Operations | Monitoring | ITSM | DevOps | Cloud

I had the opportunity to present with Michael Fratto, Senior Research Analyst at S&P Global Market Intelligence, at a virtual event hosted by Redmond. We discussed how telemetry pipelines are critical in controlling telemetry data (logs, metrics, events, and traces). Mike shared excellent insights from his recent research survey that discussed the proliferation of observability tools in enterprises and the challenges organizations face in managing those tools. ‍

When it comes to observability, we believe open source will win in the end. It’s a sentiment shared by a wide spectrum of users, whether they work in auto manufacturing, banking, or shipping. The U.S. federal government is yet another industry to prove this, showing that even those operating under the strictest security and compliance requirements see OSS as their preferred approach.

Savvy organizations are embarking on a journey toward Autonomic IT to better manage their IT environments, particularly as hybrid configurations grow in popularity and are accompanied by greater complexity. Driven by AI and automation, Autonomic IT can help organizations optimize resource allocation, improve the effectiveness of maintenance efforts, avoid downtime, and keep end users happy.

When hosting your website or application, you have two main options: dedicated servers and cloud servers. This article will explain the differences between these two hosting solutions, helping you understand which one might be the best fit for your needs. We'll look at the features, benefits, and use cases of each option, allowing you to make an informed decision for your hosting environment.

Prometheus is the go-to observability tool for countless developers and organizations, and for good reason. The popular open source tool doesn’t require any up-front costs or result in vendor lock-in. Prometheus’ short on-ramp makes the technology well-suited for organizations looking to jump-start their cloud monitoring journey.

Data is central to any business: it powers mission-critical applications, informs business decisions, and supports the growing adoption of AI/ML models. As a result, data volumes are only increasing, and teams rely on engines like Apache Spark and managed platforms like Databricks or Amazon EMR to process this data at scale.

When you face issues like reduced throughput or latency spikes in your production applications, determining the cause isn’t always straightforward. These kinds of performance problems might not arise for simple reasons such as under-provisioned resources; often, the root of the problem lies deep within an application’s runtime execution.

For network operations teams, having remote employees out of sight doesn’t mean they can be out of mind. This is particularly true for remote employees who directly support and interact with customers. In many industries today, organizations may have a significant percentage of employees working in some type of remote fashion, including those who deliver customer-facing services.