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

 
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