I use these top proven architectural patterns in AWS
Pick from these for your software project on AWS
Hey there 👋 - Amrut here!
Happy Sunday to all synced with The Tech Pulse!
I have been working as an AWS Cloud Architect for 3+ years.
Part of my role is making critical architecture decisions that can have a lasting impact on the business.
The sheer number of AWS's services makes it overwhelming to choose a good architectural pattern for your software project.
You're not alone.
A wrong architecture decision can lead to potential bottlenecks, increased costs, or system failures.
In today’s newsletter, I'll explore the top most-used architectural patterns for designing software on AWS I usually lean on before looking to anything else.
I’ll dive into each pattern, offering what it is, use cases, and examples to help you make the best decision for your needs.
By the end, you'll have a toolbox of architecture patterns you can pick from for building robust, scalable, and cost-effective applications on AWS.
Let’s dive in!
Serverless Architecture
You can build applications without managing servers with AWS Lambda, API Gateway, or AppSync. Lambda handles the execution of your code in response to triggers like HTTP requests, file uploads, or database changes.
For example, you can create a serverless image resizer that triggers whenever a new image is uploaded to an S3 bucket.
This pattern is ideal for sporadic or event-driven workloads where you want to focus on code rather than server upkeep. This reduces operational overhead and can be cost-effective.
Microservices Architecture
This pattern divides an application into loosely coupled, independently deployable services.
AWS offers ECS (Elastic Container Service) and EKS (Elastic Kubernetes Service) for orchestrating these services.
For example, an e-commerce platform could consist of independent services for inventory, cart, payment, and user profiles.
This pattern is helpful when your application is complex, needs to scale horizontally, or when different teams are responsible for different application components.
Multi-Tier (N-Tier) Architecture
This involves separating the application into tiers, often a web, application, and database layer. Each tier can scale independently.
A blogging platform might use EC2 instances for the application logic, RDS for the database, and ELB (Elastic Load Balancer) for distributing incoming traffic.
This pattern is useful for traditional web applications that require clear separation between their data, logic, and user interface components for better scalability and maintainability.
Event-Driven Architecture
You can use services like Amazon Kinesis, EventBridge, SQS (Simple Queue Service) to help manage, store, and process data streams in real-time.
For example, an IoT application can stream sensor data to Kinesis for real-time analytics.
Use this pattern when processing high-velocity data streams, such as analytics, monitoring, or data aggregation, in real-time or near real-time.
Data Lake Architecture
Data Lakes are used for storing raw, unstructured data.
Amazon S3 is a robust solution for storing this data, with querying facilitated by services like Athena or Redshift.
For example, a healthcare organization could store patient records, lab results, and imaging data in an S3 data lake.
This pattern is a great option when dealing with enormous volumes of unstructured or semi-structured data that require a flexible and cost-effective storage solution.
Cache-Aside Architecture
You can improve data retrieval performance by using services like ElastiCache alongside a primary database.
Data is read from the cache and, if not present, is fetched from the database and then cached.
For instance, an e-commerce website can cache product details that users frequently view.
This pattern is particularly beneficial for read-heavy applications requiring low latency and high throughput.
Backup and Restore Architecture
Ensuring data durability and availability through backup mechanisms is crucial.
AWS Backup or simple S3 bucket, RDS database snapshots can provide automated backups, while Glacier is ideal for long-term archival.
For example, a financial institution could set automated backups for transaction databases every night.
This pattern is universally applicable for any scenario where data loss is unacceptable, and you need to ensure data durability and availability.
Selecting the correct architectural pattern is a critical step in designing robust, scalable, and maintainable applications on AWS.
Whether you're building a serverless application, managing complex microservices, or dealing with enormous volumes of data, AWS offers a variety of architectural patterns tailored to your specific needs.
By understanding the key attributes, use cases, and nuances of these top 7 architectural patterns, you can make informed decisions that will set the stage for the success of your project.
Happy building!
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