Datadog announced Deployment Tracking, a new feature for Datadog APM.
This feature enables engineering teams to identify when new code deployments are the root cause of performance issues.
With the rise in adoption of continuous integration and continuous delivery (CI/CD) practices, DevOps teams are increasingly using modern code deployment strategies such as Canary, Blue-Green, and Shadow deployments to test new versions with limited impact to end-users. While this agility comes with an increased risk of failure, traditional APM vendors offer hard-to-setup solutions that do not monitor these deployments in real time or the impact they have on specific endpoints. To prevent such risky visibility gaps, Datadog Deployment Tracking visualizes key performance metrics such as requests per second and error rate, identifying new error types for specific endpoints during every code deployment. This allows developers to detect and contain the impact of changes as they happen, as well as respond to incidents more quickly.
“Our customers build and ship applications with multiple types of deployment practices, increasing efficiency but often with the risk of impacting overall performance or introducing errors,” said Renaud Boutet, Vice President of Product, Datadog. “Deployment Tracking will visualize and compare key data related to various version deployments, helping our customers efficiently prevent outages related to bad code deploys, so they can rapidly iterate their applications in a more organized way.”
Datadog Deployment Tracking is available for all languages supported by Datadog APM and works in both containerized and non-containerized environments. Deployment Tracking extends existing APM capabilities by using a unified version tag to analyze recent deployments. Functionalities include:
- Easily comparing performance between versions: quickly identifying bad deployments by comparing high-level performance and error data between releases.
- Ensuring efficiency of targeted fixes: viewing granular performance data down to a single endpoint to ensure a hotfix is actually resolving the issue.
- Starting troubleshooting in one-click: leveraging seamless correlation between version performance metrics and the associated hosts, traces, logs, code profiles, and processes to detect the root-cause faster.
Deployment Tracking is now available for all Datadog APM customers.
The Latest
In the heat of the holiday online shopping rush, retailers face persistent challenges such as increased web traffic or cyber threats that can lead to high-impact outages. With profit margins under high pressure, retailers are prioritizing strategic investments to help drive business value while improving the customer experience ...
In a fast-paced industry where customer service is a priority, the opportunity to use AI to personalize products and services, revolutionize delivery channels, and effectively manage peaks in demand such as Black Friday and Cyber Monday are vast. By leveraging AI to streamline demand forecasting, optimize inventory, personalize customer interactions, and adjust pricing, retailers can have a better handle on these stress points, and deliver a seamless digital experience ...
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) ...
