Identifies performance problems in cloud services by aggregating and analyzing billions of data points across tens of thousands of serversNew York, NY, October 9, 2013 – Datadog, the SaaS-based monitoring and data analytics platform that provides a unified view of IT infrastructure, announced today monitoring for auto-scaling Amazon Web Services (AWS), HP Cloud and OpenStack cloud environments.
Modern cloud infrastructures adjust to application demand by auto-scaling. It guarantees IT resource capacity, for applications, but has also created difficulties for legacy monitoring tools which require system administrators to continuously update these systems manually. Additionally, legacy tools have not been built to monitor collections of servers working together as a single unit. The modern cloud environment requires aggregated data analysis and automated monitoring adjustments that instantly reflects the infrastructure that is being monitored.
Datadog intelligently adjusts to new servers in cloud environments with server auto-registration and tagging of performance data. These servers are then dynamically added to alerts and dashboards, to provide an aggregated data stream. Datadog’s automated adjustments ensure that all AWS, HP Cloud and OpenStack servers are instantly covered by monitoring oversight after they are created.
GameChanger, the producer of an Android application that records sports statistics and auto-generates game recaps, must be ready to scale its IT infrastructure several times during weekends. According to GameChanger, CTO, Kiril Savino, “Constantly adjusting our monitoring system to report on our scaling environment was a huge drain on our engineering team with our legacy tools. Even with our most diligent efforts, our manual adjustments weren’t always timely enough to alert us of issues that were occurring in auto-deployed infrastructure. Datadog instantly gives us a real-time view of all our systems and servers. Our infrastructure can grow by hundreds of servers in just a few minutes. Being able to pinpoint the specific servers or systems that are causing problems allows us to quickly resolve issues and keep our customers happy.”
“By monitoring auto-scaling in AWS, HP Cloud and OpenStack, we are removing the lag times, in setting up a server for monitoring, that are present with legacy monitoring solutions,” said Amit Agarwal, VP, Products, Datadog. “Dev and Ops teams can be alerted of issues immediately after a server is auto-deployed, and are able to act on them as critical performance data has also been added to dashboards and reports. This monitoring automation ensures that performance issues in cloud environments can be identified as quickly as possible.”