SQLSandboxes project In Progress

SQLSandboxes: An AI-Powered SQL Learning Platform

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. Users can write queries and see instant results without installing anything.

Core Features

Technical Architecture

Core Stack

• Frontend: Next.js 15 with App Router, React 19, TypeScript
• Database Engine: DuckDB via WebAssembly for client-side execution with superior performance
• Backend: Supabase for user management, authentication, and analytics
• Storage: AWS S3 for sample database definitions and custom datasets
• Styling: Tailwind CSS 4 with custom component library
• AI Integration: OpenAI API for intelligent dataset generation and query assistance
• Hosting: Vercel for frontend deployment with edge optimization

Key Architectural Decisions

Client-Side SQL Execution with DuckDB: Migrated from SQL.js to DuckDB WebAssembly for superior performance and PostgreSQL-compatible syntax. This eliminates server load while providing instant query feedback. Users can execute complex queries with joins, window functions, CTEs, and advanced analytical functions without any backend processing.

Hybrid Storage Strategy: Sample databases stored as optimized 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 and cost efficiency.

Progressive Authentication: Guest users can explore the platform immediately before signup. Authenticated users unlock unlimited queries, custom dataset creation, and access to premium features including case studies and interview preparation content.

Growth & Market Validation

SQLSandboxes has demonstrated strong product-market fit with rapid early adoption and engagement metrics that validate the core value proposition.

Platform Metrics

• 511+ Registered Users: Strong signup conversion indicating clear value proposition
• Approximately 200 Daily Queries: Consistent platform engagement across user base
• 2,525 Distinct Visitors: Organic and paid traffic in initial launch phase
• 3,144 Total Page Views: Users exploring multiple features and tutorials

Marketing Experiments

The platform serves as a comprehensive learning laboratory for customer acquisition strategies, testing multiple advertising channels with measurable outcomes.

• Google Ads: 30,000+ impressions with 5,000+ clicks targeting SQL practice and learning keywords. Strong performance on high-intent search terms like "practice sql online" and "sql interview prep".
• Reddit Ads: Effective reach in r/learnprogramming, r/SQL, and r/datascience communities with engagement from students and professionals.
• Content Marketing: 18+ SEO-optimized tutorial pages driving organic traffic for long-tail keywords.
• Planned Expansion: Meta ads, LinkedIn Ads, and strategic partnerships with bootcamps and educational platforms.

Each channel provides insights into conversion funnels, audience segmentation, cost-per-acquisition optimization, and long-term user retention strategies.

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 for scalable dataset storage
• Sample database expansion to 10+ diverse datasets covering e-commerce, analytics, HR, finance, and social media
• Comprehensive error logging system using Supabase for production monitoring
• User authentication and profile management with email verification
• Query logging and analytics pipeline tracking user behavior and performance
• Feedback collection system integrated across platform touchpoints
• Migration to DuckDB WebAssembly for enhanced performance
• Google OAuth integration for streamlined signup process

User Experience:

• Random database selection for new visitors to showcase platform variety
• Auto-execution with contextually engaging default queries for first-time users
• User acquisition tracking in registration flow to identify best marketing channels
• SEO-optimized learning content with 18+ tutorial pages covering basics to advanced concepts
• Dark mode implementation for improved user experience
• Locale tracking in query logs to understand geographic distribution

Learning System:

• Structured SQL tutorials with interactive, executable examples
• Deep-linking integration between tutorials and main SQL editor
• Progressive difficulty levels spanning Basics, Joins, Aggregations, and Advanced topics
• AI-powered data challenges for custom dataset engagement
• Case studies featuring real business scenarios from major tech companies
• Interview preparation content with company-specific questions and solutions

In Progress & Planned Features

Core Platform Improvements:

• Custom dataset creation workflow overhaul with improved UX
• Template-based dataset generation to guide users toward realistic schemas
• Enhanced landing pages showcasing all functionalities with A/B testing
• Premium pricing modal integration for feature discovery
• Achievement system and gamification elements for user retention

Security & Performance:

• SQL injection prevention and comprehensive input sanitization
• Schema optimization for S3 data structure with YAML migration
• Performance monitoring with PostHog event tracking and analytics
• AI-powered penetration testing bots for automated security assessment
• Query performance analyzer to identify bottlenecks and optimization opportunities

