RevenueLens is a full-stack SaaS application that transforms raw sales CSV exports into actionable financial dashboards. It solves the problem of manual spreadsheet reporting by providing automated ETL pipelines, data visualization, and revenue forecasting.

🔗 Live Demo: Ver en Vercel

• High-Performance CSV Parsing: Server-side parsing using PapaParse to handle large datasets efficiently.
• Batch Processing: Implements transactional batch inserts into PostgreSQL to ensure data integrity during uploads.
• Smart Validation: Automatic column detection and data sanitization (currency cleaning, date normalization).

• Financial Forecasting: Built-in algorithm (Moving Average) to project future revenue based on historical trends.
• Dynamic Aggregation: Uses SQL Views (daily_metrics, dataset_kpis) for real-time aggregation without impacting read performance.
• Interactive Charts: Responsive visualizations using Recharts for revenue trends and product distribution.

• Multi-Tenancy: Strict data isolation using Row Level Security (RLS) policies in PostgreSQL. Users can only access data belonging to their business workspace.
• Next.js 15 App Router: Leveraging Server Components for data fetching and Client Components for interactivity.
• Optimistic UI: Staggered animations and skeleton loaders (Framer Motion) for a seamless user experience.

• Framework: Next.js 15 (App Router)
• Language: TypeScript
• Database: PostgreSQL (via Supabase)
• Auth: Supabase Auth (Magic Link, OAuth, Email/Password)
• Styling: Tailwind CSS + Shadcn UI
• Visualization: Recharts
• Infrastructure: Vercel

The application follows a strict separation of concerns with a heavy reliance on database-level logic for performance:

graph TD
    A[Client Upload CSV] -->|Server Action| B(CSV Parser / Validator)
    B -->|Batch Insert| C[(PostgreSQL Raw Sales)]
    C -->|Trigger/View| D[Materialized Views & KPIs]
    D -->|RLS Policy| E[Dashboard UI]
    C -->|Forecasting Algo| F[Prediction Engine]
    F --> E

The database is designed for scale with normalized tables and RLS enabled:

• profiles & businesses: Handle user hierarchy and workspace grouping.

• datasets: Metadata for uploaded files (status tracking: processing -> ready).

• sales: The core ledger containing individual transaction records linked to datasets.

1. Clone the repo

git clone [https://github.com/your-username/revenuelens.git](https://github.com/your-username/revenuelens.git)

1. Install dependencies

npm install

1. Environment Setup Create a .env.local file with your Supabase credentials:

NEXT_PUBLIC_SUPABASE_URL=your_project_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_anon_key

1. Run Development Server

npm run dev