Operations | Monitoring | ITSM | DevOps | Cloud

In the early days, people debated how safe it was to store their money in the bank; now, we debate running databases on Kubernetes. Over the years, Kubernetes has evolved significantly, transforming into a capable platform for handling various workloads, including stateful ones. In this blog, I will consolidate some of the best arguments from both sides and provide you with some points to discuss with your team lead in your next conversation. It's an interesting topic with varying answers.

Did you know that, on average, 70% of online shoppers abandon their carts? Yep, seven out of ten people leave without making a purchase. That's according to the Baymard Institute, which analyzed data from 49 different sources. Crazy, right?

Checkly is the industry’s best tool to monitor your production applications. With the power of playwright, developers can test the systems they’ve developed, and roll out those tests as production monitors running from multiple geographies on the Checkly system. And Checkly monitors thousands of API endpoints with complex validation, setup and cleanup scripts, and reliable alerting. So why are we expanding into TCP-based checks?

Modern applications increasingly rely on specialized databases and platforms to power real-time analytics and support advanced AI/ML capabilities. These tools help teams accelerate development by consolidating workflows and processes, enabling faster and more efficient data operations. That’s why Datadog has launched three new data platform integrations with Supabase, DuckDB, and Milvus.

With thousands of logs generated every minute from your infrastructure, applications, services, and devices, retaining all of this data for active search and analysis can be cost-prohibitive. Because log volumes continue to grow rapidly as operations scale, it’s common for organizations to implement log management strategies and limit the amount that they store in order to minimize costs.

So you had a great day playing with really awesome new tech, solving big business challenges, and feeling like you really nailed it. Then you wake up the next day to an alert from Azure telling you you've blown your monthly budget and its only the first week of the month. We've all been there... right? Using any cloud service comes with a cost, but for most services the budget risk is low. Cost calculated daily isn't a problem when usage is predictable, but not everything works like that.

OpenTelemetry (often abbreviated as OTel) is the golden standard observability framework, allowing users to collect, process, and export telemetry data from their systems. OpenTelemetry’s framework is organized into distinct signals, each offering an aspect of observability. Among these signals, OpenTelemetry metrics are crucial in helping engineers understand their systems.

Organizations around the world use Microsoft Azure to power their businesses. The cloud computing platform includes hundreds of products and services organizations can use to build and manage applications, but monitoring those environments can often feel like navigating a maze of fragmented data, tools, and processes.

Jenkins is the backbone of many CI/CD pipelines, automating builds, tests, and deployments at scale. However, when handling high-traffic workloads, such as during peak development hours, large-scale deployments, or parallel builds and pipelines, Jenkins can quickly become a resource hog, leading to slow builds, queue backlogs, and even system crashes. Optimizing resource usage is essential to ensure smooth, efficient, and scalable performance.

As the use of artificial intelligence (AI) for software testing and quality assurance (QA) becomes increasingly prevalent, there are ethical considerations that must be addressed to ensure fairness, transparency, and accountability.