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
Broad proliferation of cloud infrastructure combined with continued support for remote workers is driving increased complexity and visibility challenges for network operations teams, according to new research conducted by Dimensional Research and sponsored by Broadcom ...
New research from ServiceNow and ThoughtLab reveals that less than 30% of banks feel their transformation efforts are meeting evolving customer digital needs. Additionally, 52% say they must revamp their strategy to counter competition from outside the sector. Adapting to these challenges isn't just about staying competitive — it's about staying in business ...
Leaders in the financial services sector are bullish on AI, with 95% of business and IT decision makers saying that AI is a top C-Suite priority, and 96% of respondents believing it provides their business a competitive advantage, according to Riverbed's Global AI and Digital Experience Survey ...
SLOs have long been a staple for DevOps teams to monitor the health of their applications and infrastructure ... Now, as digital trends have shifted, more and more teams are looking to adapt this model for the mobile environment. This, however, is not without its challenges ...
Modernizing IT infrastructure has become essential for organizations striving to remain competitive. This modernization extends beyond merely upgrading hardware or software; it involves strategically leveraging new technologies like AI and cloud computing to enhance operational efficiency, increase data accessibility, and improve the end-user experience ...
AI sure grew fast in popularity, but are AI apps any good? ... If companies are going to keep integrating AI applications into their tech stack at the rate they are, then they need to be aware of AI's limitations. More importantly, they need to evolve their testing regiment ...
If you were lucky, you found out about the massive CrowdStrike/Microsoft outage last July by reading about it over coffee. Those less fortunate were awoken hours earlier by frantic calls from work ... Whether you were directly affected or not, there's an important lesson: all organizations should be conducting in-depth reviews of testing and change management ...
In MEAN TIME TO INSIGHT Episode 11, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Secure Access Service Edge (SASE) ...
On average, only 48% of digital initiatives enterprise-wide meet or exceed their business outcome targets according to Gartner's annual global survey of CIOs and technology executives ...
Artificial intelligence (AI) is rapidly reshaping industries around the world. From optimizing business processes to unlocking new levels of innovation, AI is a critical driver of success for modern enterprises. As a result, business leaders — from DevOps engineers to CTOs — are under pressure to incorporate AI into their workflows to stay competitive. But the question isn't whether AI should be adopted — it's how ...
