Comprehensive collection of MapReduce design patterns: Saves time by providing proven solutions for common big data problems
Detailed explanations with pitfalls and caveats: Helps avoid common design mistakes when modeling big data architecture
Complete overview of MapReduce: Explores origins, implementations, and importance of design patterns
Code examples for Hadoop: Ensures practical application and understanding
Covers all major data processing stages: Summarization, filtering, joining, metapatterns, input/output, and more
Summarized by Shop
Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you’re using. Each pattern is explain