Serverless

With everything going on in DevOps, I think we can safely say that building pipelines is the way to deploy your applications to production. But knowing what you deploy to production and whether it is actually okay needs more data, like security checks, performance checks, and budget checks. We’ve come up with a process for that, which we call Continuous Verification “A process of querying external systems and using information from the response to make decisions to improve the development and deployment process.” In this session, we’ll look at extending an existing CI/CD pipeline with checks for security, performance, and cost to make a decision on whether we want to deploy our app or not.

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!

Knative builds on Kubernetes to abstract away complexity for developers, and enables them to focus on delivering value to their business. The complex (and sometimes boring) parts of building apps to run on Kubernetes are managed by Knative. In this post, we will focus on setting up a lightweight environment to help you to develop modern apps faster using Knative.

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.”.

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.