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NoSQL Distilled

Learn NoSQL databases and how to use popular tools like MongoDB, Cassandra, and Neo4j.

(NoSQL.AP1) / ISBN : 978-1-64459-457-5
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About This Course

This NoSQL course will polish your skills in working with modern databases. You’ll learn the basics of NoSQL databases, including key-value stores, document databases, column-family stores, and graph databases. Through hands-on exercises, you’ll understand how to store, manage, and access data. Whether you’re looking to improve your data modeling, explore sharding and replication, or simply choose the right database for your project, this NoSQL course gives you the confidence to excel in today’s data-driven world.

Skills You’ll Get

  • Understand the key differences between relational and NoSQL databases to make informed technology choices 
  • Build and manage databases using MongoDB, Cassandra, and Neo4j for real-world applications 
  • Model data with key-value, document, column-family, and graph data models to handle complex data
  • Implement sharding and replication techniques to improve data distribution and performance 
  • Apply Map-Reduce for processing large data sets in a distributed environment 
  • Master consistency and schema migrations to ensure smooth database operations 
  • Optimize data access and query performance across various NoSQL database types 
  • Choose the right NoSQL database for your project needs by analyzing use cases and performance factors

 

Download Course Outline

1

Preface

  • Why Are NoSQL Databases Interesting?
  • What’s in the Course
  • Who Should Read This course
  • What Are the Databases
  • Acknowledgments
2

Why NoSQL?

  • The Value of Relational Databases
  • Impedance Mismatch
  • Application and Integration Databases
  • Attack of the Clusters
  • The Emergence of NoSQL
  • Key Points
3

Aggregate Data Models

  • Aggregates
  • Key-Value and Document Data Models
  • Column-Family Stores
  • Summarizing Aggregate-Oriented Databases
  • Further Reading
  • Key Points
4

More Details on Data Models

  • Relationships
  • Graph Databases
  • Schemaless Databases
  • Materialized Views
  • Modeling for Data Access
  • Key Points
5

Distribution Models

  • Single Server
  • Sharding
  • Master-Slave Replication
  • Peer-to-Peer Replication
  • Combining Sharding and Replication
  • Key Points
6

Consistency

  • Update Consistency
  • Read Consistency
  • Relaxing Consistency
  • Relaxing Durability
  • Quorums
  • Further Reading
  • Key Points
7

Version Stamps

  • Business and System Transactions
  • Version Stamps on Multiple Nodes
  • Key Points
8

Map-Reduce

  • Basic Map-Reduce
  • Partitioning and Combining
  • Composing Map-Reduce Calculations
  • Further Reading
  • Key Points
9

Key-Value Databases

  • What Is a Key-Value Store
  • Key-Value Store Features
  • Suitable Use Cases
  • When Not to Use
10

Document Databases

  • What Is a Document Database?
  • Features
  • Suitable Use Cases
  • When Not to Use
11

Column-Family Stores

  • What Is a Column-Family Data Store?
  • Features
  • Suitable Use Cases
  • When Not to Use
12

Graph Databases

  • What Is a Graph Database?
  • Features
  • Suitable Use Cases
  • When Not to Use
13

Schema Migrations

  • Schema Changes
  • Schema Changes in RDBMS
  • Schema Changes in a NoSQL Data Store
  • Further Reading
  • Key Points
14

Polyglot Persistence

  • Disparate Data Storage Needs
  • Polyglot Data Store Usage
  • Service Usage over Direct Data Store Usage
  • Expanding for Better Functionality
  • Choosing the Right Technology
  • Enterprise Concerns with Polyglot Persistence
  • Deployment Complexity
  • Key Points
15

Beyond NoSQL

  • File Systems
  • Event Sourcing
  • Memory Image
  • Version Control
  • XML Databases
  • Object Databases
  • Key Points
16

Choosing Your Database

  • Programmer Productivity
  • Data-Access Performance
  • Sticking with the Default
  • Hedging Your Bets
  • Key Points
  • Final Thoughts

1

Aggregate Data Models

  • Building the Sample Data in MongoDB
2

Document Databases

  • Using the find Query in MongoDB
  • Using db.runCommand() in MongoDB
3

Column-Family Stores

  • Creating a Super Column Family in Cassandra
  • Creating a Column Family in Cassandra
  • Inserting and Reading Data in CQL
  • Using the DELETE Query in Cassandra
  • Using the SELECT Command in Cassandra
  • Using the UPDATE Query in Cassandra
4

Graph Databases

  • Indexing Nodes in a Graph in Neo4j
  • Creating a Graph in Neo4j
  • Using the Cypher Query Language
5

Schema Migrations

  • Writing and Reading Document in MongoDB

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MongoDB is one of the most popular NoSQL databases due to its flexibility, ease of use, and strong community support.

SQL databases are relational and use structured query language to define and manipulate data in tables with fixed schemas. NoSQL databases are non-relational, offering more flexibility with data storage by using formats like key-value pairs, documents, or graphs, and they can scale more easily across distributed systems.

This course is ideal for developers, data professionals, and IT enthusiasts looking to better understand NoSQL databases.

There are no formal prerequisites for this NoSQL database course, but a basic understanding of databases and data storage concepts will be helpful.

Learning NoSQL databases will give you the knowledge and skills to work with modern, scalable, and flexible database systems. Their demand is increasing in industries like e-commerce, social media, and big data.

After completing this course, you can explore advanced topics in database management, practice using specific NoSQL systems like MongoDB or Cassandra, or even pursue certification in NoSQL databases to strengthen your position in the talent pool.

NoSQL Databases Training for Developers

  Get the training to build, manage, and optimize modern databases designed for today’s fast-paced applications.

$279.99

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