Bringing Data Closer to Your Code: A Real Implementation
We keep saying it: “Bring your data closer to your code.”
It sounds obvious — who wouldn’t want faster response times, smoother offline experiences, and more resilient apps? But for many teams, it stays theoretical. The data sits locked away in a single region or a central database, while your code tries to serve users on the other side of the world in milliseconds.
It doesn’t have to stay that way.
This is a practical look at how to actually bring your data closer — not just geographically, but logically, operationally, and in your day-to-day developer workflow.
Why Bring Data Closer?
When you close the distance between your data and your code, good things happen:
🚀 Faster User Experience
Data round-trips shrink dramatically. Instead of crossing oceans to fetch a user profile or shopping cart, you grab it from a nearby edge replica or a local cache.
📱 Better Offline and Edge Behavior
If your central database is unreachable or your user has spotty connectivity, local-first patterns keep things working. Think drafts that save on the client and sync later — or multiplayer apps that resolve edits when someone reconnects.
⚡ Quicker Developer Feedback
Data that’s easy to spin up locally speeds up development. You can run production-like scenarios without needing a staging database or a full CI pipeline for every test.
🛡️ More Resilience
If one region goes down, not everything does. Edge replicas and smart caches add natural redundancy.
The result: snappier apps, smoother user experience, and less stress when your infrastructure hiccups.
What Does ‘Closer’ Actually Mean?
The physical meaning is obvious — data that’s geographically closer to your users. But there’s more to it.
🌍 Physical Proximity
Your database or replicas live in data centers near your users. CDNs made this normal for static assets — now, it’s increasingly normal for dynamic data too.
🧠 Logical Proximity
Your app keeps relevant data near the logic that uses it. If your function needs the same config or user session data a thousand times, don’t ping the origin — keep it cached nearby or embedded in the edge function’s environment.
💻 Developer Proximity
You can run and seed real data on your laptop. Developers shouldn’t be blocked because “the staging database is down.” Local-first design also means your tests run faster and CI pipelines are simpler.
🎯 Ownership Proximity
Your data logic travels with your deployable units. An edge function isn’t much use if it still calls back to a single-region database for every request. Ideally, your data’s distribution matches your code’s distribution.
Patterns That Make This Work
Edge Databases
Modern edge databases let you spin up read replicas, deploy tiny SQLite instances, or stream writes back to a central store. For read-heavy workloads, this is a huge win — user profiles, blog content, product catalogs — all benefit from being served near the user.
Examples:
- PlanetScale — MySQL-compatible, branching, global reads with automatic failover.
- Turso — Edge-deployed SQLite that’s lightweight, fast to spin up, and naturally distributed.
- Neon — Serverless Postgres with built-in branching and on-demand scaling.
- Supabase — Open source Firebase alternative with real-time capabilities and edge functions.
Local-First and Offline-First Stores
If your data is mostly user-specific — drafts, comments, ephemeral states — it can often live safely on the device and sync when possible. This is the heart of local-first design.
Examples:
- PouchDB + CouchDB — The classic combo: the browser keeps a local copy that syncs with Couch when online.
- ElectricSQL — Postgres you can embed in the client, with CRDT conflict resolution under the hood.
- Replicache — Great for collaborative, real-time apps. Users get immediate feedback, and the sync engine handles merge logic.
- Automerge — CRDT library for building collaborative applications.
Pre-Rendering and Edge Caching
Sometimes the simplest path is to not hit a database at all. If your data doesn’t change every second, consider pre-rendering it and caching it at the edge.
Examples:
- Next.js Incremental Static Regeneration — Pre-render pages at build time, then update them as needed.
- Cloudflare Pages + KV — Store JSON blobs at the edge, update them via workers.
- Astro Static Site Generation — Pre-build content at build time for maximum performance.
Data Sync Workers
You won’t get far without thinking about data freshness. Workers that sync edge writes back to your origin — or sync offline edits from client to server — are a big part of the puzzle.
Patterns like conflict-free replicated data types (CRDTs) help make sure edits from multiple sources merge predictably. This is how collaborative editors like Figma or Notion handle multiple users editing the same thing.
Durable Objects and Co-Located State
Some workloads need sticky state that’s close to your user and doesn’t bounce between servers. Durable Objects (like Cloudflare’s implementation) give you per-session or per-object state that runs near the client.
A good fit: chat sessions, multiplayer game state, collaborative editing, or real-time dashboards.
Your New Toolkit
This ecosystem is growing fast. Here’s what teams are adopting:
Edge Compute
- Vercel Edge Functions — Lightweight serverless runtimes deployed globally
- Cloudflare Workers — Edge computing platform with global deployment
- AWS Lambda@Edge — Lambda functions that run at CloudFront edge locations
- Deno Deploy — Global edge deployment for Deno applications
Edge Storage
- Cloudflare KV — Key-value storage at the edge
- Cloudflare D1 — SQLite at the edge
- Turso — Distributed SQLite
- Upstash Redis — Global Redis API
- PlanetScale — Global MySQL-compatible database
Sync Engines
- Automerge — CRDT library for building collaborative applications
- Yjs — CRDT framework for building collaborative applications
- ElectricSQL — Local-first sync for Postgres
CDN-Backed APIs
Static or semi-static APIs served globally via Fastly, CloudFront, or Akamai.
These tools play nicely with modern frameworks — and many are pay-as-you-go, so you can experiment without huge upfront cost.
What Can Go Wrong?
Bringing data closer comes with trade-offs. Be aware of the common pitfalls:
⚖️ Consistency vs. Performance
Replication across regions means you can’t always guarantee instant consistency. You’ll need to decide where strong consistency matters and where eventual consistency is fine.
🔄 Conflict Resolution
If users can edit the same record from different places, you need a strategy to merge changes predictably. This is where CRDTs, version vectors, and timestamp-based conflict resolution come in.
💰 Cost
More replicas, more global reads, more egress traffic — it all adds up. Keep an eye on costs, especially when your app scales globally.
🛠️ Tooling Maturity
Many edge-focused databases are new. Some frameworks assume single-region backends. Make sure your ORM or data layer supports the edge pattern you want.
How to Get Started
You don’t need to re-architect everything on day one. Start with a slice:
- Put read-heavy, low-write data on an edge replica — user profiles, static settings, or public content
- Cache static JSON at the CDN edge — use Cloudflare KV or similar
- Try local-first for drafts, carts, or comments — anything that makes sense offline
- Wrap it in a feature flag and ship incrementally — use tools like LaunchDarkly or Split
For development, keep it frictionless: run Docker containers with seeded test data, or use SQLite locally to mimic your prod schema.
Bringing It All Together
Bringing data closer to your code used to mean spinning up expensive replicas and fighting complex sync pipelines. Today, edge compute, distributed databases, and local-first frameworks make it practical — and surprisingly accessible — for everyday teams.
Start small. Pick a piece of your stack. Move it closer. Your app will get faster, your infrastructure will get more resilient, and your development loops will get smoother.
If you’re experimenting with this, I’d love to hear what’s working for you — drop me a note here or ping me on Twitter.
Happy coding,
Pedro