Overview

Get the most out of MongoDB using a problem-solution approach. This book starts with recipes on the MongoDB query language, including how to query various data structures stored within documents. These self-contained code examples allow you to solve your MongoDB problems without fuss. MongoDB Recipes describes how to use advanced querying in MongoDB, such as indexing and the aggregation framework. It demonstrates how to use the Compass function, a GUI client interacting with MongoDB, and how to apply data modeling to your MongoDB application. You’ll see recipes on the latest features of MongoDB 4 allowing you to manage data in an efficient manner using MongoDB. What You Will Learn  Work with the MongoDB document model  Design MongoDB schemas  Use the MongoDB query language Harness the aggregation framework Create replica sets and sharding in MongoDB Who This Book Is ForDevelopers and professionals who work with MongoDB.                

Full Product Details

Author:   Subhashini Chellappan ,  Dharanitharan Ganesan
Publisher:   APress
Imprint:   APress
Edition:   1st ed.
Weight:   0.454kg
ISBN:  

9781484248904

ISBN 10:   1484248902
Pages:   247
Publication Date:   16 December 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Chapter 1: MongoDB Features and Installation Chapter Goal:  In this chapter, you will learn NoSQL databases, CAP theorem, MongoDB Features and MongoDB tools. This chapter will also cover installation of MongoDB and its associated tools. Sub Topics: NoSQL Databases and Categories CAP Theorem MongoDB Features MongoDB Tools Describe JSON and BSON Installing MongoDB on Windows, Linux MongoDB Terms MongoDB Data Types Chapter 2: CRUD Operations Chapter Goal:  In this chapter, you will learn how to perform CRUD operations with MongoDB. This chapter also help you to understand how to query embedded documents and arrays. Sub Topics: Basic CRUD operations Query Embedded Documents Query Arrays Bulk Write Operations   Chapter 3: Data Modelling Chapter Goal:  In this chapter, you will learn schema design and various data modelling patterns in MongoDB. Sub Topics: Data Modelling Concepts Data Model Patterns Model Relationship between documents Model Tree Structures   Chapter 4: Indexing and Aggregation Framework Chapter Goal:  In this chapter, you will learn indexes types and Aggregation Framework in MongoDB. Sub Topics: Introduction to indexes Index Types Creating Indexes Introduction to Aggregation Framework Aggregation Framework Types   Chapter 5: MongoDB Replication and Sharding Chapter Goal:  In this chapter, you will learn the replication set up and sharding set up. Sub Topics: Replication Concepts Master Slave Replication Replication Setup Introduction to Sharding and concepts Shard Setup Types of Sharding   Chapter 6: MongoDB Transaction Chapter Goal:  In this chapter, you will learn transactions in MongoDB. Sub Topics: Atomicity Multi-Document Transaction Concurrency Control   Chapter 7: MongoDB Administration Chapter Goal:  In this chapter, you will learn Database Profiler, MongoDB Backup Methods and Monitoring MongoDB. Sub Topics: Database Profiler MongoDB Backup Methods Monitoring MongoDB   Chapter 8: MongoDB Security Chapter Goal:  In this chapter, you will learn security aspects of MongoDB. Sub Topics: Creating Users Creating and Assigning custom roles Authenticating Server

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Author Information

Subhashini Chellappan is a technology enthusiast with expertise in the big data and cloud space. She has rich experience in both academia and the software industry. Her areas of interest and expertise are centered on business intelligence, big data analytics and cloud computing. Dharanitharan Ganesan is an MBA in Technology management with high level of exposure and experience in big data – Apache Hadoop, Apache Spark and various Hadoop ecosystem components. He has a proven track record of improving efficiency and productivity through the automation of various routine and administrative functions in business intelligence and big data technologies. His areas of interest and expertise are centered on machine learning algorithms, Blockchain in Bigdata, statistical modelling and predictive analytics.              

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