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Big Data Appliances for In-Memory Computing: A Real-World Research Guide for Corporations to Tame and Wrangle Their Data

Pulipaka, Ganapathi

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ISBN 10: 0692599576 / ISBN 13: 9780692599570
Published by High Performance Computing Institute of Technology
Used Condition: Fine Soft cover
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Bibliographic Details

Title: Big Data Appliances for In-Memory Computing:...

Publisher: High Performance Computing Institute of Technology

Binding: PAPERBACK

Book Condition:Fine

About this title

Synopsis:

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This book is a scientific expedition to research and explore the enterprise-grade big data appliances with blended OLTP and OLAP capabilities. Enterprise database systems with the aid of big data analytics create an intelligent ecosystem by taming and wrangling the data coming from extreme-disparate sources of structured and unstructured channels with massive parallelization techniques to discover, visualize, predict, and action the patterns and trends of mashups of big data. The book delves deeper into the research results of industry relevant case studies with the disruption of in-memory computing platform innovation that diffuses high-speed computing and dynamic performance for business applications and explores how these modern big data analytics tools shape the future of aerospace, automotive, consumer goods and beverages, healthcare, government services, high tech, and public sector industries.

From the Author:

Big Data Appliances has started creating a buzz in the big data and in-memory computing platform terrain. 
Big data is creating a new technology revolution in the 21st century, teeming with lifestyle devices impacting all of society for building an intelligent lifestyle and spawning new business models. Big data delivers a next wave generation of connected homes, digital economy, and personalized healthcare. In his new book, "Big Data Appliances for In-Memory Computing - A Real-world Research Guide for Corporations to Tame and Wrangle Their Data," Ganapathi Pulipaka takes the audience on an incredible scientific expedition to research and explores the enterprise-grade big data appliances with blended OLTP and OLAP capabilities.
Enterprise database systems with the aid of big data analytics create an intelligent ecosystem by taming and wrangling the data coming from extreme-disparate sources of structured and unstructured data channels with massive parallelization techniques to explore, conceptualize, envisage, presage, and action the patterns and trends of mashups of big data. The book delves deeper into the research results of industry relevant case studies with the disruption of in-memory computing platform innovation and machine intelligence that diffuses high-speed computing and dynamic performance for business applications and explores how these modern big data analytics tools shape the future of aerospace, automotive, consumer goods and beverages, healthcare, government services, high tech, and public sector industries with human-centered design.
Big Data reached Trough of Disillusionment in 2014. 
Many industrial research experts believed that it would take two to five years over an extended arc of time for big data to scale along Gartner's Darwinistic hype curve and reach Plateau of Productivity. Gartner listed only 37 emerging technologies in 2015. Big data in 2015 is a reality and beyond Plateau of Productivity. It disappeared entirely from hype cycle. Gartner dropped big data off of the hype cycle for emerging technologies in 2015. A broad class of big data appliances such as IBM DB2 Blu DB2 Acceleration, Oracle Exadata In-Memory Machine, and SAP HANA demonstrated the speed, agility, and off-the-charts dynamic performance. The research study explores the big-data appliances to resolve complex business conundrums of organizations in academics, aerospace, automotive, consumer goods and beverages, healthcare, government services, high tech, and public sector industries by diffusing the speed and disruption of in-memory computing.
Organizations are looking for operational control of their data by maximizing enterprise productivity and defining their objectives for their distributed data on in-memory computing platform intertwined with modern big data appliances deployed on infrastructure that meets their economies of flash and economies of the cloud. An implementation without visions of reality will not yield outstanding results for the organization. Sometimes the objects in the rear-view mirror may appear closer than they are. It is critical for the organization to identify the signals and separate them from noise by mining the data and categorizing the characteristics of business needs when implementing big data appliances on the in-memory computing platform to turn the raw data into actionable results. The technological features aid the enterprise information systems to achieve the objectives. Riding on the new wave of predictive analytics and prescriptive analytics teeming big data tsunami, there is no room for a bevy of industries for guesswork, intuition or hunch for making executive decisions.
In a data-driven economy, lack of business requirements assessment will lead to continuous usage of traditional RDBMS and ETL tools with limited parallelization capabilities in the organizations addressing big data problems. In other instances, massive leverage of big data tools to resolve enterprise information systems problems in the organization can bring the business to a grinding halt as enterprise systems require high-speed computing. Therefore, it is of paramount importance to define the success of the project before making decisions to choose the right set of big data technology tools and techniques. Another important aspect is to set up a data Center of Excellence for the organization. Creation of top-down approach for enterprise data warehousing strategy is fundamental building blocks for big data competency center.

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