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Design and implement cloud-native applications with Microsoft Azure Cosmos DB
- Categories:
- Data and Office Applications Software and DevOps
- Level:
- Intermediate
- Tier type:
- PerformancePlus
- Code:
- MDP420
- Exam:
- Yes - Available separately
Overview
This course guides developers through designing, building, and optimizing high-performance, scalable AI applications using Azure Cosmos DB for NoSQL. Delegates gain practical experience with the NoSQL API and SDK, learning to evaluate Cosmos DB for workloads, configure resources and consistency, connect and operate with SDKs, and implement advanced data modeling and partitioning strategies. The course covers querying, indexing, securing, and monitoring Cosmos DB solutions, as well as integrating AI-assisted development tools, implementing full-text and vector search, and building retrieval-augmented generation (RAG) applications. Learners also explore operational analytics, backup and restore, multi-region availability, and managing Cosmos DB at scale.
Prerequisites
Learners should have:
- Familiarity with Azure fundamentals
- Experience with C#, Java, Python, or JavaScript
- Experience working with SQL or NoSQL databases
- A basic understanding of application development concepts
Target audience
This course is designed for developers who build or support cloud-native applications using Azure Cosmos DB for NoSQL.
It is suitable for:
- Software developers
- Application developers
- Cloud developers
- Solutions developers
- Developers building modern cloud-native applications
What's included
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What is Virtual?
Live, instructor-led training delivered online
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What's included?
4 Days instructor led course
Exam: Yes - Available separately
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Dates
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Outline
Explore Azure Cosmos DB for NoSQL
- Evaluate suitability for application and AI workloads
- Create account, database, container, and items
- Explore resources and basic operations
- Exercise: Create an account and resources
Configure Resources, Throughput, and Consistency
- Assess request-unit, throughput, and consistency requirements
- Configure provisioned/serverless capacity, throughput, and consistency levels
- Configure time to live for data retention
- Balance cost and performance
- Exercise: Configure throughput and consistency
Connect to Azure Cosmos DB with the SDK
- Initialize and configure SDK for .NET and Python
- Connect using client singleton and tune connectivity mode
- Enable offline development and handle connection errors
Implement Azure Cosmos DB Operations with the SDK
- Perform point read operations
- Create, replace, upsert, and delete items
- Update items with partial document patch
- Configure item time to live and implement optimistic concurrency control
- Execute transactional batch and bulk operations
- Exercise: Implement data operations
Query Data in Azure Cosmos DB for NoSQL
- Author queries using parameters, projections, and built-in functions
- Implement correlated subqueries and cross-product queries
- Page large result sets with continuation tokens
- Exercise: Author and page queries
Design a Data Modeling and Partitioning Strategy
- Design data models based on access patterns
- Select partition keys for scalable workloads
- Choose hierarchical, synthetic, or multitenant partitioning strategies
- Denormalize and pre-aggregate for performance
- Identify anti-patterns and estimate cost
- Exercise: Design a model and partition strategy
Process the Azure Cosmos DB for NoSQL Change Feed
- Read change feed with processor and pull models
- Choose change feed mode for workloads
- React to changes with Azure Functions
- Move container data with container copy jobs
- Exercise: Process changes and copy data
Implement AI-Assisted Development Tools for Azure Cosmos DB
- Connect AI assistant with Model Context Protocol server
- Add durable memory and multi-step retrieval with agent toolkits
- Choose the right tool for a scenario
- Exercise: Inspect Azure Cosmos DB with Azure MCP Server
Secure an Azure Cosmos DB Solution
- Configure Microsoft Entra and key-based authentication
- Implement role-based access control
- Set up network isolation and dynamic data masking
- Exercise: Secure an account
Configure Backup and Restore in Azure Cosmos DB
- Choose between periodic and continuous backup
- Enable continuous backup and perform point-in-time recovery
- Describe Azure backup for long-term retention
- Exercise: Recover from a recovery point
