AWS Observability with Datadog

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Delivery methods

On-Site, Virtual

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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.

Course outline

  • The "Build vs. Buy" Debate
  • Total Cost of Ownership (TCO)
  • Managed SaaS vs. Open Source
  • 1-Click AWS Integration
  • CloudWatch Granularity
  • Demo: Onboarding an AWS Organization
  • The Datadog Agent v7
  • Host-Level Intelligence
  • Fleet Automation at Scale
  • Programmatic Provisioning
  • Python API Client
  • Lab: Deploying a "Gold Standard" Dashboard via Python
  • The Cold Start Problem
  • Distributed Tracing for Lambda
  • AWS Step Functions Visualization
  • Zero-Invasive Instrumentation
  • Demo: Monitoring Serverless Workloads with Lambda Layers
  • Observability for Non-Deterministic Systems
  • Bedrock AgentCore Monitoring
  • Tracing the "Chain of Thought"
  • Hallucination and Guardrail Detection
  • Bits AI: The SRE Copilot
  • Demo: Tracing an AI Agent on Amazon Bedrock
    • Vertical-Specific Strategy
    • Fintech: High-Frequency Fraud Detection
    • Healthcare: Redacting PII with Sensitive Data Scanner
    • Media: Monitoring Global Streaming Performance
    • Labs (Vertical Specific):
    • Fintech: Anomaly Detection on AWS Aurora
    • Healthcare: Compliance Auditing with CloudTrail
    • Media & Entertainment: Real User Monitoring (RUM) for Live Events
  • Vendor-Neutrality with ADOT
  • The OTel Collector vs. Datadog Agent
  • Unified Service Tagging
  • Secure Multi-Org Management

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