Quick Reference – Azure Design Patterns – Data Management Patterns

Data management is the key element of cloud applications and influences most of the quality attributes. Data is typically hosted in different locations and across multiple servers for reasons such as performance, scalability or availability, and this can present a range of challenges. For example, data consistency must be maintained, and data will typically need to be synchronized across different locations.

Cache-Aside pattern

Load data on demand into a cache from a data store. This can improve performance and also helps to maintain consistency between data held in the cache and data in the underlying data store.

Cache Aside Design Pattern Illustration

Command and Query Responsibility Segregation (CQRS) pattern

Segregate operations that read data from operations that update data by using separate interfaces. This pattern can maximize performance, scalability, and security; support evolution of the system over time through higher flexibility; and prevent update commands from causing merge conflicts at the domain level.

CQRS Design Pattern Illustration

Event Sourcing pattern

Use an append-only store to record actions taken on data, rather than the current state, and use the store to materialize the domain objects. In complex domains this can avoid synchronizing the data model and the business domain; improve performance, scalability, and responsiveness; provide consistency; and provide audit history to enable compensating actions.

Event Sourcing Design Pattern Illustration

Index Table pattern

Create indexes over the fields in data stores that are frequently referenced by queries. This pattern can improve query performance by allowing applications to more quickly locate the data to retrieve from a data store.

Index Table Design Pattern Illustration

Materialized View pattern

Generate prepopulated views over the data in one or more data stores when the data isn’t ideally formatted for required query operations. This can help support efficient querying and data extraction, and improve application performance.

Materialized View Design Pattern

Sharding pattern

Divide a data store into a set of horizontal partitions or shards. This can improve scalability when storing and accessing large volumes of data.

Sharding Design Pattern Illustration

Static Content Hosting pattern

Deploy static content to a cloud-based storage service that can deliver them directly to the client. This can reduce the need for potentially expensive compute instances.

Static Content Design Pattern Illustration

Valet Key pattern

Use a token that provides clients with restricted direct access to a specific resource, in order to offload data transfer from the application. This is particularly useful in applications that use cloud-hosted storage systems or queues, and can minimize cost and maximize scalability and performance.

Valet Key Design Pattern Illustration