Design Multi-Region Availability and Failover in Azure Cosmos DB
- Add regions and route reads/writes
- Configure multi-region writes and resolve conflicts
- Set up account and partition-level failover
- Exercise: Configure regions and failover
Design and Optimize an Indexing Strategy in Azure Cosmos DB
- Fit indexing policies to workload requirements
- Configure composite, spatial, and computed-property indexes
- Implement global secondary indexes for cross-partition queries
- Exercise: Optimize an indexing strategy
Analyze and Tune Query and Operation Performance in Azure Cosmos DB
- Diagnose expensive queries using metrics
- Distinguish scans from fan-outs and apply remediation
- Analyze query and index performance metrics
- Optimize cross-partition queries
- Exercise: Tune a high-cost query
Monitor and Troubleshoot Azure Cosmos DB
- Interpret response codes and diagnose throttling
- Monitor latency and partition utilization
- Configure Azure Monitor alerts and analyze diagnostic logs
- Exercise: Diagnose throttling and configure an alert
Manage Azure Cosmos DB at Scale with Fleets
- Run a multitenant estate as one unit by grouping accounts into a fleet
- Share throughput with pools and monitor fleet analytics
- Answer estate-wide usage and cost questions
- Exercise: Create and monitor a fleet
Implement Full-Text and Vector Search in Azure Cosmos DB for NoSQL
- Add keyword and semantic retrieval to containers
- Configure full-text and vector policies, indexes, and partitioning
- Rank results with BM25 and vector similarity
- Keep embeddings aligned with text using the change feed
- Exercise: Build native search over a product catalog
Implement Hybrid Search and Optimize AI Retrieval in Azure Cosmos DB for NoSQL
- Combine keyword and semantic retrieval with Reciprocal Rank Fusion
- Tune results for relevance, request charge, and latency
- Isolate search workloads from transactional traffic
- Exercise: Build and tune hybrid search
Build RAG Applications with Azure Cosmos DB for NoSQL
- Prepare and embed content for retrieval
- Retrieve context and ground responses
- Assemble prompts that cite sources and handle evidence gaps
- Orchestrate a RAG pipeline for follow-up questions
- Exercise: Build a grounded RAG assistant
Design and Implement Agent Memory Stores in Azure Cosmos DB
- Separate short-term conversation state from long-term memory
- Distill raw turns into facts for later retrieval
- Set retention, reconciliation, and erasure policies
- Exercise: Build a durable agent memory store
Implement Operational Analytics with Microsoft Fabric
- Mirror Cosmos DB database into Microsoft Fabric OneLake
- Verify the copy and query mirrored data with T-SQL and Spark
- Monitor mirroring and answer analytical questions
- Exercise: Mirror and query a Cosmos DB database in Microsoft Fabric
Exams and assessments
- Module assessments throughout the course to evaluate understanding and practical skills
- Exercises to apply knowledge in real-world scenarios
- Assessments focus on configuring, operating, and optimizing Azure Cosmos DB for NoSQL solutions
Hands-on learning
This course includes practical labs throughout the learning journey, giving learners opportunities to:
- Practical exercises accompany each module, allowing delegates to create, configure, and optimize Azure Cosmos DB resources
- Learners implement data operations, design models, process change feeds, secure accounts, and build AI-driven solutions
- Hands-on activities reinforce theoretical knowledge and develop real-world skills with Azure Cosmos DB for NoSQL
Learning outcomes
By the end of this course, learners will be able to:
- Evaluate Azure Cosmos DB for NoSQL suitability for various application workloads, including AI scenarios
- Configure resources, throughput, and consistency to balance cost and performance
- Connect to Azure Cosmos DB using SDKs and implement reliable data access
- Perform data operations, including point reads, create, replace, upsert, delete, and bulk actions
- Author and optimize queries, including correlated subqueries and cross-product queries
- Design effective data models and partitioning strategies to support scalable workloads
- Process change feeds and integrate with Azure Functions for event-driven architectures
- Implement AI-assisted development tools and build RAG applications with Cosmos DB
- Secure Cosmos DB solutions with authentication, access control, and data masking
- Configure backup, restore, multi-region availability, and failover strategies
- Design and optimize indexing, analyze and tune performance, and monitor Cosmos DB at scale
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