Real-Time Analytics Dashboard
A high-performance analytics dashboard that processes millions of events daily and delivers real-time insights through interactive visualizations.
Tech Stack
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Key Outcomes
- Reduced report generation time from 45 minutes to under 30 seconds
- Scaled to handle 12M+ daily events with sub-second query latency
- Adopted by 3 enterprise clients within the first quarter of launch
- Decreased customer churn by 18% through actionable data insights
Overview
The analytics dashboard was built to replace a legacy reporting system that required manual CSV exports and hours of spreadsheet manipulation. The goal was to provide stakeholders with real-time, self-service access to key business metrics.
Technical Architecture
The system follows an event-driven architecture with three main layers:
Ingestion Layer — A lightweight Node.js service receives events via HTTP and WebSocket connections, validates payloads against JSON schemas, and publishes them to a Redis Streams topic.
Processing Layer — A set of worker processes consume from Redis Streams, perform aggregations and windowed computations, and write materialized views to PostgreSQL. Time-series data is partitioned by day using TimescaleDB for efficient range queries.
Presentation Layer — The Next.js frontend fetches pre-aggregated data via REST endpoints and subscribes to real-time updates through WebSocket channels. D3.js powers the interactive charts with smooth transitions and drill-down capabilities.
The combination of pre-aggregated materialized views and real-time WebSocket updates allows the dashboard to show fresh data within 2 seconds of event ingestion while keeping query times under 100ms.
Key Challenges
Handling Bursty Traffic
During peak hours, event volume could spike 10x above baseline. We implemented adaptive batching in the ingestion layer — events are buffered for up to 50ms or 1000 events (whichever comes first) before being flushed to Redis. This smoothed out write amplification without adding perceptible latency.
Query Performance at Scale
With billions of rows accumulating over months, naive queries became untenable. We solved this with:
- Continuous aggregation — TimescaleDB continuous aggregates pre-compute hourly and daily rollups
- Chunk exclusion — Partitioning by time allows the query planner to skip irrelevant partitions
- Connection pooling — PgBouncer in transaction mode keeps connection counts predictable
Results
The dashboard shipped on schedule and immediately improved how the data team works. Report generation went from a 45-minute manual process to a single click. Three enterprise clients adopted the platform in Q1, and customer churn dropped 18% as teams gained visibility into usage patterns.