Operations | Monitoring | ITSM | DevOps | Cloud

Learn how SIS saved 75% in operations cost with Applications Manager.

Continuous monitoring is the backbone of staying ahead in your business, maintaining a constant watch on your company’s activities. It adapts to the demanding needs of modern times, whether for compliance checks, continuous control, and infrastructure monitoring or defending against cyber threats. However, before the widespread adoption of continuous monitoring, companies relied on periodic audits, manual assessments, and sporadic checks to monitor their systems.

With a slew of options at hand, choosing the best observability tools is challenging. Not to mention the strenuous task of researching the web, reading reviews, and scheduling demos to find the best option.

When I first had the idea for this post, I wanted to provide a collection of actionable ways to handle errors caused by API rate limits in your applications. But as it turns out, it’s not that straightforward (is it ever?). API rate limiting is a minefield, and at the time of writing, there are no published standards in terms of how to build and consume APIs that implement rate limiting.

If you’re using Grafana and Kubernetes, we’ve got exciting news — Grafana Labs will be maintaining and managing the Grafana Operator, the open source Kubernetes operator that helps you manage your Grafana instances within and outside of Kubernetes. This significant move not only elevates the Grafana Operator to an officially supported tool but also cements its place as a staple for managing Grafana as code, especially for users keen on adopting GitOps principles.

When monitoring applications with streaming data pipelines, there are additional complexities to consider that are not present in traditional batch-processing systems. Whether you’re using streaming data pipelines to power a digital trading platform, capture sensor data from an IoT device, or recommend news articles to users, it can be challenging to identify the root cause of delays when you’re dealing with distributed systems, real-time data, and the dynamic nature of events.