Handling real-time data at scale? Apache Kafka is likely at the heart of your system. It’s robust, fast, and highly reliable. But as Kafka clusters grow, so does the complexity of maintaining balanced workloads across brokers and partitions. Without a solid strategy for distributing that load, you’re likely to run into bottlenecks, resource exhaustion, and consumer lag—none of which are fun to deal with. So, how do you keep your Kafka setup running efficiently and smoothly?

The ethos – and existential urgency – driving “responsible AI” policies is rooted in themes such as privacy, security, fairness, and transparency. Many headlines blur AI concepts such as machine learning, adaptive algorithms, deep learning, natural language processing, and generative AI (GenAI) technologies that are driving the current AI boom. People are worried about their data being misused, their words misconstrued, and their work misrepresented.

OpenTelemetry, OTel, is an incredible open-source observability framework that helps you collect, process, and export trace data. It's super valuable for engineers who want to understand their systems better. At the heart of this framework lies the OpenTelemetry Collector, a pivotal component that turns raw traces into useful metrics. Let’s explore the importance of the OpenTelemetry Collector and show you how it makes it easier for engineers to make sense of data.

We consistently roll out helpful updates and fun features in Grafana Cloud, our fully managed observability platform powered by the open source Grafana LGTM Stack (Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics). And this month, on the heels of ObservabilityCON 2024 — our flagship observability event — we have no shortage of updates to share.

In this guide, we’ll walk you through the Prometheus rate function. You’ll discover how to analyze changes over time and use that information to enhance your monitoring strategy.

As our software complexity increases, so does our telemetry—and as our telemetry increases, it needs more and more tweaking en route to its final destination. You’ve likely needed to change an attribute, parse a log body, or touch up a metric before it landed in your backend of choice. At Honeycomb, we think the OpenTelemetry Collector is the perfect tool to handle data transformation in flight. The Collector can receive data, process it, and then export it wherever it needs to go.

Hydration failed because the initial ui does not match what was rendered on the server. Don’t you just love hate it when that happens?