Microsoft

Course 20777-A: Implementing Microsoft Azure Cosmos DB Solutions

Course 20777-A: Implementing Microsoft Azure Cosmos DB Solutions

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This three-day instructor-led course is aimed at database professionals who are looking to implement a Cosmos DB solution.

Audience Profile

The primary audience for this course is database developers and architects (IT professionals, developers, and information workers) who plan to implement big data solutions on Azure using Cosmos DB.

Prerequisites

  • The fundamental concepts of partitioning, replication, and resource governance for building and configuring scalable applications that are agnostic of a Cosmos DB API.
  • A basic working knowledge of the Cosmos DB SQL API.

Course outline

Module Overview

This module describe the purpose and architecture of Cosmos DB.

Lessons

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

Lab Sessions

Creating and using a SQL API database in Cosmos DB

Lab Lessons

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.

Module Overview

This module describes how to design documents and collections to meet business requirements, and how to use the SQL API to build applications that use these documents.

Lessons

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

Lab Sessions

Designing and implementing SQL API database applications

Lab Lessons

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.

Module Overview

Describe how to create user-defined functions, stored procedures, and triggers.

Lessons

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

Lab Sessions

Writing user-defined functions, stored procedures and triggers

Lab Lessons

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.

Module Overview

Describe how to optimize a database, and how to monitor performance.

Lessons

Optimizing database performance
Monitoring the performance of a database

Lab Sessions

Tuning a database and monitoring performance

Lab Lessons

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.

Module Overview

This module describes how to create efficient graph database models using Cosmos DB.

Lessons

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

Lab Sessions

Designing and implementing a Graph database

Lab Lessons

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.

Module Overview

This module describes how to use Azure Search and HDInsight with Cosmos DB to query and analyze big data.

Lessons

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

Lab Sessions

Querying and Analyzing Big Data with Cosmos DB

Lab Lessons

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

After completing 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.

Module Overview

This module describes how to use Cosmos DB as a source and sink for streaming data.

Lessons

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

Lab Sessions

Using Cosmos DB with stream processing

Lab Lessons

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.

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