Datadog / Sentry
The observability platforms that tell you what your production systems are doing — and what just broke.
What it is
About Datadog / Sentry
Datadog is the market-leading cloud observability platform, integrating metrics, logs, traces, and APM (Application Performance Monitoring) into a unified dashboard. It monitors infrastructure health (CPU, memory, disk across thousands of hosts), application performance (request latency, error rates, dependency traces), and logs in real time. Engineers configure monitors and alerting policies that fire when thresholds are breached, integrate with on-call systems (PagerDuty, Opsgenie), and use distributed tracing to follow a single user request through every service it touches — identifying exactly where latency or failures originate. Datadog is the system that runs in the background of every production application, generating the data that makes incident response possible. Sentry specialises in error monitoring and application performance at the code level. Where Datadog surfaces infrastructure and service metrics, Sentry captures every unhandled exception in your application — complete with the stack trace, the exact line of code, the environment variables, the user's browser and device, and a replay of the events leading up to the error. A Software Engineer opening Sentry after a production incident can see exactly which error fired, how many users it affected, and drill into a specific occurrence with a full stack trace. Sentry integrates with GitHub to link errors directly to the commit that introduced them, and with Jira and Linear to create issues automatically.
What you can do with it
Capabilities
Create a Datadog monitor on API error rate that fires a PagerDuty alert when the 5-minute average exceeds 2%, and set a recovery threshold so the alert resolves automatically when the rate drops below 0.5%
Use Datadog APM's distributed trace view to follow a single slow API request across five microservices, identify the database query causing a 3-second latency spike, and share the trace link in a Slack incident thread
Open a Sentry issue for a NullPointerException that affected 200 users, examine the full stack trace and the breadcrumb trail of user actions that preceded it, link it to the commit that introduced the regression, and assign it to the responsible engineer
Build a Datadog dashboard showing the four golden signals (latency, traffic, errors, saturation) for a production service — saved as a shared URL so the on-call engineer can see the current health state in one view
Set up Sentry performance monitoring with custom spans around a database query that is called on every page load, track its p95 latency over a week, and use the data to justify a caching layer investment
How to learn it
Learning Resources
Datadog Learning Center (learn.datadoghq.com) — free browser-based courses including "Introduction to Monitoring" and "Log Management Fundamentals"; uses a live Datadog sandbox environment
Sentry documentation (docs.sentry.io) — the Getting Started guide sets up error tracking in any language in under 15 minutes; the best introduction is connecting it to a real project and triggering your first error
Datadog's free trial (14 days) provides a real environment with sample data — walk through the APM product and trace a request through the sample application to understand the distributed tracing model
"The Art of Monitoring" by James Turnbull (free preview at artofmonitoring.com) — the conceptual foundation for understanding what monitoring should achieve before learning the specific tools
Pro Tip
Set up error grouping rules in Sentry before your error volume grows. By default, Sentry groups errors by stack trace fingerprint — which works well until you have 50 variations of the same underlying error appearing as separate issues. Custom fingerprinting rules that group by error type and the outermost function name keep your issue list manageable and ensure duplicate alerts don't drown the on-call engineer.
Skills that use this tool
Roles that use this tool
Alternatives