Whether you are new to the Cloud, mid-transition, or a professional at cloud or hybrid systems, no one likes being bothered with useless alerts. The options are simple: If you take the approach of ignoring the alert like a bad cold-call, you risk the chance of missing a critical alert and watching your system crash around you. No one likes to open their inbox to a few hundred alerts they have been ignoring.

In previous and great articles in here, we already talked about what Cloud is and the types that exist floating above our heads. But today, for a change, we want to focus more on one of its totally forgotten key aspects: Are clouds actually made of childish dreams and cotton candy? No! Just kidding! Today we are going to talk in “Pandora FMS discussions” about what cloud service types there are.

In the second episode of the Shifting Left podcast, we sat down with one of our resident cloud experts, Vikram Parmar, to get his take on how IT leaders can avoid the often-times spiraling cost of cloud investment.

The modern enterprise has expanded its reach by using the power of cloud computing. However, with that power comes complexity in leveraging the multiple platforms needed to provide rich functionality. To achieve a seamless integration that involves multiple cloud infrastructures you need insightful and actionable data. You also need the right team to bring the clouds together in a seamless, effective, and efficient manner.

As organizations build out their serverless footprint, they might find themselves managing hundreds or thousands of individual components (e.g., Amazon S3 buckets, Amazon DynamoDB tables, AWS SQS queues) for just a single application. At the same time, performance issues can crop up at any of these points, which means that having access to detailed observability data from your serverless functions is crucial for effective troubleshooting.

To me personally, when I think programming languages I think JavaScript and while 67% of the developers out there might think the same (at first) that does not imply it’s the most efficient language to use with AWS Lambda. So without further ado, here we go.

There has been increasing buzz in the past decade about the benefits of using a microservice architecture. Let’s explore what microservices are and are not, as well as contrast them with traditional monolithic applications. We’ll discuss the benefits of using a microservices-based architecture and the effort and planning that are required to transition from a monolithic architecture to a microservices architecture.

Minimizing costs, reducing risk, and maximizing business value—all at the same time—requires a delicate balancing act. It’s not a new challenge, nor is it unique to IT infrastructures. But when it comes to the cloud, especially in hybrid cloud scenarios, it requires you to understand the performance, risk/compliance, and cost impacts of your current resource allocations and then adjust to maintain the optimal decisions to meet your SLA and budget targets.

To put it simply, serverless computing is a cloud computing execution model meaning that the cloud provider is dynamically managing the distribution of computer’s resources. What’s taking up valuable computing resources is the function execution. Both AWS and Azure charge more if you have a combination of allocated memory and the function execution elapse time which is rounded up to 100ms.

I was speaking with a VP of Engineering friend at last year’s KubeCon about how to pitch Kubernetes to the C-Suite. The benefits for innovation were clear - containerized microservices empowered her small teams to deliver more value, more rapidly. As is often the case with Boardroom discussions, though, the question of cost was always next. Sure, they want you to innovate - as long as it’s within the constraints of a budget! But cost discussions around Kubernetes can be difficult.