Operations | Monitoring | ITSM | DevOps | Cloud

Why do some organizations excel in streamlining their database operations and applications development while others find it immensely challenging? Why can some database teams embrace agility while others take months of cycles to deploy even a single line of code? What secret sauce can allow some database teams to work smarter (not harder), streamline database development lifecycles better, get to deployment faster, and create an overall stronger alignment across departments?

As a former incident responder and now as a responder advocate for FireHydrant, I’ve seen the “build vs. buy” debate play out many times. In fact, I even supported the tool that former employers used for managing incidents for years before they decided to buy (more on that in a future blog post).

History will look back on this period of the 21st century as a pioneering, resilient, and excitingly disruptive time. We’re deep into a dynamic era as the cloud, Artificial Intelligence (AI), IT automation, and digital transformation converge to drive challenges and dazzling opportunities. The sheer force and potential of AI—coupled with unprecedented security risks and ongoing infrastructure advances will shape enterprises for years to come.

Distributed denial of service (DDoS) attacks are an ongoing issue for communications service providers, putting critical systems at risk, undercutting service level agreements, and bringing unwanted headlines. In the first half of 2022 6 million of these attacks were reported. Some metrics of DDoS attacks in 1H2022 compared to 2H2021.

DevOps is the modern convergence of people, processes and tools to create a continuous software delivery stream. Much like the code it produces, the concept of DevOps itself is continuously adapting to encompass new ideas, methods, and technologies. So what can we expect from the DevOps cultural shift in the future? The delivery stream of yesterday was a segmented process, often separating key teams and concepts into silos where people focused on their individual tasks.

Most well established data teams have a clear remit and a well defined structured for what they work on and when: from the scope of their role (from engineer to analyst) to which part of the business they work with. At incident.io, we have a 2 person data team (soon to be 3) with both of us being Product Analysts.

A guide to set practical Service Level Objectives (SLOs) & Service Level Indicators (SLIs) for your Site Reliability Engineering practices.

Looking at the report that Gartner did in 2022 regarding top technology trends, AI engineering represents an important pillar in the near future. It is composed of three core technologies: DataOps, MLOps and DevOps.The discipline’s main purpose is to develop AI models that can quickly and continuously provide business value. For instance, models that enable cross-functional collaboration, automation, data analysis, and machine learning.

On 8 November 2022, at Open Source Experience Paris, Canonical announced that Charmed Kubeflow, Canonical’s enterprise-ready Kubeflow distribution, now integrates with MindSpore, a deep learning framework open-sourced by Huawei. Charmed Kubeflow is an end-to-end MLOps platform with optimised complex model training capabilities designed for use with Kubernetes.