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In today’s rapidly evolving technological landscape, observability has become essential for supporting robust, efficient systems. According to Gartner’s report “Preparing for the Future of Observability” from September 2024, OpenTelemetry (OTEL) is emerging as the standard framework for collecting telemetry data across different application pipelines.

When we launched Grafana Drilldown, our queryless experience for quicker, easier insights into your telemetry, we focused first on Prometheus because it was—and is—such a great solution for storing time series data. But as the industry continued to evolve, a different open source project began to emerge as another standard for modern observability: OpenTelemetry.

Tracing in Istio environments should be seamless, but too often, teams run into a frustrating problem—traces are broken. Requests jump between services, but instead of a complete flow, Coralogix displays fragmented spans. Tracing should work out of the box in those environments. Istio’s sidecars capture spans automatically, so why are traces incomplete? The issue is almost always context propagation, and fixing it doesn’t have to mean modifying application code.

Apache Zookeeper is a distributed coordination tool that helps keep large-scale systems in sync. It’s the backbone for managing leader elections, service discovery, and metadata storage in projects like Kafka, Hadoop, and Elasticsearch. Think of it as a highly available traffic controller for distributed apps, ensuring everything runs smoothly.

OpenTelemetry (OTel) is supposed to be the great equalizer in observability, giving teams full control over how they collect, process, and store telemetry data. It was built to be open, flexible, and vendor-neutral. Grafana Alloy claims to be OpenTelemetry-compatible, but scratch beneath the surface, and you’ll see that, based on our investigations, it is not a neutral OpenTelemetry Collector.

OracleDB remains a top choice as a relational database management system (RDBMS), despite its strict licensing requirements. It excels at handling complex SQL queries, massive datasets, and transactional workloads, making it ideal for large Enterprise technology stacks. Its many benefits include robust indexing, partitioning, and in-memory processing to optimize query performance at scale.

Choosing between OpenTelemetry and Datadog isn't just another tool decision. It's about how you'll monitor your systems, troubleshoot issues, and ultimately keep your services running smoothly. If you've been tasked with figuring out which route to take, you're in the right place. Let's get started!

If you’re reading this, chances are you’re already familiar with OpenTelemetry (OTel)—the open-source standard for collecting observability data. But what about OpenTelemetry agents? How do they work, and why do they matter? This guide unpacks everything you need to know about OTel agents—where they fit in your stack, how to set them up, and common pitfalls to watch out for. Let’s get into it.

We all know that Checkly is a ‘secret weapon’ for engineering teams who want to shorten their mean time to detection (MTTD). With Checkly, you can know within minutes if your service is unavailable for users, or acting unexpectedly. In this article we’ll talk about how Checkly traces can help you expand on the benefits of Checkly, adding insights that will help you diagnose root causes, and further reduce your mean time to resolution (MTTR) for outages and other incidents.

Observability is essential for understanding how modern applications perform and behave in production. OpenTelemetry has emerged as the industry standard for collecting, processing, and exporting telemetry data—traces, metrics, and logs—without vendor lock-in. This guide will walk you through OpenTelemetry’s core components, how it works, and why it’s a game-changer for observability.