Serverless Microservices: Not a New Concept
Serverless microservices are increasingly relevant as technology leaders seek to make software more performant, cost-effective, and easy to deploy.
If you're on the fence about using them, it's understandable. They excel in specific scenarios but come with their own challenges. At Lineate, we've spent years working with serverless microservices, and we've learned when they work best, when they don't, and what to watch out for during implementation.
In this brief guide, we'll cover these key points to help you decide if serverless microservices are right for your application.
What are the benefits of using serverless microservices?
Microservices allow each of your teams to work on updating their subset of the larger application without testing or deployment conflicts during testing and deployment. Serverless makes it easy to automate and right-size those server instances, which can add up to tremendous cost savings. Plus, you get the combined benefits of the on-demand horizontal scalability of microservices with the easy auto-scaling of serverless. That means there’s no need to provision and maintain dedicated hardware or virtual machine instances. And when you want to upgrade, tweak or maintain your applications, using serverless microservices means your builds are less likely to break when you make changes.
Not sure if serverless microservices are right for your project?
Book a free consultation with our solutions architects.
Book consultationServerless Microservices in Large-Scale Data Management
When handling large data sets or long-running processes, serverless functions may not be the best choice.
Serverless microservices can serve as effective orchestrators for large-scale data processing by using an event-driven approach. An event triggers a serverless function to start a batch process, which then invokes additional serverless functions to manage downstream tasks.
This event-driven architecture is more robust and less error-prone than traditional systems that rely on independently scheduled jobs, eliminating the need to manage complex time buffers between dependent tasks.
