Application performance monitoring, or APM, is great for uncovering problems with individual pieces of software. It might help a system engineer sort through log after log to find the root cause of an issue. Or it could enable your IT team to discover customers are complaining about an application because of its slow performance.
There’s no doubt that APM has its place. It’s highly useful in IT environments with more simplistic infrastructure and application design. But as infrastructures and applications have grown more complex, the original concept of APM has become insufficient.
It’s seen as more of a reactive tool that doesn’t provide the level and depth of visibility IT teams need. The question is, what tools and solutions come after APM? This article explores the idea that it’s time for organizations to move beyond application performance monitoring.
Increased Need for Pipeline Visibility
Applications, databases, and data warehouses no longer exist in vacuums. They make up key pieces of data pipelines, with some components in the cloud and others on site. More moving, interconnected elements exist, and they’re not always 100% accessible or visible. Data engineers may find it difficult to manage everything in your data pipeline, relying on vendors for some degree of troubleshooting and support.
Data teams and IT staff can no longer just look at a single app. What appears to be a software performance problem might actually be a virtual server’s failure waiting to happen. There’s a need to not only monitor the entire pipeline but also gain a higher level of visibility on how the pipeline is performing (i.e. where are the blockages?) as well as the quality and reliability of the data flowing through the pipeline.
Teams have to know whether database errors and inconsistencies are causing records to drop off when applications sync. Staff must also anticipate issues before they emerge as fires to chase and put out. Today’s data observability systems provide analytics and insight at the data processing, data, and data pipeline levels, offering a single view for data teams to get ahead of data and data pipeline issues, so they are less reactive and more proactive – being ‘first to know and the first to fix’.
The Integration of Machine Learning and AI
More sophisticated and interdependent systems mean a single point of failure can cause the entire chain to collapse. The result is unexpected outages and downtime, which leads to a host of other problems. Some of them, such as damaged company reputations and loss of customer trust, last long after the outage is over. Those lingering issues often show up as a drop in revenue.
Unplanned downtime is becoming more expensive for organizations. A 2021 survey reveals that 60% of respondents reported losses of more than $100,000 because of outages. Of that 60%, 15% said their financial losses exceeded $1 million. While preventing downtime is something most businesses strive to do, complex systems make it more challenging. There’s a real need to enhance human knowledge and capabilities with machine learning and artificial intelligence.
Some APM tools might use machine learning and AI to make recommendations and avoid performance problems. However, those capabilities may be limited to a single application or cloud-based solution. Observability solutions integrate machine learning and artificial intelligence to synthesize signals across a company’s infrastructure, applications, and data layers.
AI corrects the simple errors that can lead to downtime. Meanwhile, teams gain a complete understanding of an entire system’s moving parts and potential performance issues. They can better manage the system’s overall health by quickly identifying where things might go wrong. Employees become more proactive about applying fixes before complications requiring human intervention get out of hand.
It’s a well-known fact that more applications and services are in the cloud. On-premises solutions may still exist, but they’re becoming rarer due to scaling and convenience needs. You’re more likely to find hybrid infrastructures with a mix of cloud-based platforms that must somehow all work together. Traditional application performance monitoring solutions may be limited in their ability to provide cross-platform support.
That is, APM may well be able to detect what’s going on within one type of cloud-based solution. However, it can’t discover and predict what might happen when two or more different platforms have to act in sync. Unique problems may arise when a cloud-based CRM has to exchange information with a separate inventory application. Both apps also pull that information from various virtual database servers that run on a distinct platform.
IT teams require performance monitoring and management solutions that can support a full tech stack. While APIs or extensions are a way to bridge compatibility gaps between applications and platforms, they can be clunky. Someone may have to develop a custom API or tweak existing code to make it work. Extensions also introduce another potential failure point, one a performance monitoring and management solution shouldn’t have.
Observability solutions can provide cross-platform support across the entire tech stack and infrastructure. There’s more visibility into how various apps and platforms are exchanging and moving data. Performance issues specific to cross-platform interactions become apparent to staff before customers notice. And it’s easier to see where the problem lies, so teams don’t spend as much time isolating and backtracking.
Moving Beyond APM
Application performance monitoring is built for teams that handle simple infrastructures and have time to play detective. But most modern organizations now deal with intricate, hybrid IT systems and information pipelines. APM doesn’t always offer the full visibility, AI functionality, and cross-platform support tech teams need.
The next generation of monitoring solutions provides efficient and complete insights into how well complex systems are working. Data observability tools oversee larger-scale data systems, helping IT staff predict and fix emerging problems. The result is a move away from reactive methods to proactive performance management and a seamless customer experience.