We rarely think about how many processes are running on our systems. Modern CPUs are powerful enough to run thousands of processes concurrently, but at what point do our systems become oversaturated? When you’re running large-scale distributed applications, you might reach this limit sooner than you'd expect. How can you determine what that limit is, and how does that affect the number and complexity of the workloads you deploy?

Monitoring Jenkins jobs and nodes is foundational to maintaining a robust, efficient, and secure CI/CD pipeline. It enables DevOps teams to stay proactive about system health, optimize performance, manage resources effectively, and adhere to security and compliance standards. In this article, we'll detail how to use the Telegraf agent to collect performance metrics from your Jenkins environment, and forward them to a datasource.

Large Language Models (LLMs) fall under the category of Generative AI (GenAI), an artificial intelligence type that produces content based on user-defined context. These models undergo training using an extensive dataset composed of trillions of combinations of words from natural language, enabling them to empower interactive and conversational applications across various scenarios.

The digital world comes with advantages and inherent risks. These IT incidents, which can encompass cyberattacks, system outages, and data breaches, can have a devastating impact. Beyond financial losses, IT incidents disrupt business operations, damage reputations, and erode customer trust. During an outage, having a well-prepared Incident Response Team (IRT) is essential to reduce downtime and improve response times.

Toggle topic hub menu Engineering teams of today use a plethora of tools to perform different functions in the software development life cycle. While tools like Slack, Teams, etc. are great for quick notifications, they rarely give you a comprehensive view of the things in current state. Sure, you can switch between tabs for all your tools but an "Engineering dashboard" that brings this all together makes it much easier to consume quickly and effectively.

The scalability of the cloud and its inherent variable cost has created financial and operational challenges, which demand the process of tracking varying costs in the dynamic Azure infrastructure at a granular business context level. Unit economics is a process of profit maximization in the cloud based on objective measurements like cost per product. This approach assesses how the organization is performing against its business goals.

I'm thrilled to announce the availability of Qovery on the AWS Marketplace. You can now buy and benefit from Qovery right from the AWS Marketplace. Before delving into the specific advantages of purchasing Qovery through the AWS Marketplace, let's first understand what the AWS Marketplace is and why this is something you should consider when purchasing Qovery.

As organizations increasingly migrate to the cloud, managing expenses efficiently becomes crucial. Traditional cost management methodsoften fall short in this environment, where resource allocation and usage can fluctuate dramatically. Enter Artificial Intelligence (AI) and Machine Learning (ML). These cutting-edge technologies are revolutionizing the way businesses approach cloud cost management.

Do not automatically trust server uptime metrics. These can be incomplete and inaccurate. Instead, it is wise to utilize a comprehensive observability platform to track several metrics that provide the full picture of your IT infrastructure. In this blog, we'll learn why.

Software development is a sophisticated process that comes with complexity built in. Enter DevOps (many years ago, of course) to foster an environment where developers can build complex applications while minimizing backend overhead, ensuring that processes are replicable and software design is standardized. One of the first examples of DevOps in action was the surge in popularity for service-oriented architecture.