Recommend a solution for storing semi-structured data
5 minutes
5 Questions
When recommending a solution for storing semi-structured data in Azure, Azure Cosmos DB stands out as the premier choice for most scenarios. Semi-structured data includes formats like JSON, XML, and key-value pairs that don't conform to rigid relational schemas but still maintain some organizationa…When recommending a solution for storing semi-structured data in Azure, Azure Cosmos DB stands out as the premier choice for most scenarios. Semi-structured data includes formats like JSON, XML, and key-value pairs that don't conform to rigid relational schemas but still maintain some organizational structure. Azure Cosmos DB offers multiple APIs including SQL, MongoDB, Cassandra, Gremlin, and Table, providing flexibility in how you interact with your data. It delivers single-digit millisecond latency, automatic indexing, and global distribution capabilities. For applications requiring massive scale and low latency across geographic regions, Cosmos DB excels with its turnkey global replication and guaranteed 99.999% availability. Azure Blob Storage with JSON files represents a cost-effective alternative for scenarios where you need to store large volumes of semi-structured data with less frequent access patterns. Combined with Azure Data Lake Storage Gen2, this approach works well for analytics workloads. Azure Table Storage provides a NoSQL key-attribute store suitable for simpler semi-structured datasets. It offers cost efficiency for applications needing flexible schemas and fast key-based lookups, though it lacks the advanced querying capabilities of Cosmos DB. When selecting between these options, consider throughput requirements, consistency needs, query complexity, global distribution requirements, and budget constraints. For mission-critical applications requiring low latency and global presence, Cosmos DB is ideal. For analytical workloads with batch processing, Data Lake Storage proves more appropriate. For simpler applications with basic query needs, Table Storage offers excellent value. Additionally, Azure SQL Database supports JSON data types, making it suitable when you need to combine relational and semi-structured data within a single database solution. This hybrid approach works well for applications transitioning from traditional relational models while incorporating flexible schema elements.
Recommend a Solution for Storing Semi-Structured Data
Why This Is Important
Semi-structured data represents a significant portion of modern enterprise data, including JSON documents, XML files, sensor data, and social media feeds. As an Azure Solutions Architect, you must understand how to select appropriate storage solutions that balance performance, cost, scalability, and query capabilities. The AZ-305 exam tests your ability to recommend the right Azure service based on specific requirements.
What Is Semi-Structured Data?
Semi-structured data is information that does not conform to a rigid schema like relational databases but contains tags, markers, or other elements that separate semantic elements and enforce hierarchies. Common examples include:
• JSON documents - API responses, configuration files • XML files - Legacy system integrations • Key-value pairs - Session states, user preferences • Graph data - Social networks, recommendation engines • Time-series data - IoT telemetry, application logs
Azure Services for Semi-Structured Data
Azure Cosmos DB The primary choice for globally distributed, multi-model semi-structured data. Key features include: • Multiple APIs: Core SQL, MongoDB, Cassandra, Gremlin, Table • Global distribution with multi-region writes • Guaranteed single-digit millisecond latency • Automatic indexing of all data • Five consistency levels
Azure Blob Storage Ideal for storing large volumes of unstructured and semi-structured data files: • Cost-effective for large-scale storage • Tiered storage (Hot, Cool, Archive) • Integration with Azure Data Lake Storage Gen2
Azure Table Storage Simple key-value storage for less complex scenarios: • Low cost for large datasets • Schema-less design • Limited query capabilities compared to Cosmos DB
Azure Data Lake Storage Gen2 Optimized for big data analytics workloads: • Hierarchical namespace for efficient data organization • Integration with analytics services like Synapse and Databricks • Supports Parquet, Avro, and JSON formats
How to Choose the Right Solution
Consider these factors when recommending a solution:
1. Latency Requirements - Choose Cosmos DB for sub-millisecond response times 2. Global Distribution - Cosmos DB offers turnkey global replication 3. Query Complexity - Cosmos DB SQL API for rich queries; Table Storage for simple lookups 4. Data Volume and Cost - Blob Storage or Table Storage for cost-sensitive, high-volume scenarios 5. Analytics Workloads - Data Lake Storage Gen2 for big data processing 6. Existing Application Compatibility - Use MongoDB API or Cassandra API in Cosmos DB for migrations
Exam Tips: Answering Questions on Semi-Structured Data Storage
• Look for global distribution requirements - This strongly indicates Azure Cosmos DB
• Identify latency specifications - Single-digit millisecond requirements point to Cosmos DB
• Watch for API compatibility hints - Questions mentioning MongoDB or Cassandra suggest Cosmos DB with the appropriate API
• Consider cost constraints - Budget-sensitive scenarios with simple access patterns may favor Table Storage or Blob Storage
• Analytics focus - Questions involving Spark, Synapse, or big data processing suggest Data Lake Storage Gen2
• Consistency requirements matter - If the question mentions eventual consistency vs strong consistency, Cosmos DB offers configurable consistency levels
• Read the scenario for data types - Graph relationships suggest Gremlin API; document stores suggest SQL or MongoDB API
• Time-series data - Consider Azure Data Explorer for high-volume telemetry and log analytics
• Eliminate incorrect options - Azure SQL Database is designed for structured relational data, not semi-structured scenarios