Implementing Microsoft Azure Cosmos DB Solutions

• Review of NoSQL database structures
• Migrating data and applications to Cosmos DB
• Managing data in Cosmos DB

• Creating and configuring a Cosmos DB database
• Migrating data from a Mongo DB database to Cosmos DB
• Using the SQL API to access data
• Protecting data in a Cosmos DB database

After completing this module, students will be able to:

• Create and configure a Cosmos DB.
• Migrate data from a Mongo DB database to a Cosmos DB database.
• Describe accessing data using the SQL API.
• Describe how to protect data in a Cosmos DB.

• Document models in Cosmos DB
• Querying data in a SQL API database
• Querying and maintaining data programmatically

• Design the document structure & partitioning strategy for the product catalog for the retail system
• Importing product catalog data
• Querying product catalog information
• Maintaining stock levels in the product catalog

After completing this module, students will be able to:

• Design NoSQL document structures that support business requirements and enable efficient operations.
• Describe how to perform SQL queries against a SQL API database.
• Explain how to insert, modify, delete, and query data in a SQL API database programmatically.

• Server-side programming with Cosmos DB
• Creating and using stored procedures
• Using triggers to maintain data integrity

• Design and implement the document and collection structure
• Implement the shopping cart functionality in the online retail system.
• Extend the online retail system to create orders from the items in a shopping cart.
• Extend the online retail system further to enable customers to view orders and backorders.

After completing this module, students will be able to:

• Describe how Cosmos DB enables you to implement server-side operations by writing JavaScript code.
• Describe how use the JavaScript Query API to implement transactional operations with stored procedures.
• Describe how use create triggers that you can use to maintain integrity when inserting, updating, and deleting documents.

• Optimizing database performance
• Monitoring the performance of a database

• Gathering execution statistics
• Examining how the different consistency models can impact throughput and latency
• Investigate the effects of triggers on performance
• Monitoring performance and tuning the partition key

After completing this module, students will be able to:

• Describe how to tune the configuration of a database and collections for best performance.
• Describe how to assess the performance of a document database and identify options for improving throughput.

• Graph database models in Cosmos DB
• Designing Graph database models for efficient operation

• Implementing a recommendations engine for customers
• Recording product purchase information
• Query a Graph database to obtain analytics

After completing this module, students will be able to:

• Describe the features that Cosmos DB provides for implementing graph databases.
• Design NoSQL graph structures that support business requirements and enable efficient operations.

• Integrating Cosmos DB with Azure search to optimize queries
• Analyzing data in a Cosmos DB database using Apache Spark
• Visualizing data in a Cosmos DB database

• Extending product search capabilities
• Performing end-of-month processing
• Visualizing sales data
• Exploring sales data

At the end of this module, students will be able to:

• Describe how to integrate Cosmos DB with Azure Search to perform efficient query processing over big data.
• Describe how to analyze big data held in Cosmos DB using Apache Spark.
• Describe how to visualize data in Cosmos DB using Jupyter notebooks, Power BI, and Azure Databricks.

• Working with the Cosmos DB change feed
• Integrating Cosmos DB into streaming solutions

• Handling orders
• Maintaining stock analytic data
• Displaying rolling revenue for a given time period

After completing this module, students will be able to:

• Describe the Cosmos DB change feed, and how to use it to process data efficiently.
• Explain how to incorporate Cosmos DB into streaming solutions such as Apache Kafka, Apache Spark, and Azure Stream Analytics.