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NoSQL for Mere Mortals

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by Dan Sullivan
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NoSQL for Mere Mortals by Dan Sullivan

The Easy, Common-Sense Guide to Solving Real Problems with NoSQL

The Mere Mortals tutorials have earned worldwide praise as the clearest, simplest way to master essential database technologies. Now, there's one for today's exciting new NoSQL databases. NoSQL for Mere Mortals guides you through solving real problems with NoSQL and achieving unprecedented scalability, cost efficiency, flexibility, and availability.

Drawing on 20+ years of cutting-edge database experience, Dan Sullivan explains the advantages, use cases, and terminology associated with all four main categories of NoSQL databases: key-value, document, column family, and graph databases. For each, he introduces pragmatic best practices for building high-value applications. Through step-by-step examples, you'll discover how to choose the right database for each task, and use it the right way.

Coverage includes

--Getting started: What NoSQL databases are, how they differ from relational databases, when to use them, and when not to Data management principles and design criteria: Essential knowledge for creating any database solution, NoSQL or relational

--Key-value databases: Gaining more utility from data structures

--Document databases: Schemaless databases, normalization and denormalization, mutable documents, indexing, and design patterns

--Column family databases: Google's BigTable design, table design, indexing, partitioning, and Big Data

Graph databases: Graph/network modeling, design tips, query methods, and traps to avoid

Whether you're a database developer, data modeler, database user, or student, learning NoSQL can open up immense new opportunities. As thousands of database professionals already know, For Mere Mortals is the fastest, easiest route to mastery.

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Ebook Details
Pages: 552
Size: 8.8 MB
Publisher: Addison-Wesley Professional
Date published:   2015
ISBN: 9780134029887 (DRM-EPUB)
9780134029870 (DRM-PDF)

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Nonfiction > Computers > Database Management > Database Mining

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