Operations | Monitoring | ITSM | DevOps | Cloud

Accidental DBA is a term most IT pros are familiar with, and traditionally it describes an “on-top task” for the SysAdmin. It was followed by the “involuntary security admin,” a task carried out by network admins, as back in the days it was all about securing the perimeter. Now there are the clouds, but whose responsibility is it? Surely the SysAdmin again, as it’s just about infrastructure? But then there’s VPNs and BGP and load balancers—network territory!

How much does your Kubernetes service cost to operate? This seems like a simple question, right? It’s one thing to say how much your Kubernetes cluster itself costs to operate — that, after all, is a group of real servers, associated with a specific number.

We’ve noticed interesting threads floating around the internet asking how would you explain serverless to a toddler. As it happens, we just turned three years old and to mark the occasion, we decided to take up the challenge and wrote a bedtime story, for serverless enthusiasts of all ages, about how serverless came to be, from the very beginning. It is a magical tale of the ingenious, life-changing journey that got us to a new universe called Serverless.

Data centers and cloud are undoubtedly an integral part of business continuity strategies of companies to run operations with a distributed workforce amid the lockdown successfully. The Covid-19 outbreak and the resultant work from home policies have pushed businesses across globe increase in adoption of cloud infrastructure services up by 80%. Managing the entire suite of enterprise technology is not easy for businesses that have several vendors on board.

One of the big problems we hear about with Azure is managing costs and understanding where the money is being spent. In fact, when we launched SquaredUp for Azure back in 2019, the ability to visualize costs quickly became one of the most popular features. It helped our customers (and ourselves, too) get a grip on Azure costs – by making it easy to identify under-utilized resources and take the appropriate action to reduce costs.

This blog explains a peculiar issue faced when Azure Durable Functions are deployed using Azure DevOps. In case you need to know what Azure Durable Functions are, please refer to Microsoft’s documentation on Durable Functions here as the explanation of it, is not under the scope of this blog. Durable functions work best when built with versions .NET Core 2.2 and above. After developing and testing a durable function, it can be deployed to Azure in many ways.

We’ve heard from customers about how important it is to be able to reliably operate your applications and infrastructure running on Google Cloud. In particular, observability is critical to reliable operations. To help you quickly gain insight into your Google Cloud environment, we’ve added 21 new features to Cloud Operations, the observability suite we launched earlier this year, which gives you access to all our operations capabilities directly from the Google Cloud Console.

One of the best things about cloud computing is how it converts technical efficiencies into cost-savings. Some of those efficiencies are just part of the tool kit, like pay-per-use Lambda jobs. Good DevOps brings a lot of savings to the cloud, as well. It can smooth out high-friction state management challenges. Sprucing up how you provision cloud services, for example, speeds up deployments. That’s where treating infrastructure the same as workflows from the rest of your codebase comes in.

Some time ago, an ex-colleague of mine at DAZN received an alert through PagerDuty. There was a spike in error rate for one of the Lambda functions his team looks after. He jumped onto the AWS console right away and confirmed that there was indeed a problem. The next logical step was to check the logs to see what the problem was. But he found nothing. And so began an hour-long ghost hunt to find clues as to what was failing and why there were no error messages.