Collecting data is only part of the story. With systems becoming more dynamic, distributed, and complex, teams are under pressure to make sense of vast amounts of telemetry data. When done right, observability pipelines transform noisy telemetry data into clean, contextual insights that you can act on to improve reliability, performance, and incident response.

An observability pipeline enables developers, SREs, and engineers to route, enrich, transform, and analyze telemetry data in real time. In this post, we’ll talk about using observability pipelines, how they work to give teams control, scalability, and cost-efficiency in their observability stack, and we’ll conclude with how tools like the Honeycomb Telemetry Pipeline help make telemetry smarter and more manageable.

What is an observability pipeline?

An observability pipeline is a system for managing telemetry data, including logs, metrics, traces, and events. Think of an observability pipeline as a programmable assembly line that prepares your telemetry before it reaches your observability tools.

Much like how an assembly line sorts, refines, and packages parts before they become a finished product, an observability pipeline collects raw telemetry from across your infrastructure. It transforms and enriches it, filtering out the noise and routing it to the right destination. It ensures your data is clean, contextual, and cost-effective before it hits your dashboards.

Observability itself is the ability to answer questions about what’s happening inside your systems based on the signals it emits. A well-designed telemetry pipeline enhances observability by ensuring that the telemetry data you rely on is usable, high-quality, and tailored to your specific needs.

Why you need an observability pipeline

Implementing an observability pipeline isn’t just a “nice to have,” it is table stakes for operating software systems. Here’s why:

• Control over costs and data: If you have an observability pipeline, you control what telemetry data you collect, enrich, store, or drop. This allows you to reduce volume, filter noise, and avoid overpaying for data that provides little value. You send only the right data to your observability backend, whether that’s Honeycomb, another APM, a log aggregator, or a data lake. If you need to send data to several backends, you can do so without duplicating efforts.
• Scalability: As your telemetry volumes grow across cloud-native, hybrid, or multi-region environments, a pipeline allows you to scale observability infrastructure without re-instrumenting services.
• Centralized telemetry control: With a centralized place to define policies for data transformation, enrichment, and redaction, you can ensure that your telemetry is clean, consistent, and high-quality across teams and services.
• Compliance and governance: Pipelines make it easier to enforce consistent standards for retention, redaction, and routing so that your observability meets both internal policies and external regulatory requirements.

How does an observability pipeline work?

A typical observability pipeline has four key stages.

1. Data ingestion and buffering: The pipeline begins by collecting telemetry from various sources, including instrumented applications, containers, cloud services, and infrastructure agents. This includes data like traces, logs, and metrics collected via OpenTelemetry, FluentBit, and other agents.
2. Data transformation: This stage applies enrichments (e.g., tags, attributes), formatting changes (e.g., JSON to OTLP), redactions, or schema normalization. This improves data quality and ensures consistency across destinations.
3. Sampling and cost control: You can filter out redundant or unhelpful data using trace sampling or log-level controls. This stage also includes sampling logic to ensure that high-value signals are preserved without exceeding your budgets.
4. Data routing and export: The processed data is then sent to observability tools, such as Honeycomb. You can also send the same data to multiple destinations depending on your use case, including long-term storage in services like Amazon S3 for cost-effective archiving, compliance, or future reprocessing needs.

Implementing an observability pipeline

Best practices

Here is our list of best practices for implementing observability:

• Choose the right tools: look for platforms that support high-cardinality data and multi-signal telemetry.
• Use OpenTelemetry: OpenTelemetry is the de facto standard for observability and supports vendor-agnostic instrumentation.
• Separate signal types: Use different treatment rules for traces, logs, and metrics—don’t treat them all the same way!
• Implement policy as code: Define enrichment and filtering rules in code to ensure consistency and version control.
• Use progressive sampling: Adjust sampling dynamically based on latency, errors, or high-volume endpoints.
• Retain what matters: Use smart trace retention or log filtering to focus on high-value transactions.

Common challenges and how to overcome them

Here is a list of common challenges that engineers face with their observability pipelines, along with suggestions on how to overcome them.

• Data overload: It can be easy to overwhelm your backend with too much data, especially logs. Utilize dynamic sampling and filter out low-value logs before they are stored. Focus on collecting logs that help you troubleshoot issues or optimize system performance.
• Legacy architecture compatibility: Many legacy systems (e.g., monoliths, on-prem apps, or older VMs) weren’t built with observability in mind, let alone standards like OpenTelemetry. In some cases, the necessary language SDKs or auto-instrumentation hooks don’t exist, or adding them would require risky changes to production systems. To address this, choose telemetry tools that support broad ingestion formats and use shims or exporters to convert raw legacy data into formats like OTLP, JSON, or protobuf for compatibility with modern backends.
• Security and compliance: Telemetry data often contains sensitive information. Define an observability pipeline that includes in-flight redaction, secure routing, and consistent data governance policies. This helps meet both internal compliance requirements and external regulations without exposing sensitive user data.
• Tool fragmentation: Many organizations rely on multiple observability tools for logging, metrics, and tracing, each tool with its own data model, query language, and overhead. We recommend choosing an observability platform that can ingest all signal types, letting you move to consolidate your workflow.

Integrating Honeycomb into your observability pipeline

The Honeycomb Telemetry Pipeline is designed to provide teams with full control over their observability pipeline, from ingestion to transformation to export all within the Honeycomb UI.