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Cosmos DB for MongoDB developersmigr...
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Sharma, Manish.
Cosmos DB for MongoDB developersmigrating to Azure Cosmos DB and using the MongoDB API /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Cosmos DB for MongoDB developersby Manish Sharma.
Reminder of title:
migrating to Azure Cosmos DB and using the MongoDB API /
Author:
Sharma, Manish.
Published:
Berkeley, CA :Apress :2018.
Description:
xviii, 209 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Object-oriented databases.
Online resource:
http://dx.doi.org/10.1007/978-1-4842-3682-6
ISBN:
9781484236826$q(electronic bk.)
Cosmos DB for MongoDB developersmigrating to Azure Cosmos DB and using the MongoDB API /
Sharma, Manish.
Cosmos DB for MongoDB developers
migrating to Azure Cosmos DB and using the MongoDB API /[electronic resource] :by Manish Sharma. - Berkeley, CA :Apress :2018. - xviii, 209 p. :ill., digital ;24 cm.
Learn Azure Cosmos DB and its MongoDB API with hands-on samples and advanced features such as the multi-homing API, geo-replication, custom indexing, TTL, request units (RU), consistency levels, partitioning, and much more. Each chapter explains Azure Cosmos DB's features and functionalities by comparing it to MongoDB with coding samples. Cosmos DB for MongoDB Developers starts with an overview of NoSQL and Azure Cosmos DB and moves on to demonstrate the difference between geo-replication of Azure Cosmos DB compared to MongoDB. Along the way you'll cover subjects including indexing, partitioning, consistency, and sizing, all of which will help you understand the concepts of read units and how this calculation is derived from an existing MongoDB's usage. The next part of the book shows you the process and strategies for migrating to Azure Cosmos DB. You will learn the day-to-day scenarios of using Azure Cosmos DB, its sizing strategies, and optimizing techniques for the MongoDB API. This information will help you when planning to migrate from MongoDB or if you would like to compare MongoDB to the Azure Cosmos DB MongoDB API before considering the switch. You will: Migrate to MongoDB and understand its strategies Develop a sample application using MongoDB's client driver Make use of sizing best practices and performance optimization scenarios Optimize MongoDB's partition mechanism and indexing.
ISBN: 9781484236826$q(electronic bk.)
Standard No.: 10.1007/978-1-4842-3682-6doiSubjects--Uniform Titles:
MongoDB.
Subjects--Topical Terms:
200131
Object-oriented databases.
LC Class. No.: QA76.9.D3 / S537 2018
Dewey Class. No.: 005.75
Cosmos DB for MongoDB developersmigrating to Azure Cosmos DB and using the MongoDB API /
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migrating to Azure Cosmos DB and using the MongoDB API /
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Learn Azure Cosmos DB and its MongoDB API with hands-on samples and advanced features such as the multi-homing API, geo-replication, custom indexing, TTL, request units (RU), consistency levels, partitioning, and much more. Each chapter explains Azure Cosmos DB's features and functionalities by comparing it to MongoDB with coding samples. Cosmos DB for MongoDB Developers starts with an overview of NoSQL and Azure Cosmos DB and moves on to demonstrate the difference between geo-replication of Azure Cosmos DB compared to MongoDB. Along the way you'll cover subjects including indexing, partitioning, consistency, and sizing, all of which will help you understand the concepts of read units and how this calculation is derived from an existing MongoDB's usage. The next part of the book shows you the process and strategies for migrating to Azure Cosmos DB. You will learn the day-to-day scenarios of using Azure Cosmos DB, its sizing strategies, and optimizing techniques for the MongoDB API. This information will help you when planning to migrate from MongoDB or if you would like to compare MongoDB to the Azure Cosmos DB MongoDB API before considering the switch. You will: Migrate to MongoDB and understand its strategies Develop a sample application using MongoDB's client driver Make use of sizing best practices and performance optimization scenarios Optimize MongoDB's partition mechanism and indexing.
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EB QA76.9.D3 S531 2018 2018
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http://dx.doi.org/10.1007/978-1-4842-3682-6
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