Big Data Application Architecture Q&A

A Problem - Solution Approach

By Nitin Sawant , Himanshu Shah

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Big Data Application Architecture Pattern Recipes is a tailor made problem-solution approach answering key questions for software architects, solution designers, database professionals, CIOs and CXOs keeping in mind the various problems they encounter while dealing with big data application architecture patterns for clients in a range of industries.


Full Description

  • ISBN13: 978-1-4302-6292-3
  • 172 Pages
  • User Level: Intermediate to Advanced
  • Publication Date: December 22, 2013
  • Available eBook Formats: EPUB, MOBI, PDF
  • Print Book Price: $49.99
  • eBook Price: $34.99
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Full Description

Big Data Application Architecture Pattern Recipes provides an insight into heterogeneous infrastructures, databases, and visualization and analytics tools used for realizing the architectures of big data solutions. Its problem-solution approach helps in selecting the right architecture to solve the problem at hand.  In the process of reading through these problems, you will learn harness the power of new big data opportunities which various enterprises  use to attain real-time  profits.

Big Data Application Architecture Pattern Recipes answers one of the most critical questions of this time 'how do you select the best end-to-end architecture to solve your big data problem?'.

The book deals with various mission critical problems encountered by solution architects, consultants, and software architects while dealing with the myriad options available for implementing a typical solution, trying to extract insight from huge volumes of data in real–time and across multiple relational and non-relational data types for clients from industries like retail, telecommunication, banking, and insurance. The patterns in this book  provide the strong architectural foundation required to launch your next big data application.

The architectures for realizing these opportunities are based on relatively less expensive and heterogeneous infrastructures compared to the traditional monolithic and hugely expensive options that exist currently. This book describes and evaluates the benefits of heterogeneity which brings with it multiple options of solving the same problem, evaluation of trade-offs and validation of 'fitness-for-purpose' of the solution.

What you’ll learn

  • Major considerations in building a big data solution
  • Big data application architectures problems for specific industries
  • What are the components one needs to build and end-to-end big data solution?
  • Does one really need a real-time big data solution or an off-line analytics batch solution?
  • What are the operations and support architectures for a big data solution?
  • What are the scalability considerations, and options for a Hadoop installation?

Who this book is for

  • CIOs, CTOs, enterprise architects, and software architects
  • Consultants, solution architects, and information management (IM) analysts who want to architect a big data solution for their enterprise

Table of Contents

Table of Contents

  1. Introduction
  2. Big Data Application Architecture
  3. Ingestion and Streaming Patterns
  4. Data Storage Patterns
  5. Data Access Patterns
  6. Data Discovery and Analysis Patterns
  7. Visualization Patterns
  8. Deployment Patterns
  9. Big Data NFRs
  10. Big Data Case Studies
  11. Resources and Tools
  12. Appendix
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