Operations | Monitoring | ITSM | DevOps | Cloud

In part 2 of this 4 part series, you’ll learn how to instrument your NodeJS application to capture custom method-level spans, allowing visibility into how specific methods behave in your application. Flame graphs allow for deep insight into the performance of your code. During instrumentation, we can capture custom spans for deeper layers of visibility in the resulting flame graphs. In this video, we use instrumentation to capture a method-level span, allowing us to see the performance of that specific method in our flame graphs in the Datadog UI.

Datadog Security Monitoring detects threats across the full stream of ingested observability data and leverages hundreds of integrations to give you full visibility across your environment. Now development, operations, and security teams in your organization can use a unified platform to secure your infrastructure and applications.

In this video, you’ll learn how to manually instrument APM for your Python application, granting you performance visibility into any of your Python applications.

In this video, you’ll learn how to create and scale dynamic notifications in your Datadog monitors using template variables.

In this video, you’ll learn how Datadog's App Analytics helps quickly identify and alert on your slowest queries to reduce time to resolve bottlenecks and increase cross-team collaboration.

In this video, you'll learn how to create, configure, and deploy a custom check for your Datadog agent to run on a Raspberry Pi. The results are custom metrics sent into your Datadog account which track your service provider's network speeds over time.

In part 1 of this 2 part series, you’ll learn how to use pattern matching to identify Grok log parsing rules and apply them to pipelines for effective log indexing.

In this video, you'll learn how to directly compare values from your Amazon CloudWatch console with metrics presented in Datadog.

Using hooks to add application specific tags to spans in dd-trace-js.