Operations | Monitoring | ITSM | DevOps | Cloud

Welcome to the Serverless world. One of the first things you’ll hear about is AWS Lambda - and you’ll continue to keep hearing about it! While architecture can be serverless without Lambdas involved, it’s very often the key component within a serverless application. In the first post of this 3-part AWS Lambda Handbook series, we run through what is AWS Lambda, dialling back to basics with the various terminology, how to create a Lambda function and how to run it.

Cloud adoption has continued to push the momentum on digital transformation. The initial apprehension within enterprises on managing the disruption caused by the ongoing pandemic has slowly waned. Enterprises now have a clearer picture of the situation and are moving forward. The need for online collaboration and online meetings has forced most enterprises to rely more on cloud services for managing their workforce and to provide an environment that allows employees to work remotely.

More and more companies are beginning to turn to DevOps and the cloud as a way to improve their software teams. Whilst it used to be that development and operations were seen as separate, that view has now changed. Linking the two leads to better communication, faster development times, and the ability to stay on top of things.

We have explored how we can deploy Machine Learning models using AWS Lambda. Deploying ML models with AWS Lambda is suitable for early-stage projects as there are certain limitations in using Lambda function. However, this is not a reason to worry if you need to utilize AWS Lambda to its full potential for your Machine Learning project. When working with Lambda functions its a constant worry about the size of deployment packages for a developer.

Working with Serverless computing is like riding an electric bike. You get speed, flexibility, automatic assistance to scale with ease. Development is usually hassle-free because you can focus on code and only pay for usage of the service. Except when your users hit an error. Debugging that issue feels like your bike’s battery just died while climbing a steep hill.

Many businesses use cloud services on AWS and also run workloads on Kubernetes and Knative. Today, it’s difficult to integrate events from AWS to workloads on a Rancher cluster, preventing you from taking full advantage of your data and applications. To trigger a workload on Rancher when events happen in your AWS service, you need an event source that can consume AWS events and send them to your Rancher workload.

Technology is evolving exponentially, and with it the size of the data that needs to be saved increases and the great need to access them quickly, easily and, above all, from anywhere. Every day that goes by, the use of cloud becomes even more essential for companies. For that reason, cloud computing services have started to emerge in order to meet these needs. Among them, Azure.

If it wasn’t clear before the global pandemic, it is now: Cloud applications can keep businesses moving forward. During this time, people have been able to shop, connect with friends and, of course, work from home.

In this article I’ll be sharing my own lessons learned with Dashbird in building reliable Well-Architected serverless applications and the highest impact areas to focus on no matter where you are in the Serverless journey.