How to Maximize Telemetry Data Value With Observability Pipelines

The software landscape has shifted significantly in recent years as companies digitize their operations and adopt cloud and microservices technologies. The complexity of modern software systems has led to an increasing need for real-time visibility into their performance—resulting in an explosion of telemetry data that is stretching IT budgets and outpacing engineering teams’ ability to find value in that data. In fact, data volume is growing at 23% yearly, while IT budgets are only growing at 5%.

Managing all this data at increasing cost is further compounded by unpredictable software failures and ever-evolving security threats. As a result, companies are forced to pick which parts of their software to instrument and monitor. To overcome this challenge, a different approach to managing data is required.

Observability pipelines are emerging as a way to address this problem. They centralize telemetry data (i.e., logs, metrics and traces) from many sources, transform that data to fit downstream needs, and route it to various destinations for analysis. With complete control of the data, companies can sort through large amounts and prioritize what is essential, allowing them to act swiftly to avoid disruptions while reducing costs by only storing the data they need.

Research indicates 62% of organizations currently have an observability practice. This means 38% operate with limited awareness of what’s happening in their software. Even those with an observability practice can only afford to store some data and have to choose which applications to instrument. This leads to blind spots and, ultimately, issues that are time-consuming to fix due to a lack of data.

Managing Limited Resources With Observability Pipelines

Engineers face more demands in today’s fast-paced business environment than ever before. Despite an increase in their responsibilities, the size of engineering teams has yet to grow accordingly. Given this, there has been a rise in full-stack engineers responsible for technologies from the front end to the back end. Short on time and resources, it’s difficult for these engineers to instrument and monitor software.

Observability pipelines help control the amount of telemetry data using various processors such as sampling, throttling, filtering and parsing, and only forward valuable data to the downstream systems. The rest of the data can either be discarded or stored in a low-cost system, such as Amazon S3. This reduces costs and lets engineers focus on analyzing relevant data to identify issues.

Engineers can also use observability pipelines to consolidate data from various tools. Many teams use different platforms or open source tools, like Prometheus or Jaeger. The fragmented data makes it hard to solve issues and understand application health. Observability pipelines combine all this data to make it easier to act on.

It’s also important to remember that real people work on these technical challenges. Engineers face burnout working long hours to keep up with software development demands. Constantly switching between tools and manually moving data adds to the pressure. Moreover, the number of data sources, observability tools, and platforms that need that data are constantly increasing. Embracing open standards such as OpenTelemetry can ease this integration burden. Engineers can swap out tools without extensive testing and integration. The increased control and easy integration enable engineers to focus on delivering business value.

Making Data More Usable

Teams across organizations want access to data but struggle to use it effectively. The difficulty stems from the lack of data usability: Telemetry data is unstructured; varying formats make it hard to use; data preparation is time-consuming and sensitive data in logs may lead to compliance violations.

By most estimates, 80%-90% of data is unstructured, which makes it hard to store and analyze because it doesn’t adhere to traditional data models. Observability pipelines make sense of unstructured data before it reaches its final destination. This is achieved using data processors that shape and transform data to make it more actionable. These processors include advanced parsers that identify and extract relevant information, transforming data into consumable formats that make it easier to work with and analyze.

The advantage of performing these operations within the pipeline is that the same data can be prepared to fit different use cases downstream. For instance, while one team may require data optimized for visualization and trend analysis, another may require complete data for threat hunting. These transformations are managed from a single control point, eliminating the need to maintain separate data streams.

Observability pipelines can also help companies meet data compliance requirements. For example, teams can scrub, mask or redact PII data based on a defined key structure before the data reaches a SIEM or audit platform.

Taking Immediate Steps to Make the Most of Telemetry Data

Organizations can take immediate action to make the most of their telemetry data. First, they need to adopt a visibility-first rather than a cost-first approach. They should assess what software they’ve instrumented. This will identify gaps in telemetry, which can be used to develop a plan to instrument all software. Next, they need to create a strategy for managing their telemetry data, being particularly thoughtful about how and why the data is stored and analyzed.

Once a company has taken these two steps, it can decide which tools to use. One of the great things about OpenTelemetry is that it provides flexibility for future decisions about what tools to use.

As companies invest more resources into building observability practices, they’ll encounter problems processing an extraordinary amount of telemetry data for any meaningful action. Observability pipelines provide a competitive advantage of prioritizing the essential data that will enable companies to make better decisions faster.