Building Live Streaming Dashboards in Grafana

Multi-panel build · Variables · Assignment 3 guidance

Learning Objectives

• ✓ Build a multi-panel Grafana dashboard using live MongoDB data
• ✓ Use dashboard variables to make charts interactive and filterable
• ✓ Apply dashboard design principles: hierarchy, colour meaning, panel arrangement
• ✓ Connect visualisation choices to the business questions being answered
• ✓ Produce a dashboard suitable for Assignment 3 presentation

Session Timeline

Time	Segment	Activity
0–10 min	Setup Check	Verify Grafana running, producer running, data flowing
10–20 min	Design Principles	What makes a dashboard useful vs pretty
20–50 min	Lab Part 1	Build stock monitor dashboard (4 panels)
50–65 min	Lab Part 2	Add variable dropdown + alert summary panel
65–75 min	Transactions Dashboard	Quick build from L10 transactions-topic data
75–85 min	Design Review	Student dashboards — what works, what doesn't
85–90 min	A3 Guidance	Assignment 3 requirements and tips

Section 1: Dashboard Design Principles

Section 2: Lab Part 1 — Stock Monitor Dashboard

Target Layout

┌────────────────────────────────────────────────────────┐
│  Stock Monitor  [Symbol: INFY ▼]  [Last 30 min ▼]      │
├──────────────┬──────────────┬──────────────────────────┤
│  Current     │  Open        │                          │
│  Price       │  Alerts      │  INFY Price (30 min)     │
│  (Stat)      │  (Stat)      │  (Time Series)           │
├──────────────┴──────────────┤                          │
│  Price Change from          ├──────────────────────────┤
│  Last Batch (%)             │  Alert Log (Table)       │
├─────────────────────────────┤  Latest 5 alerts         │
│  Min / Max Bar Gauge        │                          │
└─────────────────────────────┴──────────────────────────┘

Panel 1 — Current Price (Stat)

{
  "collection": "stock_prices",
  "aggregate": [
    { "$match": { "symbol": "INFY" } },
    { "$sort": { "_saved_at": -1 } },
    { "$limit": 1 },
    { "$project": { "_id": 0, "price": 1 } }
  ]
}

Settings: Visualisation = Stat | Unit = ₹ | Thresholds = none (price level is neutral)

Panel 2 — Open Alerts Count (Stat)

{
  "collection": "stock_alerts",
  "aggregate": [
    { "$match": { "status": "open" } },
    { "$count": "value" }
  ]
}

Thresholds: 0–2 = green | 3–5 = amber | 6+ = red

Panel 3 — Price History Time Series

{
  "collection": "stock_prices",
  "aggregate": [
    { "$match": { "symbol": "INFY", "_saved_at": { "$gte": "$__from" } } },
    { "$sort": { "_saved_at": 1 } },
    { "$project": { "_id": 0, "time": "$_saved_at", "price": 1 } }
  ]
}

Settings: Line width = 2 | Fill opacity = 10 | Unit = ₹ | Tooltip = All

Panel 4 — Alert Log Table

{
  "collection": "stock_alerts",
  "aggregate": [
    { "$sort": { "alert_at": -1 } },
    { "$limit": 5 },
    { "$project": {
        "_id": 0, "symbol": 1,
        "mean_price": "$stats.mean",
        "min": "$stats.min", "max": "$stats.max",
        "reason": { "$arrayElemAt": ["$alert_reasons", 0] },
        "time": "$alert_at"
    }}
  ]
}

Settings: Visualisation = Table | Sort by Time descending

Section 3: Lab Part 2 — Variables & Interactivity

Adding a Symbol Dropdown

Variables make dashboards reusable — one dashboard works for INFY and TCS instead of building two.

1. Dashboard settings (gear icon) → Variables → New variable
2. Name: symbol | Type: Custom | Values: INFY, TCS | Default: INFY
3. Save. Now replace "symbol": "INFY" with "symbol": "$symbol" in all panel queries.

The dropdown now filters all panels simultaneously — one click switches the entire dashboard from INFY to TCS.

Saving & Sharing

1. Save icon → name: "SDA — Stock Monitor"
2. Share → Snapshot → generates a public link (no Grafana login required to view)

Section 4: Transactions Dashboard

Quick build using the transactions-topic data from Lecture 10. Use as a second pattern reference for Assignment 3.

┌─────────────────────────────────────────────┐
│  Transaction Monitor  [City: All ▼]  [1h ▼] │
├─────────────┬───────────────────────────────┤
│ Total Txns  │  Transaction Volume             │
│ Last Hour   │  Time Series — count per min   │
│  (Stat)     │                                │
├─────────────┴───────────────────────────────┤
│  Avg Amount per Minute  │  Top Cities        │
│  (Time Series)          │  (Bar Chart)       │
└─────────────────────────┴────────────────────┘

Queries follow the same pattern as stock panels — change collection to transactions and field names to match your L10 schema (amount, city, timestamp).

Section 5: Design Review Checklist

For each dashboard, evaluate:

Question	What to Look For
Does it answer a clear business question?	"Who is the user? What decision are they making?" — one sentence
Is colour usage consistent?	Red = alert/bad, Green = normal. No decorative red on neutral data.
Is the refresh rate appropriate?	30s is fine for demos; 5s for live risk monitoring
Are axes labelled with units?	Always: ₹, count, %, minutes — never raw numbers without context
Is the time range visible?	Users should always know what window of data they are looking at

Assignment 3 Guidance

Due: 1 October | 15 marks

Create a dashboard for the analysis of your consumed data (the domain from A1, streamed in A2).

Requirement	Detail
Minimum 4 panels	At least 1 time series, 1 stat/KPI, 1 table
Real data	Connected to your Kafka consumer data (MongoDB or InfluxDB)
1 variable/filter	Makes the dashboard interactive
Colour thresholds	Reflect actual business logic — not default colours
Explanation	200-word write-up: what business question each panel answers

Submission: Export dashboard JSON (Dashboard → Share → Export JSON) + screenshots. Upload to GitHub with tag [A3][SDA-1], [A3][SDA-2], or [A3][SDA-G].

Key Takeaways