Tuesday, December 03, 2013

Big Data Analytics From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph

Big Data Analytics is the process of examining large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other useful information. It's helpful with companies to make better business decisions.
What is Big Data Analytics?
Big Data Analytics From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph By David Loshin. This book helps readers to understand about Big Data is, why it can add value, what types of
problems are suited to a big data approach, and how to properly plan
to determine the need, align the right people in the organization, and
develop a strategic plan for integration.
It has 11 chapters. It will good, if readers can read some chapters online... Anyway, readers will see:
Chapter 1: We consider the market conditions that have enabled broad acceptance of big data analytics, including commoditization of hardware and software, increased data volumes, growing variation in types of data assets for analysis, different methods for data delivery, and increased expectations for real-time integration of analytical results into operational processes.
Chapter 2: In this chapter, we look at the characteristics of business problems that traditionally have required resources that exceeded the enterprises’ scopes, yet are suited to solutions that can take advantage of the big data platforms (either dedicated hardware or virtualized/cloud based).
Chapter 3: Who in the organization needs to be involved in the process of acquiring, proving, and deploying big data solutions? And what are their roles and responsibilities? This chapter looks at the adoption of new technology and how the organization must align to integrate into the system development life cycle.
Chapter 4: This chapter expands on the previous one by looking at some key issues that often plague new technology adoption and show that the key issues are not new ones and that there is likely to be organizational knowledge that can help in fleshing out a reasonable strategic plan.
Chapter 5: In this chapter, we look at the need for oversight and governance for the data, especially when those developing big data applications often bypass traditional IT and data management channels.
Chapter 6: In this chapter, we look at specialty-hardware designed for analytics and how they are engineered to accommodate large data sets.
Chapter 7: This chapter discusses and provides a high-level overview of tool suites such as Hadoop.
Chapter 8: This chapter examines the MapReduce programming model.
Chapter 9: In this chapter, we look at a variety of alternative methods of data management methods that are being adopted for big data application development.
Chapter 10: This chapter looks at business problems suited for graph analytics, what differentiates the problems from traditional approaches and considerations for discovery versus search analyses.
Chapter 11: This short final chapter reviews best practices for incrementally adopting big data into the enterprise. 
In a book, it assists readers about Big Data. It still gives readers about exercises in each chapter, that will help readers think and imagine about big picture for each topic and then able to use idea/knowledge what they read in their work.

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