AZ-305 Designing Microsoft Azure Infrastructure Solutions Exam

Recommend a solution for storing semi-structured data

Design Azure Cosmos DB for Semi-Structured Workloads

Azure Cosmos DB is a globally distributed, multi-model database service designed to handle semi-structured data efficiently. It excels in maintaining performance, scalability, and cost-efficiency while providing rich query capabilities and automatic indexing. This summary will cover essential considerations and best practices for storing semi-structured data using Azure Cosmos DB, focusing on how it meets specific requirements.

Azure Cosmos DB APIs

Azure Cosmos DB supports multiple APIs, each optimized for different types of semi-structured data workloads:

• SQL API: Best for querying JSON documents with SQL-like syntax.
• MongoDB API: Allows integration with applications that already use MongoDB.
• Cassandra API: Suitable for Cassandra-compatible applications.
• Gremlin API: Ideal for graph-based data modeling.

Choosing the right API depends on your query flexibility, latency, and consistency needs.

Partitioning and Indexing

Proper partitioning and indexing are crucial for optimizing throughput and performance:

• Partition Keys: Choose a partition key with high cardinality to evenly distribute workloads across partitions.
• Indexing Policies: Configure indexing policies to optimize query performance, keeping in mind that Azure Cosmos DB automatically indexes each property unless specified otherwise.

Throughput Provisioning

Azure Cosmos DB's throughput provisioning allows you to allocate Request Units (RUs) to manage performance and cost:

• Throughput Scaling: Adjust the RUs based on workload requirements for efficient performance management.
• Serverless Mode: Azure Cosmos DB offers a consumption-based serverless mode, ideal for scenarios with unpredictable traffic patterns.

Consistency Levels

Azure Cosmos DB provides several consistency levels to balance between performance and data accuracy:

• Strong Consistency: Guarantees most robust consistency but imposes higher latency.
• Bounded Staleness: Ensures reads lag behind writes by a configurable time or number of versions.
• Session Consistency: Provides consistency within a session for reads and writes.
• Consistent Prefix: Ensures reads never see out-of-order writes.
• Eventual Consistency: Offers the lowest latency but does not guarantee immediate consistency.

Choosing the right consistency level can help tailor the performance of Azure Cosmos DB according to your application’s requirements.

Cost Considerations

When designing data storage solutions with Azure Cosmos DB, analyze cost implications:

• Transaction Costs: Consider both capacity usage and the quantity of transactions.
• Storage Costs: Factors into the expense of maintaining denormalized or duplicate data to improve performance.

Implementation Tips

• Use JSON Format: Switching to JSON can significantly reduce payload sizes, enhancing application performance.
• Optimize Queries and Updates: Design tables and partitions for frequent updates or queries within the same partition to avoid scalability issues with atomic batch transactions.
• Handle Errors Wisely: Implement retry policies to manage transient errors effectively. For non-retryable errors, ensure correct handling without aggressive retries.

Next Steps

• Quickstart guides: Various resources are available to help you get started with Azure Cosmos DB integration.
• Understand specific Azure Stream Analytics outputs and their settings for efficient configuration.

By applying these principles and techniques, students prepared for the AZ-305 Exam will be equipped to design scalable, performant, and cost-effective solutions for semi-structured data using Azure Cosmos DB.