big Data BI 84 Traditional Data Visualization 84 Data Visualization for Big Data 85 Case Study Example 86 Enterprise Technology 86 Big Data Business Intelligence 87
PART II: STORING AND ANALYZING BIG DATA
Chapter 5: Big Data Storage Concepts 91 Clusters 93 File Systems and Distributed File Systems 93 NoSQL 94 Sharding 95 Replication 97 Master-Slave 98 Peer-to-Peer 100 Sharding and Replication 103 Combining Sharding and Master-Slave Replication 104 Combining Sharding and Peer-to-Peer Replication 105 CAP Theorem 106 ACID 108 BASE 113 Case Study Example 117
Chapter 6: Big Data Processing Concepts 119 Parallel Data Processing 120 Distributed Data Processing 121 Hadoop 122 Processing Workloads 122 Batch 123 Transactional 123 Cluster 124 Processing in Batch Mode 125 Batch Processing with MapReduce 125 Map and Reduce Tasks 126 Map 127 Combine 127 Partition 129 Shuffle and Sort 130 Reduce 131 A Simple MapReduce Example 133 Understanding MapReduce Algorithms 134 Processing in Realtime Mode 137 Speed Consistency Volume (SCV) 137 Event Stream Processing 140 Complex Event Processing 141 Realtime Big Data Processing and SCV 141 Realtime Big Data Processing and MapReduce 142 Case Study Example 143 Processing Workloads 143 Processing in Batch Mode 143 Processing in Realtime 144
Chapter 7: Big Data Storage Technology 145 On-Disk Storage Devices 147 Distributed File Systems 147 RDBMS Databases 149 NoSQL Databases 152 Characteristics 152 Rationale 153 Types 154 Key-Value 156 Document 157 Column-Family 159 Graph 160 NewSQL Databases 163 In-Memory Storage Devices 163 In-Memory Data Grids 166 Read-through 170 Write-through 170 Write-behind 172 Refresh-ahead 172 In-Memory Databases 175 Case Study Example 179
Chapter 8: Big Data Analysis Techniques 181 Quantitative Analysis 183 Qualitative Analysis 184 Data Mining 184 Statistical Analysis 184 A/B Testing 185 Correlation 186 Regression 188 Machine Learning 190 Classification (Supervised Machine Learning) 190 Clustering (Unsupervised Machine Learning) 191 Outlier Detection 192 Filtering 193 Semantic Analysis 195 Natural Language Processing 195 Text Analytics 196 Sentiment Analysis 197 Visual Analysis 198 Heat Maps 198 Time Series Plots
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