AZ-305 Designing Microsoft Azure Infrastructure Solutions Exam

Recommend a solution for storing semi-structured data

Semi-structured data is data that does not follow a fixed schema but still contains tags or markers to separate elements. Examples include JSON, XML, and CSV files where records can vary in structure. The flexibility of semi-structured data makes it ideal for modern web applications that need to evolve quickly.

Azure offers several services to handle semi-structured data based on scale and consistency needs:

• Azure Cosmos DB for global distribution and multiple APIs (SQL, MongoDB, Cassandra).
• Azure Table Storage for simple key–attribute storage at low cost.
• Azure SQL Database with built-in JSON support for relational querying of semi-structured content.

When choosing a service, consider factors like throughput scalability, read/write latency, and cost per RU or transaction. For high-speed, globally distributed reads and writes, Cosmos DB is best. For simpler, cost-sensitive scenarios, Table Storage may suffice.

Recommend a solution for storing unstructured data

Unstructured data is any data without a predefined data model, such as documents, images, videos, and backups. It often requires a storage solution that can handle large binary objects and varied file formats. The lack of structure means storage must focus on capacity, access speed, and integration with analytics tools.

The primary Azure service for unstructured data is Azure Blob Storage, which offers:

• Hot, Cool, and Archive tiers to optimize cost based on access frequency.
• High throughput for large file uploads and downloads.
• Integration with Azure Data Lake for analytics workloads.

Key considerations include access patterns, data retention policies, and cost per gigabyte across tiers. Choose the Hot tier for frequent access, the Cool tier for infrequent access, and the Archive tier for long-term storage at the lowest cost.

Recommend a data storage solution to balance features, performance,and costs

Balancing features, performance, and cost requires understanding workload requirements and Azure pricing models. Different tiers offer varying performance levels, and replication options add resilience at extra cost. The goal is to match service capabilities with application needs without overspending.

Strategies to achieve balance include:

• Adjusting provisioned throughput or using autoscale in Cosmos DB.
• Selecting the right access tier in Blob Storage.
• Using Premium or Standard tiers in Azure databases based on I/O needs.

Trade-offs often involve choosing lower-cost tiers with higher latency versus premium tiers with guaranteed performance. By monitoring usage and adjusting service tiers, you can optimize both user experience and operational costs.

Recommend a data solution for protection and durability

Ensuring data protection and durability is critical to prevent loss from hardware failures, accidental deletes, or regional outages. Azure provides built-in features to keep data safe and recoverable. These features aim for high durability and robust disaster recovery.

Azure replication options include:

• Locally Redundant Storage (LRS) for three copies within one region.
• Zone-Redundant Storage (ZRS) for copies across availability zones.
• Geo-Redundant Storage (GRS) and Read-Access GRS (RA-GRS) for cross-region replication.

Additional protection features are:

• Soft delete to recover deleted blobs or tables.
• Snapshots for point-in-time recovery.
• Immutable storage policies for compliance and data retention.

Conclusion

In summary, designing data storage for semi-structured and unstructured data in Azure requires selecting the right service and tier based on data format, performance needs, and cost constraints. Semi-structured data often fits best in Cosmos DB or Table Storage, while unstructured data is stored in Blob Storage with tiered access. Balancing features and costs involves adjusting throughput and access tiers, and ensuring protection and durability relies on Azure’s replication options and recovery features. By understanding these concepts, you can build robust, cost-effective storage solutions in Azure.