Design and implement cloud-native applications with Microsoft Azure Cosmos DB Training in Denmark

What Can You Achieve with This Training?

• Learn to build high-performance, scalable cloud-native applications using Azure Cosmos DB for NoSQL.

• Develop reliable data access solutions with SDKs, data operations, querying, and data modelling techniques.

• Balance performance and cost through effective partitioning, throughput, consistency, and indexing strategies.

• Explore full-text search, vector search, and hybrid search for modern AI-powered applications.

• Build Retrieval-Augmented Generation (RAG) solutions and persistent AI agent memory stores using Azure Cosmos DB.

• Understand how Microsoft Entra, RBAC, backup, restore, and multi-region configurations support secure and available database solutions.

• Learn to monitor and manage data environments with Azure Monitor, Cosmos DB Fleets, and Microsoft Fabric.

The Design and Implement Cloud-Native Applications with Microsoft Azure Cosmos DB Training helps developers design, build, and optimise scalable, high-performance cloud-native and AI-enabled applications using Azure Cosmos DB for NoSQL.

The course introduces Azure Cosmos DB as both a NoSQL database service and a data platform that can support modern AI application scenarios.

Participants learn to evaluate whether Azure Cosmos DB for NoSQL meets application requirements, create accounts, databases, containers, and items, and configure resources for different workloads. Topics such as Request Units (RUs), Throughput, Consistency, and Time to Live (TTL) are examined in relation to performance, data retention, and cost.

The programme covers application connectivity using .NET and Python SDKs, including point reads, item creation and updates, deletion, partial document patching, transactional batches, bulk operations, and optimistic concurrency control.

Data architecture modules focus on access-pattern-based data modelling, partition key selection, hierarchical and synthetic partitioning, multitenant strategies, and indexing policies.

Participants also explore Change Feed processing, integration with Azure Functions, and moving data through Container Copy Jobs.

The AI-focused modules introduce Model Context Protocol (MCP), AI-assisted development tools, full-text search, vector search, BM25 ranking, Reciprocal Rank Fusion (RRF), and hybrid retrieval.

Learners then examine how Azure Cosmos DB can support Retrieval-Augmented Generation (RAG) applications and persistent memory solutions for AI agents.

Security and availability topics include Microsoft Entra authentication, role-based access control, network isolation, dynamic data masking, backup and restore, point-in-time recovery, and multi-region failover.

The course also covers query optimisation, monitoring with Azure Monitor, managing Cosmos DB environments at scale with Fleets, and integrating operational data with Microsoft Fabric OneLake.

Throughout the programme, module assessments and practical exercises help participants apply the concepts to realistic application development and database management scenarios.

Participants should have:

• Familiarity with Microsoft Azure fundamentals

• Development experience with C#, Java, Python, or JavaScript

• Experience working with SQL or NoSQL databases

• A basic understanding of application development concepts

This course is intended for developers who build or support cloud-native applications using Azure Cosmos DB for NoSQL.

It is particularly suitable for:

• Software Developers

• Application Developers

• Cloud Developers

• Solutions Developers

• Professionals developing modern cloud-native applications

By the end of this course, participants will be able to:

• Evaluate Azure Cosmos DB for NoSQL for different application and AI workloads.

• Create and manage accounts, databases, containers, and items.

• Configure Request Units, Throughput, Consistency, and TTL to balance performance and cost.

• Connect to Azure Cosmos DB using .NET and Python SDKs.

• Perform point reads, create, replace, upsert, delete, patch, transactional batch, and bulk operations.

• Apply optimistic concurrency control to data updates.

• Write parameterised queries, correlated subqueries, and cross-product queries.

• Page large query results using continuation tokens.

• Design data models and select partitioning strategies based on access patterns.

• Evaluate hierarchical, synthetic, and multitenant partitioning approaches.

• Process database changes using the Change Feed and Azure Functions.

• Use MCP Server and AI-assisted development tools in Azure Cosmos DB scenarios.

• Secure solutions using Microsoft Entra, RBAC, network isolation, and dynamic data masking.

• Configure periodic backup, continuous backup, and point-in-time recovery.

