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 Imperativesdescribes 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.
What you’ll learn
- Understanding the technology, implementation of big data platforms and their usage for analytics
- Big data architectures
- Big data design patterns
- Implementation best practices
Who this book is for
This book is designed for IT professionals, data warehousing, business intelligence professionals, data analysis professionals, architects, developers and business users.
Table of Contents
- The New Information ManagementParadigm
- Big Data's Implication for Businesses
- Big Data Implications for Information Management
- Defining Big Data Architecture Characteristics
- Co-Existent Architectures
- Data Quality for Big Data
- Data Security and Privacy Considerations for Big Data
- Big Data and Analytics
- Big Data Implications for Practitioners
|Product dimensions:||6.00(w) x 8.90(h) x 0.60(d)|
About the Author
His functional expertise ranges from Big Data Analytics, BI Architectures, Data Warehouse, CRM/Customer Insight, Supply Chain Analytics, Marketing Insights, & MDM.
Madhu Jagadeesh is a Senior Manager and leads Sales Enablement for Accenture s Data warehousing business intelligence practice in India. She has over 15 years of data warehousing business intelligence project management and solution development experience in both Systems Integration and Application outsourcing across various industry groups Madhu has extensive experience in End to End Solution Architecting and delivery of Complex BI engagements.
Her Functional expertise ranges from Big Data Analytics, BI Architectures, Data Warehouse, CRM/Customer Insight, Supply Chain Analytics, & Marketing Insights.
Harsha Srivatsa is an experienced Manager with Accenture s Information Management practice with 12+ years experience across multiple industry verticals ranging from Hi-Tech, Financial Services, Retail and Healthcare. He has deep technical and functional skills in architecting and delivering complex initiatives involving Business Intelligence, Information Management , ERP implementation, Master Data Management, Data Quality, Data Integration and Customer Analytics.
His functional expertise ranges from Big Data Processing, Data Visualization, Master Data Management, Data Quality, & Data Governance to CRM/Customer Insight, BI Architectures and Reporting, and Data Warehouse implementation.