AI-Powered Learning Features:

• Custom learning environment creation with AI-generated curricula
• Domain-specific dataset generation with industry-focused templates
• Adaptive question generation based on user performance and skill gaps
• Natural language to SQL query conversion for accessibility
• Automated assessment and detailed progress tracking with analytics dashboard
• AI-powered query optimization suggestions and explanations

Platform Expansion:

• CSV and JSON dataset upload functionality for custom data
• Sharing capabilities for collaborative learning and query exchange
• Portuguese language translation for Brazilian market expansion
• Performance comparison tools for different join strategies and query approaches
• Real-time transaction data simulator for advanced analytical practice

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, engaging default queries demonstrating real analytical value instead of basic SELECT statements
• Auto-execution for new visitors with progressive onboarding

Performance Considerations

• DuckDB WebAssembly Optimization: Lazy loading and streaming compilation for instant query execution
• Efficient S3 Data Structure: Optimized JSON schemas with YAML migration planned for reduced file sizes
• Client-Side Caching: Smart caching strategies for repeated database access and query history
• Edge Deployment: Vercel edge functions for global low-latency access

Current Challenges & Solutions

User Retention & Engagement

Data analysis revealed that only 63 out of 153 registered users returned after initial signup, with just 8 users demonstrating consistent multi-session engagement. This activation challenge drives several product development priorities.

Implemented Solutions:

• Random Database Selection: New visitors see varied datasets on each visit, showcasing platform diversity
• Contextual Default Queries: Engaging, pre-written queries that demonstrate real analytical value instead of basic SELECT statements
• Auto-Execution: First-time users see immediate results, reducing friction to first value
• Progressive Onboarding: Guided tours introducing features incrementally rather than overwhelming users

Planned Improvements:

• AI-powered data challenges to maintain engagement with custom datasets
• Gamification elements including achievements and skill progression tracking
• Email nurture campaigns with personalized learning content
• Session-triggered feedback modals to gather user insights

Development Status & Roadmap

Current Phase: Growth Optimization

• Status: Pre-monetization, focusing on reducing error rates and expanding premium functionality
• Priority: Achieving near-zero daily error rate before introducing paid tiers
• Focus: User retention improvements and feature completion based on user feedback

Planned Monetization Approach

The platform will implement a freemium model with tiered access once core metrics stabilize and error rates approach zero.

Free Tier:

• Access to core SQL editor with 10+ sample databases
• Basic tutorials and learning resources
• Limited daily query execution

Premium Individual ($9-19/month):

• Unlimited AI-powered database generation
• Full access to case studies and interview preparation content
• Advanced AI features including natural language to SQL conversion
• Detailed query performance analytics and optimization suggestions
• Unlimited queries and custom dataset storage

Team/Enterprise ($40-99/month):

• Classroom management and progress tracking tools
• Custom curriculum creation for bootcamps and corporate training
• Multi-user collaboration on datasets and queries
• Advanced analytics dashboard for instructors

Competitive Positioning

SQLSandboxes differentiates through a combination of modern technology, AI-powered features, and comprehensive learning content.

• vs. DB Fiddle: Wins on speed-to-value and user experience. Users get fully populated, realistic relational databases in seconds versus manual CREATE TABLE statements. Modern interface with AI-enhanced features versus dated functionality.
• vs. DataLemur: Wins on flexibility and personalization. Custom learning environments with AI-generated datasets versus fixed problem sets. Users can practice on domains relevant to their career interests.
• vs. SQL Fiddle: Wins on technology and features. DuckDB WebAssembly execution, modern React interface, and AI integration versus legacy architecture. Comprehensive learning system versus basic query execution.
• vs. StrataScratch: More accessible pricing model and broader feature set. Focus on learning journey from beginner to expert versus primarily interview-prep oriented.

Long-Term Vision

SQLSandboxes aims to transform SQL education from a frustrating setup nightmare into an engaging, immediately practical learning experience. The platform bridges 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.

The ultimate goal is to create a personalized, adaptive learning platform where users practice on datasets that mirror their specific career interests. Through AI-powered course generation, custom learning paths, and real-world scenario training, SQLSandboxes represents the future of technical education.