Project 7: Real-Time Dashboard App

Build a high-trust metrics dashboard with freshness guarantees, anomaly drill-downs, and graceful degradation.

Quick Reference

Attribute Value
Difficulty Advanced
Time Estimate 2-3 weeks
Main Programming Language TypeScript
Alternative Programming Languages Python, Elixir
Coolness Level Level 4
Business Potential High
Prerequisites Metrics basics, time-series aggregation, visualization
Key Topics Freshness policy, chart explainability, anomaly drill-down

1. Learning Objectives

  1. Design dashboard tools for aggregate and drill-down paths.
  2. Expose freshness state explicitly to users.
  3. Build anomaly interaction patterns that stay interpretable.
  4. Prevent stale-data confusion in conversational contexts.

2. All Theory Needed (Per-Concept Breakdown)

Trustworthy Operational Visualization

Fundamentals A dashboard is trustworthy only when users understand freshness, scope, and uncertainty. In chat contexts, users often assume “latest” even when data is stale, so your app must communicate recency and reliability clearly.

Deep Dive into the concept Split dashboard concerns into three contracts: aggregation, freshness, and explanation. Aggregation tools should define explicit windows and metrics. Freshness contract should classify states (fresh, stale, expired) with user-visible badges. Explanation contract should provide anomaly context and likely contributors.

Do not overload one tool for everything. Keep get_dashboard_overview separate from get_anomaly_details. This improves planner routing and lowers latency for routine interactions. Track data timestamps and include them in every result envelope.

When data backends lag, degrade gracefully. Show last-known-good values with clear warning labels and optional manual refresh action.

Minimal concrete example

overview result:
{ window:"6h", generated_at:"...", freshness:"stale", metrics:{p95_ms:410,error_rate:0.018} }

3. Project Specification

3.1 What You Will Build

A dashboard widget with KPI cards, trend chart, and anomaly drill-down panel.

3.2 Functional Requirements

  1. Show KPI metrics with freshness labels.
  2. Render trend chart with anomaly markers.
  3. Support click-through anomaly details.
  4. Handle stale/expired data modes.

3.3 Real World Outcome

User asks for API latency last 6h.
Widget shows KPI cards + chart.
User clicks anomaly marker.
Widget shows contributors and recommended checks.

4. Solution Architecture

dashboard tools -> metric envelope -> chart transformer -> widget state -> anomaly drill-down tool

5. Implementation Guide

5.1 The Core Question You’re Answering

“How do I show near-real-time metrics without misleading users about data freshness?”

5.2 Concepts You Must Understand First

  1. Aggregation window semantics.
  2. Freshness policy design.
  3. Explainable anomaly summaries.

5.3 Questions to Guide Your Design

  1. Which metrics require near-real-time updates?
  2. How are stale thresholds chosen?
  3. How do drill-down summaries stay concise and actionable?

5.4 Thinking Exercise

Define fresh/stale/expired thresholds and simulate one incident timeline.

5.5 The Interview Questions They’ll Ask

  1. How do you avoid stale-data trust failures?
  2. What is a safe polling cadence?
  3. How do you test chart correctness?
  4. Why split overview and drill-down tools?
  5. Which metrics indicate dashboard health?

5.6 Hints in Layers

  • Hint 1: Build static snapshots first.
  • Hint 2: Add freshness labels next.
  • Hint 3: Add anomaly drill-down tool.
  • Hint 4: Simulate stale backend behavior.

5.7 Books That Will Help

Topic Book Chapter
Reliability thinking “How Linux Works” System observability mindset
Defensive design “Code Complete” Verification chapters
Practical architecture “Clean Architecture” Use-case boundaries

6. Testing Strategy

  • Aggregation correctness tests.
  • Freshness label transition tests.
  • Drill-down consistency tests.

7. Common Pitfalls & Debugging

Pitfall Symptom Solution
Hidden stale data Users trust wrong values Always display freshness state
Mixed windows KPI/chart mismatch Centralized window config
Overloaded tools Slow, unstable routing Split overview and drill-down

8. Extensions & Challenges

  • Add multi-service comparison mode.
  • Add SLO budget burn visualization.
  • Add alert acknowledgement workflow.

9. Real-World Connections

  • SRE dashboards
  • Product analytics operations
  • Revenue monitoring systems

10. Resources

  • OpenAI build/test docs
  • OpenAI troubleshoot docs

11. Self-Assessment Checklist

  • I can explain freshness policy choices.
  • I can prove KPI/chart consistency.
  • I can provide anomaly drill-downs users can act on.

12. Submission / Completion Criteria

Minimum Viable Completion

  • Dashboard with freshness labels and anomaly details.

Full Completion

  • Includes degraded modes, replay tests, and clarity-focused UX states.