The most important thing with building out any application is to think BIG. Build for ten users now and 10,000 users tomorrow. Having infrastructure that scales as your needs do is critical for user adoption—one of the many reasons we love a serverless approach and particularly AWS Lambda. The other part of any growth journey is managing access to organizational cloud infrastructure, especially with rapidly growing organizational development and DevOps teams.

I have been an AWS customer since 2010 and in the early days I, along with just about everyone else on AWS, spent a lot of my time just managing infrastructure. Patching AMIs, configuring load balancers, updating auto-scaling configurations, and so on. It was the sort of thankless task that no one cared about until something went wrong! The very definition of what Werner Vogel often refers to as “undifferentiated heavy-lifting”.

This blog will brief on vital ways to regain control of Azure cloud cost and to achieve cost optimization. To start with, let us have a short introduction about Azure cost management. As the growth towards cloud adoption is increasing in enterprises, it is getting more difficult for enterprises to manage the cost spent on the cloud across the organization.

One of the things we love about working in the cloud is the ease and scalability it brings to application development. It enables us to build out applications, APIs and any infrastructure that is needed from prototyping an idea, through to self scaling deployments. Monitoring and troubleshooting production-level serverless applications is always tricky, Especially working across a number of services and the many logs they can produce.

When building serverless applications on AWS Lambda, Amazon CloudWatch provides out-of-the-box metrics that measure the performance, errors, and duration of your functions. Although these standard Lambda metrics provide visibility into your serverless applications, it can also be invaluable to monitor custom metrics that are unique to your use case and application.

Sometimes it may not be easy to debug Azure Functions deployed on Azure since we cannot do inline debug through the Azure Portal. An option for us is to write information to the Logs console in key parts of our code to get insights into what’s happening inside our Function. We can do that using the following sentence: Another option, especially if we are developing our Azure Functions inside Visual Studio, is to run the Function locally.

Today I’m going to speak about another important Best practice, Tips, and Tricks that you need to know before you start to develop your Logic App solutions: Choose properly your developer tools.

Today I will talk about naming convention because I think this is so critical to implement from day one, and people tend to forget. We earlier addressed the importance of providing a good naming convention to your Logic App name and actions inside your business process. Today we will speak about the Connectors (or API Connections) naming convention.