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NoSQL Data Models

You're reading from   NoSQL Data Models Addresses severe issues related to NoSQL data models

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Product type Paperback
Published in Aug 2018
Publisher Wiley
ISBN-13 9781786303646
Length 278 pages
Edition 1st Edition
Languages
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Author (1):
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Olivier Pivert Olivier Pivert
Author Profile Icon Olivier Pivert
Olivier Pivert
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Toc

Table of Contents (11) Chapters Close

Preface
1 NoSQL Languages and Systems 2 Distributed SPARQL Query Processing: a Case Study with Apache Spark FREE CHAPTER 3 Doing Web Data: from Dataset Recommendation to Data Linking 4 Big Data Integration in Cloud Environments: Requirements, Solutions and Challenges 5 Querying RDF Data: a Multigraph-based Approach 6 Fuzzy Preference Queries to NoSQL Graph Databases 7 Relevant Filtering in a Distributed Content-based Publish/Subscribe System List of Authors
Index
End User License Agreement

4.2. Big Data integration requirements in Cloud environments

Most of the data-intensive applications deployed in cloud environments question the “one size fits all” [STO 05] data management principles and promote the use of specialized Cloud data management infrastructures, also known as NoSQL systems. These solutions are able to perform orders of a magnitude that is better than the traditional (and generic) relational DBMS. NoSQL, which stands for Not only SQL, refers to a new generation of data stores. Unlike relational data stores, they are not standardized. Indeed, each NoSQL data store has its own data model, its own query language, its own API, etc. They are mostly open-source, schema-less, largely distributed database systems that enable rapid, ad-hoc organization and analysis of extremely high-volume, disparate data types [SAD 12]. They mainly come from web companies developing very highly intensive web applications such as Facebook, Amazon and Twitter. NoSQL data...

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