Modern applications are composed of hundreds or thousands of services that are developed and tested independently by teams that may not communicate with each other. Software deployments may be automated, occurring several times a day across production environments.
Enterprises implementing an end-to-end observability pipeline will lower infrastructure costs by 30% and resolve issues four times faster than competitors, improving customer satisfaction and increasing spend by 15%.
Increasing the complexity, each service may also have its own database and data model, which is also independently managed. Add in short-lived containers and dynamic scaling, and it’s easy to understand why the only time companies can test their applications is when they’re deployed in front of customers. We’ve made customers unwitting acceptance testers. Monitoring solutions haven’t kept pace with the realities of today’s application environments. This is why we believe enterprises implementing an end-to-end observability pipeline will lower infrastructure costs by 30% and resolve issues four times faster than competitors, improving customer satisfaction and increasing spend by 15%.
Traditionally, infrastructure and operations teams would deploy monitoring for visibility into their environments. The challenge is monitoring hasn’t kept pace with modern complexity for three reasons:
- Exorbitant costs force teams to compromise on what they’re monitoring. Forced into decisions about which logs, metrics, and traces to keep staying within budget, teams simply can’t store everything they need to observe their environment.
- As we’ve pointed out above, pre-built dashboards and alerts don’t reflect today’s infrastructure reality. Systems scale dynamically, and DevOps teams may deploy code across thousands of containers dozens of times each day. The static views offered by traditional monitoring systems don’t reflect this reality.
- Monitoring is a point solution, targeting a single application or service. A failure in one service cascades to others, and unraveling those errors is well beyond the scope of monitoring applications.
DevOps and ITOps teams must evolve past monitoring into observability. Observability is the characteristic of software and systems allowing them to be “seen,” and to answer questions about their behavior. Unlike monitoring, which relies on static views of static resources, observable systems invite investigation by unlocking data from siloed log analytics applications.
Implementing observability requires a way to collect and integrate data from complex systems, which is where the observability pipeline comes in. An observability pipeline decouples the sources of data from their destinations. This decoupling allows teams to enrich, redact, reduce and route data to the right place for the right audience. The observability pipeline gets you past what data to send and lets you focus on what you want to do with it.
By providing context around logs and metrics, an observability pipeline makes debugging faster by allowing you to ask “what if” questions of the environment, rather than the pre-calculated views prevalent in monitoring solutions. Faster debugging and root cause analysis means fewer customers experiencing errors in production, which drives up sales.
Another benefit of an observability pipeline is rationalizing infrastructure costs. Often, the team deploying infrastructure isn’t the team paying for it, resulting in over-provisioned infrastructure. Collecting performance data, even for transient infrastructure like containers, gives ITOps and DevOps teams visibility into how many resources are actually being consumed and where optimizations are possible.