Datadog joins Google Cloud Platform Partner Ecosystem to Monitor Infrastructure and Applications at Scale | Datadog
Datadog joins Google Cloud Platform Partner Ecosystem to Monitor Infrastructure and Applications at Scale

Datadog joins Google Cloud Platform Partner Ecosystem to Monitor Infrastructure and Applications at Scale

Datadog aggregates, visualizes, correlates and alerts on Google App Engine performance metrics

Datadog aggregates, visualizes, correlates and alerts on Google App Engine performance metrics

November 4, 2014

2:00 PM UTC

Published by Cision PR Web

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Alex Rosemblat

press@datadoghq.com

Datadog, the SaaS-based monitoring and data analytics platform for dynamically scaling cloud infrastructure, announced today that it has joined the Google Cloud Platform partner ecosystem with the release of monitoring for Google App Engine. This release will allow Google Cloud Platform customers to visualize, analyze and alert on the performance metrics from their Google Cloud Platform applications and infrastructure. Datadog’s monitoring capabilities will be demonstrated during the Deploy and Operate session at Google Cloud Platform Live in San Francisco, CA.

Elastic applications that run on Google App Engine can allocate and deallocate resources on demand for specific application components. Traditional monitoring tools, which were built for infrequently provisioned resources, are no longer suitable for monitoring the behavior of such applications.

Datadog’s monitoring solves the problem of aggregating data in real-time from rapidly changing resources and allows customers to understand the true behavior of horizontally scaling applications. Infrastructure and application monitoring through Datadog will be available to Google App Engine users after signing up for a free Datadog account and enabling the Google App Engine integration.

Google App Engine customers that use Datadog will be able to:

• Alert on Metrics: Users can set alarms on thresholds or changes in Google App Engine metrics for individual instances or an aggregated value collected from multiple hosts • Visualize Performance: Aggregated values from multiple servers, or counters from individual devices can be visualized through a variety of graphing options • Correlate Data to Identify Issue Root Cause: Users will be able to determine which infrastructure event caused an issue by overlaying alerts and configuration changes over metrics from Google App Engine, other cloud platforms, and 80+ integrations with other commonly used tools and services. • Collaboratively Troubleshoot: Teams can share dashboards, as well as comment and work off of the same data and alerts. “Google Application Engine allows us to focus on building our application code and not on the scaling of our highly dynamic server infrastructure. Datadog provides the same benefits, as it scales alongside our infrastructure and immediately alerts us of performance issues. This insight is critical to maintaining our site reliability,” says Ryan Bonham, Production Systems Manager, Workiva

“We’re pleased to join the Google Cloud Platform ecosystem and make our monitoring service available for Google Cloud Platform customers," says Amit Agarwal, Chief Product Officer, Datadog, “Our collaboration with the Google Cloud Platform team will ensure tight monitoring integration as new enhancements to Google Cloud Platform are released.”

General Availability Datadog Free Edition is available for up to 10 Google App Engine projects and 5 Google Compute Engine instances. Pricing for Datadog’s Pro edition starts at $15 per monitored instance per month. Customers can sign-up for a trial at http://www.datadog.com/. Hourly pricing is available for non-persistent resources.

About Datadog

Datadog is a monitoring service that brings together data from servers, databases, applications, tools and services to present a unified view of the infrastructure. These capabilities are provided on a SaaS-based data analytics platform that enables Dev and Ops teams to work collaboratively on the infrastructure to avoid downtime, resolve performance problems and ensure that development and deployment cycles finish on time.