Describe Considerations for Working with Non-Relational Data on Azure
Understand Azure storage capabilities for blobs, files, and tables, and describe Azure Cosmos DB features, use cases, and APIs.
This domain covers non-relational data storage options in Azure. Candidates must describe capabilities of Azure storage including Azure Blob storage for unstructured object data with hot, cool, and archive access tiers; Azure File storage for fully managed cloud file shares; and Azure Table storage for semi-structured NoSQL key-value data. The domain also covers Azure Data Lake Storage Gen2 as a foundation for big data analytics. Additionally, candidates must describe capabilities and features of Azure Cosmos DB — identifying appropriate use cases for globally distributed, multi-model databases, and describing Azure Cosmos DB APIs including the API for NoSQL, API for MongoDB, API for PostgreSQL, API for Apache Cassandra, API for Table, and API for Apache Gremlin. (15–20% of exam)
5 minutes
5 Questions
When working with non-relational data on Azure, there are several important considerations to keep in mind. Non-relational data, also known as NoSQL data, differs from traditional relational databases in that it does not rely on structured tables with fixed schemas. Azure offers multiple services to handle non-relational data, primarily through Azure Cosmos DB, Azure Blob Storage, Azure Table Storage, and Azure Data Lake Storage.
**Data Structure:** Non-relational data can be semi-structured (JSON, XML), unstructured (images, videos, documents), or key-value pairs. Understanding your data's nature is critical for choosing the right storage solution. Azure Cosmos DB supports document, key-value, graph, and column-family data models, making it highly versatile.
**Scalability and Performance:** Azure non-relational services are designed for massive scalability. Azure Cosmos DB offers globally distributed, multi-region replication with guaranteed low latency. You must consider throughput requirements, measured in Request Units (RUs), and partition strategies to optimize performance and cost.
**Consistency Models:** Unlike relational databases that typically offer strong consistency, Azure Cosmos DB provides five consistency levels ranging from strong to eventual consistency. Choosing the right level involves balancing between data accuracy and performance.
**Cost Management:** Pricing for non-relational services varies based on storage consumed, throughput provisioned, and data transfer. It is essential to plan capacity effectively to avoid unexpected costs, particularly with provisioned throughput models.
**Security and Compliance:** Azure provides encryption at rest and in transit, role-based access control (RBAC), and virtual network integration to secure non-relational data. Compliance certifications ensure regulatory requirements are met.
**Data Access and APIs:** Azure Cosmos DB supports multiple APIs including SQL, MongoDB, Cassandra, Gremlin, and Table APIs, allowing developers to use familiar tools and languages.
**Use Cases:** Non-relational data solutions are ideal for IoT data ingestion, real-time analytics, content management, and applications requiring flexible schemas and horizontal scaling. Proper consideration of these factors ensures efficient, cost-effective, and secure non-relational data management on Azure.When working with non-relational data on Azure, there are several important considerations to keep in mind. Non-relational data, also known as NoSQL data, differs from traditional relational databases in that it does not rely on structured tables with fixed schemas. Azure offers multiple services t…
