Datadog announced the extension of Network Performance Monitoring (NPM) to Windows.
Datadog NPM now monitors the performance of network communications between applications running on Windows Server and Linux, providing seamless network visibility across cloud environments, on-premises data centers, and operating systems.
Datadog Network Performance Monitoring translates distributed traffic of complex network architectures into meaningful application dependencies, so that customers can spot latencies or inefficiencies that negatively contribute to application performance, infrastructure load, and network-related costs. With this enhanced functionality, organizations can monitor their entire network across varying operating systems, providing complete visibility.
“At Datadog, we are pushing the boundaries of what it means to holistically monitor Windows Server workloads by analyzing every aspect of their health, from infrastructure, application, network through to security,” said Ilan Rabinovitch, VP, Product and Community, Datadog. “With this latest development, we’re excited to create new opportunities for all Windows Server customers to isolate the root cause of their app issues, whether they be upstream code errors, heavy network traffic, or regional outages.”
“Assessing the performance of crucial application traffic in our Windows environment used to be very difficult,” said Alex Kanevsky, Lead Architect at Generali Global Assistance. “With Datadog Network Performance Monitoring, we can quickly determine if our network is at fault for slow traffic or low connectivity before our applications are affected, so that insuring travel is a seamless experience for our customers.”
“At AWS, we are focused on ensuring that Windows applications can achieve digital transformation goals customers have set,” said Fred Wurden, General Manager, Amazon EC2 Enterprise & Benchmarking, AWS. “Now with Datadog Network Performance Monitoring, we can empower our shared customers to manage their complex service dependencies, improving the digital experience for all.”
Datadog NPM enables monitoring of distributed traffic across on-premises and cloud environments, so organizations are able to:
- Spot cost and performance bottlenecks: identify unexpected or costly communication between services and cloud regions to quickly detect where network connectivity and latency issues are concentrated.
- Isolate the root cause: determine when application and infrastructure issues are the root cause of faulty dependencies, misconfigured connection pooling, or cloud provider outages.
- Visibility for every engineer: visualize connection data at the application layer, so it can be analyzed and understood by network, application, and Reliability Engineers alike.
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 ...
