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Version: 4.4

Harper Use Cases

Harper is designed to cut out infrastructure complexity so you can move faster.
Here are some common ways developers use Harper in production today — each one showing how Harper’s architecture translates into real-world outcomes.

RESTful APIs for Distributed & Cached Data​

Great for: web apps, mobile apps, data-heavy platforms.

Harper’s most common use case is exposing distributed, cached data over a RESTful interface.
This lets you serve complex or large-scale datasets efficiently, with built-in caching and global distribution.

  • Define your schema with the graphqlSchema plugin.
  • Expose it instantly over REST using the rest plugin.
  • Take advantage of Harper’s caching layer to serve hot data without extra infrastructure.
  • Power both web and mobile applications from the same API.
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💡 Why it matters: Instead of bolting a cache or API layer onto a database, Harper gives you a unified system that scales for real-world apps.

Online Catalogs & Content Delivery​

Great for: e-commerce sites, real estate listings, media & content platforms.

Harper’s distributed architecture makes your pages load fast worldwide, improving SEO and conversion rates.

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💡 Why it matters: Instead of stitching together CDN + DB + API layers, you deliver catalog and content experiences from a single platform.

Data Delivery Networks​

Great for: live sports updates, flight tracking, software updates.

Harper combines messaging, data storage, and application logic in one system. That means:

  • Push real-time updates directly to clients.
  • Process and store data without leaving Harper.
  • Eliminate extra message brokers or caching systems.

Explore the real-time docs to see how it works.

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💡 Why it matters: You can build real-time data services in hours, not weeks, with fewer moving parts to manage.

Edge Inference Systems​

Great for: IoT pipelines, sensor networks, edge AI.

Normally, capturing and analyzing streams at the edge requires a patchwork of tools. Harper simplifies this with:

  • Self-healing connections that keep data flowing even in flaky environments.
  • The same Harper runtime running at both layers.
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💡 Why it matters: One consistent stack across edge and cloud makes AI/ML inference faster, cheaper, and easier to scale.

✅ Want to explore more? Contact us and we’ll walk you through building your own use case.