Live Application

SQLSandboxes: Building an AI-Powered SQL Practice Platform

Project Overview

SQLSandboxes is a Next.js 15 web application that eliminates the friction of SQL practice by providing an interactive environment where users can execute queries against realistic datasets without any local setup. The platform leverages client-side SQL execution, AI-powered dataset generation, and a comprehensive learning system to make SQL accessible to everyone from beginners to interview candidates.

The Problem & Solution

Traditional SQL learning has a massive activation energy problem. Students spend hours setting up PostgreSQL, dealing with PATH variables, and VPN connections before writing a single query. Professionals need quick prototyping environments but can't risk experimental queries on staging databases. Interview candidates practice on generic employee tables that don't prepare them for real-world schemas.

SQLSandboxes solves this by providing zero-setup SQL practice with realistic, diverse datasets accessible instantly in any browser.

Technical Architecture

Core Stack

  • Frontend: Next.js 15 with App Router, React 19, TypeScript
  • Database Engine: SQL.js (SQLite via WebAssembly) for client-side execution
  • Backend: Supabase for user management, authentication, and analytics
  • Storage: AWS S3 for sample database definitions and custom datasets
  • Styling: Tailwind CSS 4 with custom components
  • AI Integration: OpenAI API for intelligent dataset generation

Key Architectural Decisions

Client-Side SQL Execution: Using SQL.js WebAssembly eliminates server load and provides instant query feedback. Users can execute complex queries with joins, window functions, and CTEs without any backend processing.

Hybrid Storage Strategy: Sample databases stored as JSON definitions in S3, converted to SQL DDL by a custom DatabaseLoader class. Custom user datasets leverage Supabase metadata with S3 file storage for scalability.

Progressive Authentication: Guest users get 3 free queries before signup, tracked via localStorage. Authenticated users get unlimited queries and custom dataset creation capabilities.

Early Traction & Marketing Experiments

In the first month of development and advertising, SQLSandboxes achieved impressive initial traction:

Traffic Metrics

  • 2,525 distinct visitors
  • 3,144 total page views
  • 30,000+ ad impressions
  • 5,000+ ad clicks

Advertising Channel Performance

  • Google Ads: Strong performance with targeted SQL-related keywords
  • Reddit Ads: Effective reach in programming and data communities
  • Upcoming Tests: Meta (Facebook/Instagram) ads and other platforms

This project serves as both a technical challenge and a real-world laboratory for learning customer acquisition in the internet business space. Each advertising experiment provides insights into conversion funnels, audience targeting, and cost-per-acquisition optimization.

Vision: Custom Learning Environments

The ultimate goal extends far beyond a SQL practice tool. SQLSandboxes aims to become a comprehensive, personalized SQL education platform where users can:

AI-Powered Course Creation

  • Domain-Specific Datasets: Generate realistic databases for specific industries (healthcare, finance, e-commerce, social media)
  • Custom Question Generation: AI creates practice problems tailored to user skill level and chosen domain
  • Progressive Difficulty: Automatically adjusts challenge complexity based on user performance
  • Interactive Assessments: Custom quizzes and coding challenges with instant feedback

Personalized Learning Paths

  • Skill Gap Analysis: Identify weak areas through query pattern analysis
  • Adaptive Curriculum: Dynamic learning paths that evolve based on user progress
  • Real-World Scenarios: Practice problems based on actual business use cases
  • Performance Analytics: Detailed insights into learning progression and concept mastery

Feature Development Journey

✅ Completed Features

Infrastructure & Core Functionality:

  • ✅ S3 integration with custom database loader
  • ✅ Sample database expansion (6 diverse datasets covering e-commerce, analytics, HR, etc.)
  • ✅ Comprehensive error logging system using Supabase
  • ✅ User authentication and profile management
  • ✅ Query logging and analytics pipeline
  • ✅ Feedback collection system

User Experience:

