Big Data Imperatives [electronic resource] :  Enterprise Big Data Warehouse, BI Implementations and Analytics /
Mohanty, Soumendra.author.
Jagadeesh, Madhu.author.
Srivatsa, Harsha.author.
Big Data Imperatives, focuses on resolving the key questions on everyone’s mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use. This book addresses the following big data characteristics: Very large, distributed aggregations of loosely structured data – often incomplete and inaccessible Petabytes/Exabytes of data Millions/billions of people providing/contributing to the context behind the data Flat schema's with few complex interrelationships Involves time-stamped events Made up of incomplete data Includes connections between data elements that must be probabilistically inferred Big Data Imperatives explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability. Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.
Description based upon print version of record.
""Contents at a Glance""; ""Contents""; ""Preface""; ""About the Authors""; ""About the Technical Reviewer""; ""Acknowledgments""; ""Introduction""; ""Chapter 1: â€œBig Dataâ€? in the Enterprise""; ""Search at Scale""; ""Multimedia Content""; ""Sentiment Analysis""; ""Enriching and Contextualizing Data""; ""Data Discovery or Exploratory Analytics""; ""Operational Analytics or Embedded Analytics""; ""Realizing Opportunities from Big Data""; ""Innovation""; ""Acceleration""; ""Collaboration""; ""New Business Models""; ""New Revenue Growth Opportunities""; ""Taming the â€œBig Dataâ€?""
""Where Will Big Data and Analytics Create Advantages for the Company?""""How Should You Organize to Capture the Benefits of Big Data and Analytics?""; ""What Technology Investments Can Enable the Analytics Capabilities?""; ""How Do You Get Started on the Big Data Journey?""; ""End Points""; ""References""; ""Chapter 2: The New Information Management Paradigm""; ""What Is Enterprise Information Management?""; ""New Approach to Enterprise Information Management for Big Data""; ""New capabilities needed for big data""
""Leading practices of enterprise information management for big data platforms""""Implications of Big Data to Enterprise IT?""; ""Map-reduce""; ""Storage""; ""Query""; ""End Points""; ""References""; ""Chapter 3: Big Data Implications for Industry""; ""The Opportunity""; ""Big Data Use Cases by Industry Vertical""; ""Big Data Analytics for Telecom""; ""Big Data Analytics for Banking""; ""Big Data Analytics for Insurance""; ""Analytics Domains in Insurance""; ""Big Data Analytics for Retail""; ""Big Data Analytics for Health Care""; ""Big Data Analytics for IT/Operations""; ""End Points""
""References""""Chapter 4: Emerging Database Landscapeimages ""; ""The Database Evolution""; ""The Scale-Out Architecture""; ""The Relational Database and the Non-Relational Database""; ""OldSQL, NewSQL, and the Emerging NoSQL""; ""The Influence of Map-Reduce and Hadoop""; ""Key Value Stores and Distributed Hash Tables""; ""XML Defined Data""; ""Unstructured Data as Un-modeled Data""; ""Database Workloads""; ""Workload Characteristics""; ""Implication of Big Data Scale on Data Processing""; ""Database Technologies for Managing the Workloads""; ""Hardware Architectures and Databases""
""Columnar Databases""""Combination/Workload Challenges""; ""Requirements for the Next Generation Data Warehouses""; ""Polyglot Persistence: The Next Generation Database Architecture""; ""How Digg is Built Using Polyglot Persistence""; ""Use Case: E-commerce Retail Application""; ""End Points""; ""References""; ""Chapter 5: Application Architectures for Big Data and Analytics""; ""Big Data Warehouse and Analytics""; ""Data Design Principles for Big Data Solutions""; ""Big Data Warehouse System Requirements and Hybrid Architectures""; ""Enterprise Data Platform Ecosystem â€? BDW and EDW""
""How does Traditional Data Warehouse processes map to tools in Hadoop Environment?""
Big Data Imperatives, focuses on resolving the key questions on everyone’s mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use. This book addresses the following big data characteristics: Very large, distributed aggregations of loosely structured data – often incomplete and inaccessible Petabytes/Exabytes of data Millions/billions of people providing/contributing to the context behind the data Flat schema's with few complex interrelationships Involves time-stamped events Made up of incomplete data Includes connections between data elements that must be probabilistically inferred Big Data Imperatives explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability. Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.
Big data.
Application software.
Computers.
Big Data.
Computer Appl. in Administrative Data Processing.
Information Systems and Communication Service.