NoSQL Database with Microsoft Azure Cosmos DB Training Course

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Course Code



14 hours (usually 2 days including breaks)


  • Experience with database management systems
  • Familiarity with cloud computing


  • Database administrators
  • Developers


Microsoft Azure Cosmos DB is a fully managed NoSQL database service designed for high-speed data processing and storage scaling. It supports multiple data models and open-source APIs, such as MongoDB and Cassandra.

This instructor-led, live training (online or onsite) is aimed at database administrators or developers who wish to use Microsoft Azure Cosmos DB to develop and manage highly responsive and low latency applications.

By the end of this training, participants will be able to:

  • Provision the necessary Cosmos DB resources to start building databases and applications.
  • Scale application performance and storage by utilizing APIs in Cosmos DB. 
  • Manage database operations and reduce cost by optimizing Cosmos DB resources.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Course Outline


  • Azure Cosmos DB vs Redis vs other NoSQL databases
  • Overview of Azure Cosmos DB features and architecture

Getting Started

  • Introducing NoSQL DB
  • Choosing an API
  • Creating Cosmos DB resource models

Creating and Managing Data

  • Building console apps, web apps, and mobile apps
  • Modeling and partitioning data
  • Querying data with SQL

Working with DB APIs

  • Using Cassandra API in Cosmos DB
  • Migrating from MongoDB to Cosmos DB's MongoDB API
  • Modeling graph data with Gremlin API
  • Using Table APIs for applications

Optimizing Cosmos DB for Cost Reduction

  • Partitioning and scaling containers
  • Distributing data globally
  • Planning and managing costs
  • Implementing rate limits and reserved capacity


Summary and Conclusion



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