AWS data lakes require real-time monitoring as their complexity increases to maintain reliable data pipelines. The combination of Datadog for AWS service monitoring and Microsoft Teams as an alerting platform enables efficient incident response.

Integration Overview

Datadog + AWS:

To activate AWS integration in Datadog you need to set up an IAM role. The system should gather metrics and logs from Glue and Lambda and S3 services. The Datadog Forwarder Lambda functions to transmit CloudWatch logs.

Datadog Monitors:

Create monitors for:

  • Glue job failures (aws.glue.jobs.failed)
  • S3 data lag (custom metrics based on file timestamps)
  • Athena query errors (via logs)
  • Data volume drops (anomaly detection on S3 metrics)

Alerting via Microsoft Teams:

  • Set up an incoming webhook within a Teams channel.
  • Add the webhook in Datadog's integration settings.
  • Attach it to monitors to send real-time alerts.

Best Practices

  • AWS resources need tagging to create dashboards and alert filters.
  • Suppress alerts during maintenance windows.
  • Alerts should include dashboard links together with runbooks to enable faster team action.

Example Alert (in Teams):

  1. 🚨Glue Job Failed
  2. Job: daily_sales_load
  3. View in Datadog

The integration enables your team to discover and fix problems such as ETL job failures and delayed ingestion data as soon as they occur to maintain healthy pipelines and informed teams.