What Is An Observability Pipeline, Anyway?

When working with software monitoring and observability, there's a bit of a paradox. Here's how it goes: To understand what's happening in complex environments, you usually need to gather a lot of logs, metrics, traces, and other observability data. But, if you collect too much data without an efficient way of processing and managing it, the information becomes a hindrance more than a help.

it's a problem for modern businesses committed to optimizing the performance and reliability of digital systems.

Fortunately, there's a solution: observability pipelines. By routing and processing data efficiently and at scale, observability pipelines play a critical role in ensuring that having large volumes of observability data at your disposal doesn't ironically undercut visibility into software environments.

What is an observability pipeline?

An observability pipeline is a type of tool that moves observability data from its sources to its destinations. It can also perform tasks like data transformation, enrichment and aggregation.

These are important capabilities in the context of modern application performance management (APM), as well as monitoring and observability for two main reasons.

First, the volume of telemetry data – meaning logs, metrics, traces and other information that provides insight into the health and performance of software – that teams have to contend with has exploded over the past decade, due largely to the shift toward microservices and distributed architectures. A modern app could include dozens of individual microservices and containers, each producing its own logs and metrics – not to mention tracing data that tracks requests across multiple services. As a result, there is much more observability data, and more discrete data sources.

As Forbes puts it, "there's exponentially more data coming from the proliferation of microservices and containers along with additional complexity and dependencies."

The second key challenge is that observability data and workflows have become more complex in the age of microservices. To leverage modern observability data effectively, you need not just to collect it, but also to correlate data from different sources to gain context on performance issues and pinpoint root causes. This requires the ability to route and merge data from multiple locations.

Because observability pipelines help solve these challenges, they are now essential to business and used to manage application performance. Gartner predicts that 40 percent of log telemetry data will be processed through observability pipelines by 2026, a 400 percent increase compared to 2022.

Why use an observability pipeline?

We just explained at a high level why observability pipelines are important in the context of modern APM and observability. But to illustrate the value further, let's take a look at more specific benefits of observability pipelines:

• Better security: Observability data could contain sensitive information, such as personal names stored in log files. Observability pipelines help keep this data safe by managing it in a centralized way. Plus, through features like data anonymization, pipelines can remove sensitive information to reduce security risks further.
• Faster incident response: By moving data as quickly and efficiently as possible, as well as by optimizing the data for analysis while it's in transit, observability pipelines help teams make sense of data quickly. This translates to faster incident response because the root causes of issues are easier to identify.
• Simplified data collection: With an observability pipeline, you can easily create automation that moves all relevant data from its places of origin to its destination – which is much simpler and faster than collecting and exporting data manually.
• Full data control: Instead of being limited by your data architecture and the features of your data analytics tools, observability pipelines allow you to remain in control of where your data comes from and what happens to it.
• Vendor neutrality: When you use observability pipelines based on standards like OpenTelemetry, which enables a vendor-neutral approach to telemetry data collection and management, you avoid becoming locked into certain observability tools or vendor ecosystems.
• Reduced storage costs: By making it possible to perform processes such as data minimization and compression before observability data even arrives at its destination, pipelines can help reduce overall data volumes – and, by extension, data storage costs.

Observability pipelines help teams use observability data more efficiently and effectively, while also lowering security risks and providing observability cost advantages.

Who's using observability pipelines?

Observability pipelines can benefit virtually any organization that must collect, process and manage observability data on any significant scale. But they're particularly valuable for businesses that fall into at least one of the following categories:

• Those with tight compliance or security requirements, which pipelines help to address by reducing the security and privacy risks of observability data.
• Organizations that have adopted cloud-native computing strategies and architectures, which tend to increase the volume and complexity of observability data.
• Businesses seeking to embrace GitOps, which requires a standardized, systematic approach to data collection and management.
• Companies committed to open standards and open source, which are at the core of observability pipelines that manage data based on standards like OpenTelemetry.

It's worth noting, too, that observability pipelines can benefit multiple types of teams and roles. IT engineers responsible for collecting and managing observability data are one obvious beneficiary of this type of tool. However, pipelines can also be useful for security analysts, who also need to collect vast quantities of data and route it to various SIEMs and other tools. Likewise, data engineers can benefit from observability pipelines as a way of streamlining the collection and processing of the data they manage from disparate sources.

Getting started with observability pipelines

The exact process for implementing an observability pipeline varies depending on which types of data you're collecting, what you're doing with it and which types of tools you use to work with it. In general, however, setting up an observability pipeline boils down to the following four basic steps:

1. Identify data sources and destinations: These are the data resources that will serve as the starting and ending points of your pipeline.
2. Identify data transformations: Determine which types of processes – such as data minimization, integration or deduplication – you need to perform within the pipeline.
3. Choose an observability pipeline tool: Find a solution that can pull data from your sources, process it as you require and deliver it to the destinations. (In case you haven't noticed, we're partial to open, standards-based pipeline tools like BindPlane.)
4. Deploy your pipeline: Implement the pipeline using whichever architecture – such as on-prem, cloud-based or a hybrid approach that combines the former and the latter – makes most sense based on the infrastructure you are working with.

An unironic approach to observability

Observability can be a real challenge when you struggle to move observability data where it needs to move, in the most efficient way possible. But with an observability pipeline at your disposal, having too much data to work with, or the inability to process data efficiently, no longer gets in the way of achieving visibility into software environments.

To learn more about how observability pipelines work and how to implement one, learn about BindPlane, the vendor-agnostic observability pipeline solution that features over 200 integrations and can run on-prem, in the cloud or as part of a hybrid architecture.