Big Data Application Architecture Q & A: A Problem-Solution Approach

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?

About the Authors

Nitin Sawant is managing director, technology, and is the practice lead for technology architecture for BPM, SOA, and cloud at Accenture India. He is an Accenture certified master technology architect (CMTA), leading various initiatives in the emerging technologies of cloud and big data. Nitin has over 17 years of technology experience in developing, designing, and architecting complex enterprise-scale systems based on Java, JEE, SOA, and BPM technologies. He received his master's degree in technology in software engineering from the Institute of System Science, National University of Singapore. He graduated with a bachelor's degree in electronics engineering from Bombay University. He is a certified CISSP, CEH, and IBM-certified SOA solutions architect. Nitin has filed three patents in the SOA-BPM space and is currently pursuing his PHD in BPM security from BITS Pilani, India.

Himanshu Shah is an Accenture senior technology architect with 14 years of IT experience and currently leads the Big Data (Hadoop) Capability in Accenture India Delivery Centre. Himanshu has acted as an enterprise architect for projects involving cloud computing, operations architecture and big data. Himanshu has worked extensively in custom development of JEE based architecture for multiple clients in various Industries including telecom, retail and the insurance domain. Himanshu also has expertise in ITIL operations architecture. Himanshu has been part of Java, Platform Cloud, and SOA Centre of Excellences within Accenture.