Prevent large-query loss in web SQL editors — autosave, chunking & versioning

Executive Summary

Data engineers, analytics engineers and analysts at roughly 3.0M teams increasingly author and modify multi-hundred-line, generated SQL in web editors, and accidental refreshes, crashes or limited editor buffers routinely cost hours of work and introduce operational risk; that persistent pain maps to an approximate $9.0B addressable market (3.0M teams × $3K ACV) and a market score of 88/100. Teams that run complex queries and rely on collaborative workflows are the most exposed because recovery is slow and manual backups are often incomplete or inaccessible when you need them. You could build a web SQL editor engineered around continuous autosave, intelligent chunking of large queries, and statement-level versioning: sub-2s autosave checkpoints during active editing, chunk rendering to keep client state under ~10k characters per pane, and a time-ordered diff/restore UI that supports statement-level undo and branching. Add server-side encrypted backups (S3/GCS), an API for DB drivers and CI integration, and AI-assisted diffs and intent-aware merge resolution to make recovery and collaboration fast and comprehensible rather than cryptic line-by-line merges. This is attractive now because cloud databases and analytics platforms are growing, IDE consolidation pressures favor integrated editors+backups, and recent LLM advances make intelligent diffs and merge-resolution practical; the revenue potential score of 84/100 reflects a realistic path to recurring high-ACV deals. To stand out you must combine low-latency UX for very long queries, deep dialect-aware DB integrations and enterprise-grade audit/compliance; the honest challenges are broad dialect support, secure credential handling and earning trust versus embedded editors, but solving those creates a defensible product that meaningfully reduces costly query-loss incidents.