10 Cosmos DB

 

This chapter covers:

  • Creating a Cosmos DB account, database and collection
  • Data modelling and partitioning to optimize Cosmos DB
  • Consistency models and concurrency control
  • Managing costs and performance optimization

We have arrived at one of my favorite services on Azure: Cosmos DB. It is one of those services that wouldn’t exist with cloud computing, so it isn’t merely an adaptation of traditional services like virtual machines or storage. Microsoft Azure's highly scalable and distributed NoSQL database service is designed to handle massive amounts of data across global networks. Cosmos DB stands out with its remarkable flexibility, blazing-fast performance, and comprehensive support for various data models. It handles key-value, document, or graph databases, and has plenty of developer tools to shout about too.

10.1 Guaranteed Speed at Any Scale

10.1.1 Scalability

10.1.2 Speed with multi-region replication

10.1.3 Pitfalls of Enabling Autoscaling and Multi-Region for Cosmos DB

10.2 Simplified Application Development

10.2.1 Introduction to Cosmos DB APIs

10.2.2 SDKs for Enhanced Development

10.3 Advanced Data Modeling and Partitioning

10.3.1 Core Principles of Data Modeling in Cosmos DB

10.3.2 Understanding Partitioning in Cosmos DB

10.3.3 Advanced Partitioning Strategies

10.4 Consistency, Availability, and Concurrency

10.4.1 Consistency

10.4.2 Availability

10.4.3 Concurrency

10.5 Near Real-Time Analytics and Integration with Azure Services

10.5.1 Using Azure Synapse Analytics

10.5.2 Integrating with Azure Functions

10.5.3 Using Azure Stream Analytics

10.6 Managing Costs and Performance Optimization

10.6.1 Provisioned Throughput vs. Autoscale

10.6.2 Monitoring and Optimization

10.6.3 vCore Usage Considerations

10.7 Summary