Operations | Monitoring | ITSM | DevOps | Cloud

Have you ever found yourself wishing for a magical solution to handle the unpredictable ebb and flow of user traffic on your cloud-hosted platforms? Organizations today face the ever-present challenge of effectively managing fluctuating levels of traffic on their platforms. Enter application autoscaling, a concept in modern resource management that allows organizations to seamlessly adjust their resources in response to spikes or lulls in user activity. But what exactly is autoscaling?

In part 1 of this two-part blog about utilizing Failover Clusters in your network to improve performance and availability, we'll uncover how they work, why they are popular for large-scale organizations, and discuss several of the most common issues with them. In part 2, we'll discover the best troubleshooting strategies to address Failover Cluster performance issues, and we'll review a helpful checklist that streamlines the process for fixing these issues.

If you’re working with AI, you’re working with data. From numerical data to videos or images, regardless of your industry or use case, every AI project depends on data in some form. The question is: how can you efficiently store that data and use it when building your models? One answer is PostgreSQL, a proven and well-loved database that, thanks to recent developments, has become a strong choice to support AI.

Within Kubernetes, the Domain Name System (DNS) plays a pivotal role in facilitating service discovery, allowing pods to effectively locate and interact with other services within the cluster. For organizations transitioning their workloads to Kubernetes, establishing connectivity with services external to the cluster is equally important.

If your organization asked you to report on the reliability improvements you’ve made over the past 90 days, would you be able to pull up a report? If you’re like many engineers, this question might make you anxious. Reliability is a difficult metric to quantify in a meaningful way, let alone measure.

Unraveling the complexities of handling data to match your tech needs.

It’s 3am. You wake up to a blaring alarm, the sound burned into your soul from countless sleepless nights. You reach for your phone, ‘press 4 to acknowledge’ and bleary eyed, you open your laptop, grab a coffee and get to work. The next hour is a whirlwind—bringing services back online, keeping colleagues in the loop, maintaining a list of action items, updating a status page that will be seen by millions of customers. Potentially for the fifth time this month.

Cribl’s suite of products excel at collecting and organizing your IT and security event data. Did you know it can also help with IoT data collection and analysis? If you can get the text of the data into Cribl, in most cases, we can process it, transform it, and send it to where you want it to go. A few years ago, I bought a weather station. I immediately hooked up some home automation gear to show me the temperature, humidity, and air quality. But the geek in me wants more.

Every day the world is becoming increasingly powered by artificial intelligence. In fact, you’d struggle to find tech companies that have not announced AI integrations into their tech stack in one way or another. Cynics might say this is a passing phase, but the reason AI is so popular is that it’s a versatile set of capabilities that can help solve a lot of problems.

Whether your data is on the moon or in your basement, Grafana has got you covered. As the go-to platform for monitoring and observability, Grafana has been your trusty sidekick for data visualization for years, in part because we’re always looking for new ways to support our users, no matter where they keep their data. That’s why we’re excited to tell you about our latest supported data source — SurrealDB.