Über den Autor
As Managing Director, Technology, Nitin Sawant is the practicelead for technology architecture, BPM, SOA, and cloud at Accenture India. He isan Accenture certified master technology architect (CMTA), leading variousinitiatives in the emerging technologies of cloud and big data. Nitin has over17 years of technology experience in developing, designing, and architectingcomplex enterprise scale systems based on Java, JEE, SOA, and BPM technologies.He received his master s degree in technology in software engineering from theInstitute of System Science, National University of Singapore. He graduatedwith a bachelor s degree in electronics engineering from Bombay University. Heis a certified CISSP, CEH, and IBM-certified SOA solutions architect. Nitin hasfiled three patents in the SOA BPM space and is currently pursuing his PhD inBPM security from BITS Pilani, India.
- Big Data Application Architecture
- Ingestion and Streaming Patterns
- Data Storage Patterns
- Data Access Patterns
- Data Discovery and Analysis Patterns
- Visualization Patterns
- Deployment Patterns
- Big Data NFRs
- Big Data Case Studies
- Resources and Tools
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.
- Use cases for big data specific to architectures like Walmart and Ebay.
- Covers the end-to-end application architecture required to realize the big data solution covering the analytics and visualization aspects in addition to hadoop and not just focus on providing design patterns in the map-reduce or hadoop area only.
- The book can be used as reference to search the closest big data pattern and quickly use it to start building the application which corresponds to your problem statement.
- Complete list of application architectures used by peers for specific industries.