● Active SaaS Data Visualization React/Next.js

Nova Analytics Platform

A real-time business intelligence dashboard enabling enterprise clients to visualize complex datasets, automate reporting workflows, and make data-driven decisions at scale.

Q3 2024 Timeline

6 Team Members

98% Uptime SLA

v2.4.1 Current Build

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Core Stack

Next.js 14, TypeScript, Tailwind, PostgreSQL, Redis, AWS Lambda

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Security

SOC2 Type II compliant, RBAC, End-to-end encryption, Audit logging

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Performance

<50ms query latency, 99.9% availability, Auto-scaling architecture

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Integrations

Stripe, Snowflake, Salesforce, Slack, Custom Webhooks API

Project Scope & Objectives

Nova was designed to replace fragmented legacy reporting tools with a unified, real-time analytics layer. The platform ingests data from 12+ sources, normalizes it through a custom ETL pipeline, and surfaces actionable insights via customizable dashboards.

Key outcomes include a 40% reduction in time-to-insight, automated anomaly detection, and role-based access controls tailored for enterprise compliance requirements.

[Interactive Dashboard Preview]

System Architecture

Built on a serverless-first architecture with event-driven data processing. The frontend utilizes React Server Components for optimal initial load times, while the backend leverages containerized microservices orchestrated via Kubernetes.

Data flows through a Kafka streaming layer into partitioned PostgreSQL clusters, with Redis handling session caching and real-time WebSocket updates.

[System Diagram Placeholder]

Key Deliverables

Phase 1 delivered the core dashboard, user management, and data connector framework. Phase 2 introduced advanced filtering, scheduled reports, and the public REST API. Phase 3 focuses on AI-driven anomaly detection and predictive modeling.

All code is hosted in a private monorepo with automated CI/CD pipelines, comprehensive E2E testing, and documentation generated from JSDoc comments.

[Roadmap & Milestone Tracker]

Usage & Performance Metrics

Live telemetry shows an average of 4,200 active daily users with a peak concurrent load of 850. API response times consistently remain under 120ms, with p99 latency capped at 340ms.

User engagement metrics indicate a 72% weekly retention rate and an average session duration of 18 minutes. The export engine processes an average of 15,000 reports monthly.

[Live Metrics Chart Placeholder]

Development Timeline

Mar 2024

Discovery & Architecture

Requirements gathering, technical spike, system design, and initial prototype validation with stakeholder feedback.

May 2024

MVP Development

Core dashboard UI, data ingestion pipelines, authentication flow, and foundational API endpoints shipped.

Aug 2024

Scale & Optimization

Performance tuning, caching layer implementation, advanced filtering, and enterprise SSO integration.

Nov 2024

AI & Predictive Features

Machine learning model integration for anomaly detection, automated reporting, and forecast generation.