Big Data Application Architecture Q&A

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

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