February 2015 Summaries

The guest post from Kiyoto Tamura, developer evangelist at Treasure Data, discusses the benefits of building a unified logging layer using Fluentd, an open source data collector to unify logging infrastructure. It highlights four key properties of Fluentd: Unified Logging with JSON, Pluggable Architecture, Minimal Resources Required, and Built-in Reliability. The post also explains how Fluentd integrates with Datadog and provides step-by-step instructions on setting up the Datadog Agent to monitor Fluentd. Finally, it emphasizes the importance of visualizing and monitoring Fluentd's performance using a platform like Datadog.

Fluentd is an open source data collector that unifies logging infrastructure by collecting, filtering, buffering, and outputting logs across multiple sources and destinations. It has a flexible plugin system, minimal resources required, built-in reliability, and can be integrated with Datadog for monitoring and visualization. The integration allows users to monitor Fluentd's performance as well as over 100 other applications and components, providing insights into site performance, customer behavior, and gnarly bugs that can help accelerate growth by learning more from data faster.

Datadog has recently integrated with Amazon's Simple Notification Service (SNS), allowing users to send and receive SNS notifications through the platform. This integration enables automatic firing of Datadog alerts via SNS, including push notifications to mobile devices or other forms of communication like email or text messages. Additionally, Datadog can capture SNS messages in its event stream by subscribing to a "topic" (SNS access point). Key metrics from Amazon Cloudwatch about SNS are also captured and displayed on a dedicated dashboard for easy monitoring of the health and activity levels of your SNS environment. This integration opens up new possibilities for users, making it easier to manage their SNS-related tasks within Datadog's platform.

Datadog has launched an integration with Amazon's push messaging service SNS, allowing users to send and receive notifications through the platform. This integration provides a preconfigured dashboard for monitoring the health and activity levels of the user's SNS environment, capturing key metrics from Cloudwatch about SNS, including the number and size of published messages, delivery rates, and failure rates. The dashboard displays this information in a way that makes it easy to monitor the health and activity levels of the user's SNS environment, showing traffic over time, active topics, delivery rates, and a snapshot of the recent SNS stream. This integration opens up new uses for the service, allowing users to automatically fire off Datadog alerts via SNS or capture SNS messages in their event stream, providing a more comprehensive view of their Amazon SNS environment.

We are pleased to announce that the Mortar Data team has joined forces with Datadog, a fellow NYC-based enterprise startup. As long-time users of Mortar's platform, we have been impressed by its award-winning technology for building scalable data pipelines and analytics solutions. With Mortar now part of the Datadog family, we are already combining our resources to provide customers with even deeper insights into their infrastructure and applications. The acquisition marks a significant step forward in bringing sanity to both devops teams and operations, and we look forward to sharing more product advances with our users in the future.

The text discusses how to use the expvar package in Go to define variables that can be exported and published over http, allowing them to be exposed as metrics in Datadog. It provides a step-by-step guide on setting up an example application - a simple webserver that tracks the number of times a particular page has been visited in the last minute. The integration involves creating an expvar variable, defining the Datadog configuration file for expvar, and enabling it within the go_expvar.yaml file. Once set up, users can view the metric in the Metrics Explorer and use it to monitor their Go application performance.

Datadog has created various integrations with popular applications, allowing users to instrument their custom apps using DogStatsD libraries and the RESTful API. For developers writing in Go, Datadog Agent 5.1.0 introduces the ability to use expvar, a Go package that allows variables to be exported and published over HTTP. By following simple steps, developers can expose an expvar as a Datadog metric, enabling real-time monitoring of their application performance. An example using a trivial web server demonstrates how to define an expvar variable, create a counter, and set its value, which is then published to a standard location by the expvar package. The final step involves enabling the Datadog expvar configuration, allowing users to monitor their Go application performance in the Metrics Explorer.

Datadog has introduced a new Salesforce Desk integration that allows users to view trends in support case data, set alerts on case counts, and access new cases from events in the Events Stream. This integration enables support teams to monitor their case metrics alongside operational metrics, helping them determine if performance or availability issues are affecting support case volume. The integration also facilitates creating dashboards of support case overview statistics that can be filtered based on assigned user or case status. Datadog hopes this feature will help users stay informed about support issues and their impact on the organization's health.

Datadog has announced its new Salesforce Desk integration, which allows users to view trends in support case data and set alerts on support case volume. This integration provides added visibility into the relationship between performance or availability issues and support case volume, enabling teams to determine if there are any correlations between them. The integration also enables the creation of a dashboard for support case overview statistics, which can be easily shared with management and external teams, providing real-time data on top resolved cases by user and number of open cases over time.