  • ✅ Random database selection for new visitors to showcase variety
  • ✅ Auto-execution with engaging default queries for first-time users
  • ✅ User acquisition tracking in registration flow
  • ✅ SEO-optimized learning content (12+ tutorial pages covering basics to advanced concepts)

Learning System:

  • ✅ Structured SQL tutorials with interactive examples
  • ✅ Deep-linking integration between tutorials and main editor
  • ✅ Progressive difficulty levels (Basics → Joins → Advanced)

🚧 In Progress & Planned Features

Core Platform Improvements:

  • Dark mode implementation
  • Custom dataset creation workflow overhaul
  • AI-powered data challenges for user engagement
  • Enhanced landing page showcasing all functionalities

Security & Performance:

  • SQL injection prevention and input sanitization
  • Schema optimization for S3 data structure
  • Performance monitoring with PostHog event tracking

AI-Powered Learning Features:

  • Custom learning environment creation
  • Domain-specific dataset generation with guided templates
  • Adaptive question generation based on user performance
  • Natural language to SQL query conversion
  • Automated assessment and progress tracking

Customer Acquisition Learning Lab

This project doubles as an intensive course in digital marketing and customer acquisition:

Channel Testing Strategy

  • Phase 1: Google Ads + Reddit Ads (Completed - 5K clicks from 30K impressions)
  • Phase 2: Meta ads, LinkedIn Ads, Twitter Ads (In Progress)
  • Phase 3: Content marketing, SEO optimization, community building

Key Learning Areas

  • Conversion Funnel Optimization: From ad click to user registration
  • Audience Segmentation: Students vs. professionals vs. interview candidates
  • Creative Testing: Ad copy, visuals, and landing page variations
  • Cost Analysis: CAC (Customer Acquisition Cost) across different channels
  • Retention Strategies: Email sequences, product-led growth tactics

Technical Challenges & Solutions

Dataset Generation Sustainability

Initial attempts at pure LLM-generated dummy data proved challenging for maintaining referential integrity across complex schemas. The solution involves:

  • Template-based generation with AI enhancement
  • Careful prompt engineering for consistent foreign key relationships
  • Hybrid approach combining structured templates with AI creativity

User Engagement Optimization

To reduce bounce rates and increase activation:

  • Random database selection showcases platform variety
  • Contextual, emoji-enhanced default queries instead of basic SELECT *
  • Auto-execution for new visitors with progressive onboarding

Performance Considerations

  • WebAssembly loading optimization for instant query execution
  • Efficient S3 data structure for fast database loading
  • Client-side caching strategies for repeated database access

Development Status & Future Roadmap

Current Phase: Foundation Building

  • Status: Pre-monetization, focusing on user experience and feature completeness
  • Priority: Perfecting the core learning experience before introducing paid features
  • Timeline: 3-6 months of feature development and user feedback integration

Future Monetization Strategy

Once the platform reaches product-market fit:

  • Freemium Model: Basic practice environments remain free
  • Premium Tiers: Advanced AI features, custom learning paths, detailed analytics
  • Enterprise/Education: Classroom management tools, progress tracking, custom curricula

Competitive Positioning

  • vs. DB Fiddle: Winner on speed-to-value. Users get populated relational databases in seconds vs. manual table creation.
  • vs. DataLemur: Winner on personalization. Custom learning environments vs. fixed problem sets.
  • vs. SQL Fiddle: Winner on user experience. Modern AI-enhanced interface vs. dated manual approach.

Long-Term Vision

SQLSandboxes represents the future of technical education: personalized, adaptive, and immediately practical. The goal is to transform SQL learning from a frustrating setup nightmare into an engaging, game-like experience where users can practice on datasets that mirror their career interests.

The platform aims to bridge the gap between academic SQL teaching and real-world database skills, preparing users not just to pass interviews, but to excel in data-driven roles across any industry.

This project showcases the intersection of modern web development, AI integration, and growth marketing. It's both a technical achievement and a business experiment, providing hands-on experience in building scalable SaaS platforms while learning the fundamentals of customer acquisition in the digital age.

Try SQL Sandboxes

Launch Application