• Evaluate multi-region availability, multi-region writes, conflict resolution, and failover strategies.

• Develop indexing strategies using composite, spatial, computed-property, and global secondary indexes.

• Analyse query cost, latency, scans, fan-out operations, and partition utilisation.

• Configure Azure Monitor alerts and investigate diagnostic logs.

• Manage multitenant environments and assess usage and cost through Cosmos DB Fleets.

• Implement full-text search, vector search, BM25 ranking, and vector similarity.

• Develop hybrid search solutions using Reciprocal Rank Fusion (RRF).

• Build Retrieval-Augmented Generation (RAG) applications with contextual retrieval and grounded responses.

• Design short-term and long-term memory stores for AI agents.

• Use Microsoft Fabric OneLake, T-SQL, and Spark for operational analytics on Azure Cosmos DB data.

1. Explore Azure Cosmos DB for NoSQL

This module introduces the core resources and capabilities of Azure Cosmos DB for NoSQL.

• Evaluate Azure Cosmos DB suitability for application and AI workloads

• Create accounts, databases, containers, and items

• Explore Cosmos DB resources and basic operations

• Exercise: Create an Azure Cosmos DB account and resources

2. Configure Resources, Throughput, and Consistency

This module examines how to configure Azure Cosmos DB capacity and consistency to meet application requirements.

• Assess Request Unit (RU), Throughput, and Consistency requirements

• Configure provisioned throughput and serverless capacity

• Configure throughput and consistency levels

• Set Time to Live (TTL) for data retention

• Balance cost and performance

• Exercise: Configure throughput and consistency

3. Connect to Azure Cosmos DB with the SDK

This module focuses on application connectivity and SDK configuration.

• Initialise and configure .NET and Python SDKs

• Connect using a client singleton

• Tune connectivity modes

• Enable offline development

• Handle connection errors

4. Implement Azure Cosmos DB Operations with the SDK

This module introduces the data operations available through Azure Cosmos DB SDKs.

• Perform point read operations

• Create, replace, upsert, and delete items

• Update items using partial document patch

• Configure item-level Time to Live (TTL)

• Implement optimistic concurrency control

• Execute transactional batch operations

• Perform bulk operations

• Exercise: Implement data operations

5. Query Data in Azure Cosmos DB for NoSQL

This module explores advanced query techniques and result management.

• Write parameterised queries

• Use projections and built-in functions

• Implement correlated subqueries

• Implement cross-product queries

• Page large result sets using continuation tokens

• Exercise: Author and page queries

6. Design a Data Modeling and Partitioning Strategy

This module examines data modelling and partitioning approaches for scalable workloads.

• Design data models based on access patterns

• Select partition keys for scalable applications

• Choose hierarchical partitioning

• Evaluate synthetic partitioning

• Apply multitenant partitioning strategies

• Use denormalisation and pre-aggregation for performance

• Identify data modelling anti-patterns

• Estimate workload cost

• Exercise: Design a data model and partitioning strategy

7. Process the Azure Cosmos DB for NoSQL Change Feed

This module focuses on processing database changes and supporting event-driven applications.

• Read the Change Feed using processor and pull models

• Select an appropriate Change Feed mode

• Respond to changes using Azure Functions

• Move container data using Container Copy Jobs

• Exercise: Process changes and copy data

8. Implement AI-Assisted Development Tools for Azure Cosmos DB

This module introduces AI-assisted development tools and their integration with Azure Cosmos DB.

• Connect an AI assistant through a Model Context Protocol (MCP) Server

• Add durable memory using agent toolkits

• Implement multi-step retrieval

• Select appropriate tools for different scenarios

• Exercise: Inspect Azure Cosmos DB using Azure MCP Server

9. Secure an Azure Cosmos DB Solution

This module covers identity, access management, and data protection controls.

• Configure Microsoft Entra authentication

• Configure key-based authentication

• Implement Role-Based Access Control (RBAC)

• Set up network isolation

• Apply dynamic data masking

• Exercise: Secure an Azure Cosmos DB account

10. Configure Backup and Restore in Azure Cosmos DB

This module introduces backup options and recovery strategies.

