Following the Request

Short lessons for developers and operators investigating real production behaviour. Each lesson teaches one idea in about a minute, using small fictional examples, so you can use correlated telemetry to diagnose requests while managing instrumentation overhead, privacy and pipeline reliability.

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Wulo Academy 56 lessons · 1 exercise · Self-paced · Certificate

Overview

About this course

Short lessons for developers and operators investigating real production behaviour. Each lesson teaches one idea in about a minute, using small fictional examples, so you can use correlated telemetry to diagnose requests while managing instrumentation overhead, privacy and pipeline reliability.

Outcomes

What you will learn

Use correlated telemetry to diagnose requests while managing instrumentation overhead, privacy and pipeline reliability.

Before you start

Requirements

Basic application operations; the series introduces traces, metrics and logs before pipeline design. Lessons build on each other; each lesson lists the earlier lessons it relies on.

Curriculum

56 lessons across 2 modules

01 First Section [edit me] 1 lesson
Trace Observable Work
02 Ungrouped 55 lessons · 1 exercise
Reconstruct the Action
Useful Logs Need Not Contain Everything
Route Alerts to a Responder
One Failure Behind a Green Chart
Connect Relevant Evidence
Describe the Work
Time Is a Weak Joining Key
What a Span Tells You
How a Trace Connects Services
What Metrics Leave Out
Attach Logs to Their Trace
Resource or Request Attributes
Shared names need shared meaning
Parallel Spans Share Time
Match the Instrument to the Measure
See Latency as a Distribution
Exemplars Link Metrics to Traces
Attributes multiply metric series
Put Request Detail in Its Place
When Telemetry Meanings Change
Carry Context Across Boundaries
Baggage needs a travel policy
OpenTelemetry Is Not Your Backend
Link Requests to Host Conditions
Follow One Request
API Calls and SDK Processing
Missing Span Coverage
Add Business Meaning to Traces
Match Exporter and Receiver
Set Up Telemetry Before Work
Instrument in Proportion
Buffered Before Export
Flush Before Exit
Keep Only Useful Metric Detail
Libraries Should Use APIs Only
Explain It Inside the Library
Where Kubernetes Collectors Run
Export Before the Runtime Ends
Trace Work After the Response
Direct or Through an Intermediary
Move telemetry work locally
Plan Capacity for Collector Pools
Filtering or Sampling
Head Sampling Decides Early
Wait for the outcome
Selection Changes the Trace Picture
Redact Before the Boundary
Order Changes the Result
A Bigger Buffer Buys Time
Control Collector Endpoints
Tail Sampling Needs Trace Affinity
Coverage and Depth Are Separate
Test the Telemetry Contract
Preserve the Questions
What AI Traces Can Show

Skills & tools

What you'll walk away with

  • Following the Request

Your instructor

Taught by a practitioner

Wulo Academy