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Picture this, your observability tool already nails the basics like request rates, latency and memory usage, but you need more insight. Think user churn rates, engagement spikes, or even how many carts get abandoned mid-checkout. That’s where OpenTelemetry steps in, providing a way to track those critical custom metrics with ease.

OpenTelemetry backends provide storage, analysis, and visualization for telemetry data (traces, metrics, logs). This guide lists available OpenTelemetry-compliant backend options, categorized by use case: APM platforms, storage backends, visualization tools, and distributed tracing systems. For detailed comparison, see OpenTelemetry Backend Comparison.

Over the past year, the Governance Committee ran user interviews and surveys with organizations deploying OpenTelemetry at scale. A few patterns came up consistently: Stability levels aren't always obvious. When you install an OTel distribution, some components might be experimental or alpha without clear markers. This makes it harder to evaluate what's production-ready. Instrumentation libraries sometimes wait on semantic conventions.

Start with distributed tracing to find which service is slow, then use continuous profiling to see why the code is slow, and finally apply high-cardinality analysis to identify which users or conditions trigger the problem. It's 2 AM. Your phone buzzes. Users are reporting timeouts. The metrics dashboard shows p99 latency spiking from 200ms to 4 seconds, but everything looks normal—CPU at 60%, memory stable, no error spikes. A quick pod restart helps briefly, then latency climbs right back up.

When troubleshooting complex software, it’s important to be able to gain insight via its telemetry quickly and precisely. No one wants to waste time switching between tools or worrying about how to interact with different types of data. At Honeycomb, all your data is available in one place, accessible via our fast query engine. But what does that look like in practice?

Some log sources bundle multiple events into a single record before shipping them. This is common with VPC flow logs, CloudWatch exports, and certain Windows endpoint collectors. While this batching approach is efficient for transport, it creates challenges when you need to filter, search, or correlate individual events. When a log record contains an array of 47 events, your analytics tool sees one entry instead of 47 distinct records.

So, you've embraced OpenTelemetry, and it's been great. Pat, Pat. That single, vendor-neutral pipeline for your traces, metrics, and logs felt like the future. But now, the future is getting bigger. That simple OTel Collector configuration that worked perfectly for a few services is starting to show its limits as you scale. The data volume is climbing, reliability is becoming a concern, and you're wondering if that single collector instance is now a bottleneck waiting to happen.

You can instrument Jenkins with OpenTelemetry using the official plugin and an OpenTelemetry Collector, then send the data to a backend like Last9 to understand where pipeline latency and failures actually originate. Jenkins provides job status and console logs, but it doesn't show how time is distributed across stages, agents, plugins, and external systems. OpenTelemetry fills that gap by emitting traces, metrics, and logs in a standard format that any OTLP-compatible backend can process.

Django is a popular open-source "batteries-included" Python web framework that enables rapid development while taking out much of the hassle from routine web development. By providing pre-built components like ORM integrations, authentication/authorization systems and more, it enables developers to focus on business logic and iterate fast. As such, developers and organizations worldwide use Django to build web apps of varying complexities.