Insights
Deep dives, architecture notes, and product guidance from the hypequery team.
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Steal the exact query patterns, cache plays, and architecture shortcuts behind sub-second analytics. 5 minute read, every Friday.
We've made our databases real-time. We haven't made our analytics interfaces real-time-safe. The missing abstraction between ClickHouse and your consumers — human or machine — is a typed, programmable analytics language layer.
Go from raw SQL strings to a fully typed SDK + HTTP API + React hooks in about 5 minutes. Learn how to auto-generate TypeScript types from your ClickHouse schema and never worry about schema drift again.
Production patterns, performance pitfalls, and when to use something else. Learn how ClickHouse materialized views really work, how to choose the right engine, and when alternative patterns like projections make more sense.
Uber, Cloudflare, Instacart, GitLab, Lyft, Microsoft, and Contentsquare independently built near identical abstraction layers on top of ClickHouse. The shared architecture isn't coincidence, it is forced by the economics of making high performance schemas accessible at organisational scale.
Learn how to solve schema evolution challenges in ClickHouse with automated type generation, compile-time safety, and best practices for keeping your analytics applications resilient as your data grows.
Learn how to build type-safe, real-time analytics dashboards using hypequery, ClickHouse, and Next.js. Get complete type safety, autocomplete, and developer productivity for your analytics applications.
Developer productivity suffers when SQL queries break at runtime instead of compile time. Type-safe SQL in TypeScript isn't just a nice-to-have, it's essential for building reliable applications. This guide explores why traditional approaches fall short and shows you how modern solutions like hypequery can transform your development workflow.