Telemetry Pipelines: The Critical Missing Link in Modern Application Monitoring and Performance Management

When it comes to telemetry data – meaning the logs, metrics, traces, and other information engineers use to monitor applications, manage performance, and troubleshoot outages– more is usually better. But there's a big caveat: If you fail to manage telemetry data effectively, the data can quickly create more problems than it solves, leading to problems like higher storage costs, difficulty finding the right information when responding to failures, and the risk of exposing sensitive information to unauthorized access.

That's why telemetry pipelines have become an essential ingredient in modern application observability and performance management strategies and a critical resource when deploying tools like Security Information and Event Management (SIEM) platforms. Telemetry pipelines allow businesses to collect, process, route, and store telemetry data efficiently and at scale.

That, at least, is a high-level overview of what telemetry pipelines do and their importance from both a technical and a business perspective. For a deeper dive, keep reading as we draw on our collective experience in designing, implementing, and managing telemetry pipelines to explain why they're so valuable and what to look for when building a telemetry pipeline tailored to your organization's needs.

What are telemetry pipelines, and why do they matter?

Telemetry pipelines are a type of solution that collects, processes, and routes telemetry data – which means logs, metrics, traces, and any other kind of information that provides visibility into application performance.

Telemetry data has been important for decades, given the central role it plays in allowing organizations to monitor application performance, detect problems, and troubleshoot them in ways that minimize the impact on users. But until relatively recently, few organizations had deliberate strategies in place for managing that data. Instead, they relied on ad hoc approaches for collecting data from the places where it originates and moving it to the tools they relied on to analyze it.

That approach worked well enough in most cases when the volume of logs, metrics, and traces that a business managed was relatively low. But in today's world of distributed software architectures, the amount of telemetry data that the typical organization must contend with has exploded. Instead of having to collect just one set of logs files and metrics for each application, as you would have done in the age of monolithic apps, it's common today for a single application to consist of a dozen or more microservices, each of which generates its own logs and metrics. Couple that with the fact that today's apps often run on distributed infrastructure that may consist of hundreds or thousands of individual servers, and it's easy to see why there is so much more telemetry data to manage today.

To handle that volume efficiently, most organizations need more than an ad hoc solution for collecting, processing, and routing data. They require a purpose-built solution that systematically pulls data from the various sources where it originates, normalizes and transforms it as necessary, and delivers it to the places where it's analyzed or stored. They need, in other words, a telemetry pipeline.

The benefits of telemetry pipelines

By bringing consistency and order to telemetry data management, telemetry pipelines produce a range of business benefits. The most important include:

Telemetry pipeline features: Basic vs. advanced

To deliver the benefits we just discussed, every telemetry pipeline solution worthy of the name should provide a core set of features, including:

These, however, are the bare minimum features that telemetry pipelines must support. To achieve optimal pipeline efficiency and flexibility, organizations should seek out several key additional capabilities.

The OpenTelemetry Project

OpenTelemetry (or OTel for short) is an open framework (governed by the CNCF) that offers a standardized approach to collecting, processing, and transmitting telemetry data.

OTel has become a virtually universal standard, with its tools experiencing more than 30 million downloads each month, which means that as long as your pipeline supports OTel, you'll be able to use the pipeline to connect almost any data source to any data analytics or management tool.

The Value of an Open Framework

Supporting OTel ensures that a telemetry pipeline will work with any OTel-compatible data source or tool. However, to maximize the flexibility of your pipeline and minimize the risk of vendor lock-in, you can take openness a step further by building a pipeline that includes minimal proprietary components.

When your pipeline software is open – meaning it's based on transparently standardized components – you don't have to worry about becoming locked into your pipeline software itself or beholden to a particular vendor to support the tool you depend on to work with telemetry data.

Processing previews

Processing previews allow you to predict how any data processing routines that you've configured within a pipeline will change your data. This is important because you don't want to discover after the fact that you processed data in a way that made it unusable or that introduced errors or formatting problems. With previews, you can take a more proactive approach to data management and get ahead of data issues in real time.

Data summaries

In addition to viewing data as it flows through individual pipelines, the ability to summarize all data within your pipeline helps you track how much data you're processing and what you're doing with it. In turn, these insights allow you to identify long-term trends involving your telemetry data. They can also help to track telemetry costs and find opportunities to streamline data workflows.

How to build a telemetry pipeline

Once you've decided what you need your telemetry pipeline to do and found software that does it, you need to implement the pipeline itself – which can be a challenging task, given the complexity of modern pipelines and telemetry data.

We won't walk through every step of the implementation process here since the specifics vary depending on which telemetry pipeline software you're using. What we would like to mention, however, are implementation and operational challenges that teams sometimes overlook, such as:

Conclusion: Transforming application performance management and SIEM with telemetry pipelines

The bottom line: For many businesses, traditional approaches to managing telemetry data no longer suffice. They're too slow, costly, and challenging to scale.

The solution is to take a deliberate, consistent approach to telemetry data processing and delivery by building a pipeline to ensure that every data source reaches its intended destination and is ready to support its intended use case. When you do this, you've set your organization up for long-term success in the era of increasingly large and complex telemetry data sets.