Operations | Monitoring | ITSM | DevOps | Cloud

Logic Apps are a gift to the integration of business scenarios as they are quite easy to understand and integrate with other systems but there are some challenges faced by the users in real-time.

One of the mistakes organizations make related to Kubernetes costs is addressing them primarily after-the-fact, once the application is running successfully. There are certainly changes that can be made to improve efficiency once the application is running, but cost-control measures are best considered at the beginning, not middle or end, of the application lifecycle.

When organizations think about the relationship between Kubernetes and cloud costs, they often focus on Kubernetes’ auto-scaling capabilities and what this means for optimizing compute resources. Kubernetes does allow organizations to provision compute resources more thinly, because the platform allows them to scale up automatically if there’s a demand spike in the middle of the night.

In this article, we are going to look at two new visualizations from SquaredUp for Azure: Summary Grid and Summary Bar. Read on to learn how exactly they can help you easily summarize your Azure resources.

With the advent of cloud technologies, there has been a major shift in the way organizations handle infrastructure operations and service delivery. The increasing shift towards cloud-based and hybrid cloud ecosystems for business operations has made it necessary for organizations to employ cloud monitoring tools that monitor the performance and efficiency of these cloud-hosted services.

Azure Kubernetes Service, Microsoft's managed Kubernetes solution, allows you to quickly create a Kubernetes cluster in Microsoft Azure and provides features to help you manage and maintain your Kubernetes cluster in Azure. In this blog post we will go over some of the features of AKS and then walk through creating an AKS cluster.

When you build your application on top of Lambda, AWS automatically scales the number of “workers” (think containers) running your code based on traffic. And by default, your functions are deployed to three Availability Zones (AZs). This gives you a lot of scalability and redundancy out of the box. When it comes to API functions, every user request is processed by a separate worker. So the API-level concurrency is now handled by the platform.

Amazon Web Services is the world’s biggest cloud platform, and businesses of all shapes and sizes use it every day to run their businesses. You may find this surprising, but AWS accounts for more than half of Amazon's operating income. Thus, Amazon has a vested interest in getting as many people to use AWS as possible, so it offers a whole bunch of tools to make it easy to use. AWS Amplify is one of these.

If you’ve run across other articles I’ve written about hybrid cloud, you’ll notice a familiar theme: it’s an artifact of other technology decisions nobody asked for. As such, it’s left up to IT operations to figure out how to know if it’s working well, cost-effectively, or as expected in the first place. However, tools are improving rapidly, and IT teams are gaining skill with the model and technologies.

Serverless applications, more often than not, have logic distributed over multiple functions and services, which with growth and agents and wrappers attached, can get more complex and costly. This is where Serverless monitoring comes in to help. But what is Serverless monitoring? Serverless monitoring allows developers to gain important insight on what happens during each execution and event, errors become more easily visible and measuring resource consumption for each invocation is possible.