Datadog – Splunk Integration

Data Types: Problems / Metrics

Datadog (Problems) → Splunk (Problems)

Datadog (Events) → Splunk (Events)

Datadog – Splunk Integration Use Case

If you are using both Datadog and Splunk to monitor different aspects of your IT infrastructure, an integration between the two would help you unite monitoring, troubleshooting, and optimizing your entire stack performance. When you have a complete overview on your applications, infrastructure, and teams, you can easily take the right decisions.

For example, Datadog has detected a problem, and the company’s IT teams needs to have this problem in Splunk in order to collect the complete data and trigger an action. ZigiOps makes an instant connection, and the problem is automatically present in Splunk for further processing.

Another case is when Datadog has recorded some metrics that are not collected by Splunk. ZigiOps transfers the metrics immediately and everything is up to date in real-time. You can easily set correlation and mapping in ZigiOps, so only the necessary information is transferred.

Challenges for Datadog and Splunk users

Datadog helps you monitor, troubleshoot, and optimize application performance. With Splunk you can monitor and analyze your data, as well as lower data breaches and other fraud risks.

Using both Datadog and Splunk in your organization gives you broader information and helps you collect specific data. However, if they are not connected, you will have the data scattered and it would be more difficult to act on it.

Real-time synchronization between your monitoring systems is critical if you want to take the appropriate actions quickly and avoid delays and silos on important decisions.

How do we solve them?

An integration platform can help you connect Datadog and Splunk and seamlessly transfer data between them. You can do this in a matter of minutes with ZigiOps.

With ZigiOps you can get all metrics and problems from Datadog directly delivered into Splunk. Any updates are also automatically transferred.

The integration between the two allows you to search, analyze, or visualize your data easier, detect application anomalies faster, and instantly discover and prevent problems.