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Книги для разработчиков

Practical MongoDB: Architecting, Developing, and Administering MongoDB

Practical MongoDB: Architecting, Developing, and Administering MongoDB

Автор: Shakuntala Gupta Edward, Navin Sabharwal
Год: 2015
Формат: PDF
Страниц: 249
Просмотров: 1250

9,51 MB
Description

The “one-size-fits-all” thinking regarding traditional RDBMSs has been challenged in the last few years by the emergence of diversified NoSQL databases. More than 120 NoSQL databases are now available in the market, and the market leader by far is MongoDB. With so many companies opting for MongoDB as their NoSQL database of choice, there’s a need for a practical how-to combined with expert advice for getting the most out of the software.

Beginning with a short introduction to the basics of NoSQL databases, MongoDB experts Navin Sabharwal andShankatala Gupta Edward introduce readers to MongoDB – the leading document based NoSQL database, acquainting them step by step with all aspects of MongoDB. They cover the data model, underlying architecture, how to code using Mongo Shell, and administration of the MongoDB platform, among other topics. The book also provides clear guidelines and practical examples for architecting and developing applications using the MongoDB platform and deploying them. Database developers, architects, and database administrators will find useful information covering all aspects of the MongoDB platform and how to put it to use practically.

Practical Guide to MongoDB provides readers with:

  • A solid understanding of NoSQL databases;
  • An understanding of how to get started with MongoDB;
  • Methodical coverage of the architecture, development, and administration of MongoDB.

A plethora of “How to’s” enabling you to use the technology most efficiently to solve the problems you face.

Practical MongoDB is for those just starting to learning to work with NoSQL databases in general and MongoDB in particular. Skills in these areas are in demand, making this book essential reading for those who want to work more productively or break into big data work.

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