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- Explore the core features, architecture, and use cases for Azure DocumentDB, a fully managed MongoDB-compatible database service.
After completing this module, you'll be able to:
- Describe what Azure DocumentDB is and how it relates to the open-source DocumentDB project.
- Explain the vCore-based architecture, compute tiers, and scaling options.
- Identify scenarios and workloads where Azure DocumentDB is the right choice.
- Learn how to provision an Azure DocumentDB cluster, connect with MongoDB tools, and configure compute, storage, and security settings.
After completing this module, you'll be able to:
- Create an Azure DocumentDB cluster in the Azure portal.
- Connect to the cluster using MongoDB Shell and connection strings.
- Configure cluster settings including compute tiers, storage, and network security.
- Write queries to create, read, update, and delete documents in Azure DocumentDB. Build aggregation pipelines to transform and analyze data.
After completing this module, you'll be able to:
- Insert documents into collections using insertOne and insertMany.
- Query and filter documents using comparison, logical, and element operators.
- Update and delete documents using field and array update operators.
- Build aggregation pipelines to group, join, and summarize data.
- Build applications that connect to Azure DocumentDB using the official MongoDB drivers for Python, .NET, and JavaScript. Perform CRUD operations programmatically and integrate database operations into your application code.
After completing this module, you'll be able to:
- Connect to an Azure DocumentDB cluster using the MongoDB driver for Python, .NET, or JavaScript.
- Perform CRUD operations programmatically using your preferred language's driver.
- Handle connection management, error handling, and resource cleanup in application code.
- Model data relationships effectively in Azure DocumentDB. Analyze entity relationships using access patterns and cardinality, apply the embed-vs-reference decision framework, and implement one-to-one, one-to-many, and many-to-many relationship patterns with practical e-commerce examples.
After completing this module, you'll be able to:
- Analyze entity relationships by identifying cardinality, access patterns, and update frequency.
- Apply the embed-vs-reference decision framework to determine the optimal modeling strategy.
- Implement one-to-one, one-to-many, and many-to-many relationship patterns including hybrid approaches.
- Apply 10 schema design patterns to solve data modeling challenges in Azure DocumentDB. Organize documents, precompute results, manage arrays, group time-series data, evolve schemas, and archive old data.
After completing this module, you'll be able to:
- Apply the inheritance, computed, and approximation patterns to optimize document structure and read/write performance.
- Use the extended reference and schema versioning patterns to reduce cross-collection lookups and manage schema evolution.
- Implement the single collection, subset, bucket, outlier, and archive patterns for complex data scenarios.
- Identify and fix common schema design mistakes in Azure DocumentDB, including unbounded arrays, collection sprawl, unnecessary indexes, over-normalization, and case-sensitivity issues.
After completing this module, you'll be able to:
- Identify the unbounded arrays and collection sprawl anti-patterns and apply structural fixes.
- Recognize unnecessary indexes and over-normalization, and apply performance-focused solutions.
- Detect and resolve case-sensitivity issues that cause duplicate data and failed lookups.
- Optimize query performance in Azure DocumentDB by building effective indexing strategies. Create single-field, compound, multi-key, and specialized indexes. To maintain performance at scale, analyze query execution with the explain() command and monitor index health.
After completing this module, you'll be able to:
- Explain how indexes work internally and how they affect read and write performance.
- Create single-field, compound, multi-key, and specialized indexes based on query patterns.
- Use the explain() command to analyze query execution and identify performance bottlenecks.
- Monitor and optimize indexes for production workloads.
