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
- Contents at a Glance
- About the Authors
- About the Technical Reviewer
CHAPTER 1: Big Data Introduction
- Why Big Data
- Aspects of Big Data
- How Big Data Differs from Traditional BI
- How Big Is the Opportunity?
- Deriving Insight from Data
- Cloud Enabled Big Data
- Structured vs. Unstructured Data
- Analytics in the Big Data World
- Big Data Challenges
- Defining a Reference Architecture
- Need for Architecture Patterns
- 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
- Case Study: Mainframe to Hadoop-Based NoSQL Database
- Case Study: Geo-Redundancy and Near-Real-Time Data Ingestion
- Case Study: Recommendation Engine
- Case Study: Video-Streaming Analytics
- Case Study: Sentiment Analysis and Log Processing
- Case Study: Real-Time Traffic Monitoring
- Case Study: Data Exploration for Suspicious Behavior on a Stock Exchange
- Case Study: Environment Change Detection
CHAPTER 11: Resources, References, and Tools
- Big Data Product Catalog
- Hadoop Distributions
- In-memory Hadoop
- Hadoop Alternatives
- Hadoop SQL Interfaces
- Ingestion tools
- Map Reduce alternatives
- Cloud Options
- Table-Style Database Management Services
- NoSQL Databases
- In-Memory Big Data Management Systems
- Data Discovery
- Analytics Tools
- Data Integration Tools
- APPENDIX A: References and Bibliography
- Title: Big Data Application Architecture Q&A: A Problem - Solution Approach
- Release date: December 2013
- Publisher(s): Apress
- ISBN: 9781430262923
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