• Compare periodic and continuous backup

• Enable continuous backup

• Perform Point-in-Time Recovery (PITR)

• Describe Azure Backup options for long-term retention

• Exercise: Recover data from a recovery point

11. Design Multi-Region Availability and Failover in Azure Cosmos DB

This module examines high availability and regional deployment configurations.

• Add regions to an Azure Cosmos DB account

• Route reads and writes across regions

• Configure multi-region writes

• Implement conflict resolution

• Configure account-level failover

• Configure partition-level failover

• Exercise: Configure regions and failover

12. Design and Optimize an Indexing Strategy in Azure Cosmos DB

This module explores indexing approaches for different query and workload requirements.

• Align indexing policies with workload requirements

• Configure composite indexes

• Configure spatial indexes

• Configure computed-property indexes

• Implement global secondary indexes for cross-partition queries

• Exercise: Optimise an indexing strategy

13. Analyze and Tune Query and Operation Performance in Azure Cosmos DB

This module focuses on identifying expensive queries and improving database performance.

• Diagnose high-cost queries using metrics

• Distinguish scans from fan-out operations

• Apply appropriate performance remediation techniques

• Analyse query performance metrics

• Evaluate index performance metrics

• Optimise cross-partition queries

• Exercise: Tune a high-cost query

14. Monitor and Troubleshoot Azure Cosmos DB

This module introduces monitoring and troubleshooting approaches for Azure Cosmos DB workloads.

• Interpret response codes

• Diagnose throttling

• Monitor latency

• Monitor partition utilisation

• Configure Azure Monitor alerts

• Analyse diagnostic logs

• Exercise: Diagnose throttling and configure an alert

15. Manage Azure Cosmos DB at Scale with Fleets

This module examines centralised management of large and multitenant Cosmos DB environments.

• Group accounts into a Fleet

• Manage a multitenant estate as a single unit

• Share throughput through pools

• Monitor Fleet Analytics

• Analyse estate-wide usage and costs

• Exercise: Create and monitor a Fleet

16. Implement Full-Text and Vector Search in Azure Cosmos DB for NoSQL

This module explores native keyword and semantic search capabilities.

• Add keyword retrieval to containers

• Implement semantic retrieval

• Configure full-text search policies

• Configure vector policies and indexes

• Select appropriate partitioning for search workloads

• Rank results using BM25

• Apply vector similarity for semantic ranking

• Use the Change Feed to keep embeddings aligned with source text

• Exercise: Build native search over a product catalogue

17. Implement Hybrid Search and Optimize AI Retrieval in Azure Cosmos DB for NoSQL

This module focuses on combining retrieval techniques and improving search performance.

• Combine keyword and semantic retrieval

• Apply Reciprocal Rank Fusion (RRF)

• Tune result relevance

• Evaluate request charge

• Optimise latency

• Isolate search workloads from transactional traffic

• Exercise: Build and tune hybrid search

18. Build RAG Applications with Azure Cosmos DB for NoSQL

This module introduces Retrieval-Augmented Generation applications supported by Azure Cosmos DB.

• Prepare content for retrieval

• Generate embeddings

• Retrieve relevant context

• Ground model responses in retrieved information

• Assemble prompts that cite sources

• Handle evidence gaps

• Orchestrate a RAG pipeline for follow-up questions

• Exercise: Build a grounded RAG assistant

19. Design and Implement Agent Memory Stores in Azure Cosmos DB

This module explores persistent memory architectures for AI agents.

• Separate short-term conversation state from long-term memory

• Distil raw conversation turns into facts for later retrieval

• Configure retention policies

• Apply reconciliation policies

• Define erasure policies

• Exercise: Build a durable agent memory store

20. Implement Operational Analytics with Microsoft Fabric

This module examines how Azure Cosmos DB data can support analytics in Microsoft Fabric.

• Mirror an Azure Cosmos DB database into Microsoft Fabric OneLake

• Verify the mirrored data copy

• Query mirrored data using T-SQL

• Analyse mirrored data using Apache Spark

• Monitor mirroring operations

• Answer operational and analytical questions

• Exercise: Mirror and query a Cosmos DB database in Microsoft Fabric