Azure cloud design patterns are proven solutions to common cloud computing challenges in building scalable, resilient, and highly available cloud-based applications. Here is a list of Azure cloud design patterns with their summary and categories:
| Pattern | Summary | Category |
|---|---|---|
| Cache-aside | Load data on demand into a cache from a data store | Data Management |
| Circuit Breaker | Handle faults that may take a variable amount of time to recover from when connecting to a remote service or resource | Resiliency |
| Compensating Transaction | Undo the work performed by a series of steps, which together define an eventually consistent operation, that has completed with a failure or an exception | Resiliency |
| Competing Consumers | Enable multiple concurrent consumers to process messages received on the same messaging channel | Messaging |
| Compute Resource Consolidation | Increase the efficiency of compute resources by dynamically allocating them between different applications based on demand | Management and Monitoring |
| Command and Query Responsibility Segregation (CQRS) | Separate the part of the application that writes to the database from the part of the application that reads from the database | Data Management |
| Event Sourcing | Use an append-only store to record the full series of events that describe actions taken on data in a domain | Data Management |
| External Configuration Store | Move configuration information out of the application deployment package to a centralized location | Management and Monitoring |
| Gateway Routing | Route requests to multiple services using a single endpoint and a routing table | Management and Monitoring |
| Health Endpoint Monitoring | Implement functional checks in an application that external tools can access through exposed endpoints at regular intervals | Management and Monitoring |
| Index Table | Create an index for fast read access to large, tabular data sets | Data Management |
| Leader Election | Coordinate a distributed system by electing a leader process | Coordination |
| Materialized View | Generate pre-populated views over the data in one or more data stores to improve read performance | Data Management |
| Pipes and Filters | Decompose a task that performs complex processing into a series of separate elements that can be reused | Data Management |
| Priority Queue | Ensure that the most important jobs are processed first in a scalable manner | Messaging |
| Publisher/Subscriber | Enable an application to notify one or more decoupled subscribers that state has changed | Messaging |
| Queue-Based Load Leveling | Enable a system to handle temporary increases in load by using a queue that acts as a buffer between the request and the service that handles it | Resiliency |
| Retry | Enable an application to handle transient failures when it tries to connect to a service or network resource by transparently retrying an operation that's previously failed | Resiliency |
| Runtime Fabric | Host and scale microservices in a Kubernetes cluster | Management and Monitoring |
| Sharding | Partition data to scale out a database | Data Management |
| Static Content Hosting | Host and deliver static web content with high scalability and redundancy | Web and Mobile |
| Throttling | Regulate resource consumption, protect resources from being over-consumed, and provide status information about rate limiting | Resiliency |
| Valet Key | Use a temporary credential or key that's provided to a third party to access a specific Azure service | Security |
| Zone-Redundant Services | Distribute a service across multiple zones to protect against data center-level failures | Resiliency |
Azure cloud design patterns are used to provide guidance on how to design and implement cloud solutions on the Microsoft Azure platform. These patterns provide a set of best practices and proven solutions to common challenges faced when building cloud applications. By using these patterns, developers and architects can save time and ensure their cloud solutions are reliable, scalable, and performant. The patterns also help to ensure that the solutions are secure, resilient, and cost-effective.
Below are some potential pros and cons of using Azure cloud design patterns:
Pros:
- Reusability: Design patterns promote code reuse, which can help to reduce the amount of code you need to write and improve development efficiency.
- Standardization: Design patterns provide a standardized approach to solving common problems, which can help to ensure that your code is consistent and maintainable.
- Best Practices: Azure cloud design patterns are based on best practices and proven techniques, which can help to ensure that your code is reliable and efficient.
- Improved scalability and performance: Azure cloud design patterns are often designed to improve scalability and performance, which can help to ensure that your application can handle increasing traffic and user demands.
- Cost savings: By using design patterns, you can often reduce development time and improve the efficiency of your application, which can lead to cost savings over time.
Cons:
- Complexity: Design patterns can be complex and difficult to understand, especially for inexperienced developers.
- Over-engineering: Overusing design patterns or applying them inappropriately can lead to over-engineering, which can increase complexity and reduce maintainability.
- Dependency: Relying heavily on design patterns can create a dependency on third-party libraries, which can introduce security and performance risks.
- Time-consuming: It can take time to learn and apply design patterns, which can delay the development process.
- Limited applicability: Not all design patterns will be applicable to all projects, and in some cases, custom solutions may be needed instead.
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