Operations | Monitoring | ITSM | DevOps | Cloud

Azure Functions is Microsoft’s solution to serverless computing. While it actually does run on servers, the key difference here is that you aren’t responsible for maintaining the function hosting environment. This is both a blessing and a curse.

Today at Think 2019 we’re announcing our intent to work with IBM to support the Sysdig Cloud-Native Intelligence Platform on IBM Cloud Private and IBM Multicloud Manager. By supporting IBM Multicloud Manager and IBM Cloud Private, Sysdig will help IBM customers accelerate the transition to cloud architectures.

This past couple of month were a bit hectic and there’s a good reason behind that. We’ve set out to create a better experience for our users, and it’s exactly what we did! Besides making a lot of quality-of-life changes, we’ve introduced new features that we think, will speed up the speed at which you’ll debug your applications and give you a whole new perspective on all things AWS Lambda.

Serverless has, for the last year or so, felt like an easy term to define: code run in a highly managed environment with (almost) no configuration of the underlying computer layer done by your team. Fair enough, but what is is a serverless application? A Lambda isn’t an app by itself, heck, it can’t even communicate with the world outside of Amazon Web Services (AWS) by itself, so there must be more to a serverless app than that.

It’s the celebration time of the year! Dashbird is now an official Amazon Web Services (AWS) Advanced Technology Partner (APN). Our team has demonstrated that we can build a strong, secure and reliable AWS-based business that can benefit Amazon Web Services users. The new partner status is recognition of Dashbird ability to help companies to better monitor and troubleshoot their Lambdas by providing state of the art solution for serverless observability.

Today we’ll talk more about the cost tracking for serverless architecture. Following the cost of serverless architecture utilization it might give you insights on how and what should you change or adapt to, to be able to reduce the cost. After all, we’re all always trying to improve our performances in every field while keeping the costs to a minimum (if possible).

With Azure Virtual Machine (VM) Scale Sets you can automatically scale the number of VMs running an application based on the compute resources required. VM Scale Sets make it easier to deploy and manage a large number of Virtual Machines consistently and allow you to use and pay for the minimum resources needed at any given time, but they also introduce a few monitoring challenges.

When we talk about cloud providers, we often forget that not all data is the same — even in the same application, while we might think of this data as from a “financial application” or a “computation process”, the reality is that each data set has subsets upon subsets, and thus require specific applications to manage them.