Getting Started with Cosmos DB NoSQL Development (DP-3015)

Module 1: Introduction to Azure Cosmos DB for NoSQL

• Evaluate whether Azure Cosmos DB for NoSQL is the right database for your application.
• Describe how the features of the Azure Cosmos DB for NoSQL are appropriate for modern applications.

Module 2: Try Azure Cosmos DB for NoSQL

• Create a new Azure Cosmos DB for NoSQL account
• Create database, container, and item resources for an Azure Cosmos DB for NoSQL account

Module 3: Plan Resource Requirements

• Evaluate various requirements of your application

Module 4: Configure Azure Cosmos DB for NoSQL database and containers

• Compare the various service and throughput offerings for Azure Cosmos DB
• Migrate between standard and autoscale throughput

Module 5: Use the Azure Cosmos DB for NoSQL SDK

• Integrate the Microsoft.Azure.Cosmos SDK library from NuGet
• Connect to an Azure Cosmos DB for NoSQL account using the SDK and .NET

Module 6: Configure the Azure Cosmos DB for NoSQL SDK

• Configure the SDK for offline development
• Troubleshoot common connection errors
• Implement parallelism in the SDK
• Configure logging using the SDK

Module 7: Implement Azure Cosmos DB for NoSQL point operations

• Perform CRUD operations using the SDK
• Configure TTL for a specific item

Module 8: Query the Azure Cosmos DB for NoSQL

• Create and execute a SQL query
• Project query results
• Use built-in functions in a query

Module 9: Author complex queries with the Azure Cosmos DB for NoSQL

• Implement a correlated subquery
• Create a cross-product query

Module 10: Implement a non-relational data model

• Determine access patterns for data.
• Apply data model and partitioning strategies to support an efficient and scalable NoSQL database.

Module 11: Design a data partitioning strategy

• Manage relationships between data entities by using advanced modeling and partitioning strategies.
• Maintain the referential integrity of your data by using change feed.
• Implement pre-aggregating and denormalizing data strategies to improve data-model performance and scaling.
• Optimizing storage and compute by mixing entity types in a single container