How Anjeer Labs Built a Real-Time Analytics Dashboard for a Business Processing 20L/Month

What it takes to build a dashboard founders can actually trust — and act on.

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The Problem With Most Analytics Dashboards

Most analytics dashboards lie — not with wrong data, but with old data.

By the time a founder opens their dashboard and sees a spike in drop-offs or a sudden revenue dip, the moment to respond has already passed. For businesses operating at scale, that delay isn't just inconvenient. It's costly.

When a client came to us processing over 20 lakhs in monthly transactions, they had exactly this problem. They were flying blind in near-real-time watching metrics that were always minutes or hours behind, unable to trust whether the numbers were even accurate.

They needed a live analytics dashboard:

• Real active user counts
• Real revenue figures
• Real conversion rates
• Updating as events happened, not after the fact

This is how we built it.

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Starting With Strategy, Not Code

Before any architecture decisions, we defined what success looked like:

• Live metrics with sub-one-second latency
• Reliable data with no duplicate counting
• A system that could scale without a full rebuild

We also established:

• Weekly client updates
• Working prototypes from day one

This isn't just good communication — it's good engineering. Catching a flawed data model or a broken tracking assumption in a prototype costs a fraction of fixing it after full implementation.

Every major technical decision was tested against a real business requirement. That discipline shaped everything that followed.

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The Technology Stack — And Why Each Choice Was Made

Frontend: Next.js (React + TypeScript)

We chose Next.js for the dashboard interface because it gives us:

• Fast initial loads via server-side rendering
• A rich, dynamic client experience after hydration

For a dashboard that business teams open first thing every morning, first-load performance matters.

The UI displays live charts — active users, revenue, conversions using responsive components designed to stay smooth even as data updates continuously.

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Backend: Node.js (Monolithic Architecture)

We deliberately chose a monolithic Node.js backend over microservices.

For this project, that meant:

• Faster development
• Simpler testing
• Easier deployment
• No cross-service latency

We centralized:

• Caching logic
• API layer
• Real-time event handling

The structure still allows future extraction into services if needed, but the monolith gave us speed and stability without unnecessary complexity.

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Data Layer: PostgreSQL + Redis

We used two data stores, each for a specific purpose.

PostgreSQL → Persistent & Historical Data

Used for:

• Historical reports
• Aggregated summaries
• Complex time-range queries

Configured with:

• Managed PostgreSQL
• PgBouncer connection pooling
• Support for thousands of concurrent requests

Redis → Real-Time Data

Used for:

• Pageviews
• Active sessions
• Click events
• Revenue counters

All stored as in-memory hashes, updated per event, retrievable in milliseconds.

We use Redis pipelines to batch dashboard queries into a single round trip, enabling always-current metrics with minimal overhead.

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Real-Time Updates: WebSockets

Instead of polling every few seconds, we maintain a persistent WebSocket connection.

When new data arrives:

• A purchase happens
• A session starts
• A conversion fires

The server pushes updates instantly to all connected dashboards.

Charts move. Counters tick. No refresh. No delay.

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Infrastructure: DigitalOcean

We deployed the system on DigitalOcean using:

• Droplets for compute
• Managed PostgreSQL
• Load balancers with WebSocket sticky sessions

Services are containerized with Docker, enabling:

• Autoscaling during traffic spikes
• Reliable deployments
• Cost-effective operation at scale

For this volume, we needed infrastructure that is stable, quiet, and predictable.

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How Real-Time Data Actually Flows

From user action to dashboard update:

1. Tracking Library
   A lightweight client script captures events (page view, click, purchase).
   Each user gets a persistent ID + session ID to prevent double-counting.
   Events are batched and sent to the API.

2. Event Ingestion
   The backend receives the event and increments the correct Redis counter.
   Example:

   • Purchase → revenue hash for today
   • Page view → session counter

3. Aggregation
   On dashboard request, Redis pipelines fetch all relevant counters in one batch.
   The backend computes:

   • Totals
   • Trends
   • Period-over-period comparisons

4. Live Delivery
   New events trigger WebSocket pushes to connected clients.
   The UI updates instantly.

Result: fast, accurate, duplicate-proof metrics stakeholders can trust.

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Quality, Testing, and What Happens After Launch

We test every feature before release:

• Unit tests (Jest) → API logic & data computation
• End-to-end tests (Cypress) → Real dashboard user flows
• Continuous performance monitoring →
  • Slow queries
  • API latency
  • Runtime errors

Post-launch, we provide:

• Performance tuning
• Security updates
• Feature expansion

At Anjeer Labs, we build for the long term and stay accountable to outcomes — not just deliverables.

10+ projects delivered. 98% client satisfaction.

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What This Means for Founders

The best analytics tools aren't just fast — they're trustworthy.

When a number changes on your dashboard, you should be able to act immediately, knowing it reflects what is happening right now in your business.

We built a system where:

• Latency is measured in milliseconds
• Accuracy is built into the architecture
• Infrastructure scales quietly as the business grows

If your business is operating at scale and your data can't keep up, you're not just behind on metrics — you're behind on decisions.

Anjeer Labs builds high-performance web applications and analytics systems for founders who need software that works at scale.
👉 Get in touch →

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Tags

Analytics Dashboard Real-Time Data Next.js Node.js Redis WebSockets Startup Tech SaaS Development Anjeer Labs