AWS Observability with Datadog

Delivery methods

On-Site, Virtual

Duration

1 day

This comprehensive, vertical-specific training demonstrates the strategic value of Datadog for monitoring and securing modern AWS environments, from foundational infrastructure to cutting-edge AI agents. Students will learn how to unify fragmented telemetry into a single source of truth to accelerate innovation and reduce operational overhead. By the end of the day, participants will be able to justify the "Build vs. Buy" investment and implement advanced observability for serverless and GenAI workloads.

Learning Objectives

Articulate the Datadog on AWS value proposition using the Total Cost of Ownership (TCO) framework
Configure the 1-click AWS integration to achieve CloudWatch metric granularity across multi-account environments.
Deploy and manage the Datadog Agent with minimal resource overhead using Fleet Automation.
Implement Serverless Monitoring to track cold starts and end-to-end traces across AWS Lambda and Step Functions.
Monitor and secure AI Agents on Amazon Bedrock using Datadog LLM Observability.
Automate observability workflows using the Datadog Python API and OpenTelemetry (ADOT).
Create industry-specific dashboards to align DevOps, Security, and Business stakeholders.

Who Should Attend

Architects, developers, DevOps engineers, and technical analysts working on AWS-based applications or migrating existing workloads to the cloud. Some previous operations experience, including deploying and managing IT systems, either on-premises or in a public cloud environment, is assumed. Prior experience with AWS services (EC2, Lambda, S3) is required, and basic familiarity with Python and JSON will be helpful for the hands-on programmatic automation segments.