
Datadog announced the launch of Error Tracking, a new product that automatically gathers application errors in realtime and intelligently aggregates them into actionable issues for engineering teams.
A single issue within an application can cause hundreds to thousands of errors due to events generated for every user session, code version, service, error type, SDK or other environmental detail affected by the issue. This overwhelming error volume can require several hours of manual analysis to identify underlying problems before an engineering team can triage the most critical issues, investigate and then resolve these errors. Datadog Error Tracking streamlines the troubleshooting effort by intelligently grouping individual application errors which are interrelated into a small set of issues. Engineering teams can then work off of this short list to determine the root cause and rapidly resolve the problem.
“In modern applications, the amount of errors can increase rapidly as we serve more users, make frontend code logic more complex with Single Page Applications, and increasingly rely on microservices and elastic infrastructure. Application engineers need a solution to prioritize issues in fast moving situations that impact customer experience and revenues,” said Renaud Boutet, Vice President, Product at Datadog. “Datadog Error Tracking automatically processes the data that is already available within our platform to provide engineers the insight that they need to resolve issues quickly and efficiently.”
Datadog Error Tracking will enable teams to clearly identify similar errors and view contextual data needed for resolution in a single platform. Key features include:
- Automatic Error Extraction: Errors are automatically extracted for existing users of Datadog RUM without the need to install a new SDK or write new code.
- Errors view: A simplified search function and visualization tool using tags and facets to group errors into related issues, as well as when an issue was first and last seen, to help teams prioritize their troubleshooting.
- Unminified stack traces: Access to unminified source-code, so teams can pinpoint the cause of the error from the stack trace.
- Seamless developer experience: Functions within existing CI/CD workflows using the Datadog CLI. This enables application developers to track their releases and link the associated source code with error-events generated by each release.
- Correlation across RUM sessions: Valuable data including session ID, view ID, URL, browser, location, OS, are automatically correlated with the error so teams can triage and resolve frontend application errors.
Datadog Error Tracking is now generally available within the Datadog platform and included for all RUM customers at no additional charge.
The Latest
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...