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 explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop. Summarization patterns: get a top-level view by summarizing and grouping data Filtering patterns: view data subsets such as records generated from one user Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier Join patterns: analyze different datasets together to discover interesting relationships Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job Input and output patterns: customize the way you use Hadoop to load or store data "A clear exposition of MapReduce programs for common data processing patterns-this book is indespensible for anyone using Hadoop." --Tom White, author of Hadoop: The Definitive Guide
Il n'y a pas encore de discussion sur ce livre
Soyez le premier à en lancer une !
Les meilleurs albums, romans, documentaires, BD à offrir aux petits et aux plus grands
Il n'est pas trop tard pour les découvrir... ou les offrir !
Inspirée d’une histoire vraie, cette BD apporte des conseils et des solutions pour sortir de l'isolement
L’écrivain franco-vénézuélien Miguel Bonnefoy poursuit l’exploration fantasmagorique de sa mémoire familiale...
