Serverless

In a nutshell, Continuous Verification comes down to making sure that DevOps teams put as many checks as possible into their CI/CD pipelines. These checks use external systems to validate the performance, security, and cost of your app without asking your engineers to do that manually. The systems that provide the data which decided whether your deployment goes to production or not, can also be used to help your engineers understand where the bottlenecks are in the process. With more checks in your automated pipeline, you have fewer manual tasks, less overhead, and better decisions to deploy to production or not. All that together means you get to spend more time at the beach!

At VMware we define Continuous Verification as:

“A process of querying external systems and using information from the response to make decisions to improve the development and deployment process.”

At #OSSDay, I got a chance to not only talk about what that means for serverless apps and how you can build it into your existing pipelines using tools like GitLab, CloudHealth, Wavefront and Gotling.

If you’ve read the blog posts on CloudJourney.io before, you’ve likely read the term “Continuous Verification”. If you haven’t that’s okay too. There’s an amazing article from Dan Illson and Bill Shetti on The New Stack explaining in detail what Continuous Verification is. In a nutshell, the Continuous Verification comes down to making sure that DevOps teams put as many checks as possible into their CI/CD pipelines. Adding checks into a pipeline means there are fewer manual tasks and that means you have access to more data tot smooth out and improve your development and deployment process.

So far we covered the tools and technologies, Continuous Integration, and Infrastructure as Code aspects of the ACME Serverless Fitness Shop. Now, it’s time to dive into observability!

If you’ve read the blog posts on CloudJourney.io before, you’ve likely read the term “Continuous Verification”. If you haven’t that’s okay too. There’s an amazing article from Dan Illson and Bill Shetti on The New Stack explaining in detail what Continuous Verification is. In a nutshell, the Continuous Verification comes down to making sure that DevOps teams put as many checks as possible into their CI/CD pipelines. Adding checks into a pipeline means there are fewer manual tasks and that means you have access to more data tot smooth out and improve your development and deployment process.

In part one we covered the tools and technologies and in part two we covered the Continuous Integration aspect of the ACME Serverless Fitness Shop. Now, it’s time to dive into Infrastructure as Code!

If you’ve read the blog posts on CloudJourney.io before, you’ve likely read the term “Continuous Verification”. If you haven’t that’s okay too. There’s an amazing article from Dan Illson and Bill Shetti on The New Stack explaining in detail what Continuous Verification is. In a nutshell, the Continuous Verification comes down to making sure that DevOps teams put as many checks as possible into their CI/CD pipelines. Adding checks into a pipeline means there are fewer manual tasks and that means you have access to more data tot smooth out and improve your development and deployment process.

If you’ve read the blog posts on CloudJourney.io before, you’ve likely read the term “Continuous Verification”. If you haven’t that’s okay too. There’s an amazing article from Dan Illson and Bill Shetti on The New Stack explaining in detail what Continuous Verification is. To make sure we’re all on the same page, though, I’ll quickly go over it as well. As a definition, Continuous Verification is “A process of querying external system(s) and using information from the response to make decision(s) to improve the development and deployment process.”.

The article I wrote with Bill Shetti on Tracking Distributed Errors in Serverless Apps is now available on DZone too 🙌 In this article, we demonstrate how to use Sentry.io to better monitor errors in serverless applications and save you some time while debugging your apps running on AWS Lambda.

Microservices give us as developers an incredible amount of freedom. We can choose our language and we can decide where and when to deploy our service. One of the biggest challenges with microservices, though, is figuring out how things go wrong. With microservices, we can build large, distributed applications, but that also means finding what goes wrong is challenging. It’s even harder to trace errors when you use a platform like AWS Lambda.

At VMware we define Continuous Verification as:

“A process of querying external systems and using information from the response to make decisions to improve the development and deployment process.”

At Serverless Nashville, I got a chance to not only talk about what that means for serverless apps but also how we use serverless in some of the business units at VMware.

I’ve looked at Pulumi to do a bunch of things, including creating subnets in a VPC, building EKS clusters, and DynamoDB tables. The one thing I hadn’t explored yet was how to deploy AWS Lambda functions using Pulumi, so that’s exactly what this blog is about.