Velocity dies in the queue
Twenty teams. Three staging slots. Every PR waits while someone else finishes, or ships on contaminated shared state.
Why Guepard DataOps
The data layer built for how you ship today
You parallelized builds, preview apps, and agent runs, then serialized them behind one persistent staging database. Guepard brings approachable, developer-first DataOps back: prod-identical clones in seconds, gone when the work is done.
Why ephemeralEverything else went ephemeral.
Everything else went ephemeral.
Your database didn't.
You parallelized builds, preview apps, and agent runs, then serialized them behind one persistent staging database. That mismatch is why shipping slowed down even as your toolchain got faster.
The modern stack
- ComputeEphemeral
- ContainersEphemeral
- Preview environmentsEphemeral
- Your databaseStill persistent
Guepard closes the gap: git-like branches for data, at the speed your pipelines already expect.
CI lies to you
Pipelines run in parallel but hit the same stale DB. Migrations pass in CI, break in prod. Agents can't experiment without blocking humans.
Cost compounds quietly
Orphaned clones, oversized persistent staging, and ops tickets to provision/teardown, all because data envs weren't designed to be disposable.
Persistent staging made sense when releases were monthly. With daily deploys, agent swarms, and clone-per-PR CI, data environments have to be as disposable as the code that uses them.
How you get there
Four steps from persistent bottleneck to ephemeral by default
No migration project. No new data platform. Connect, snapshot, branch: the same mental model as git, applied to your databases.
- Connect
Connect production (read-only)
Point at Postgres, MySQL, Mongo, or any of 7 engines. Data never leaves your VPC.
- Snapshot
Snapshot once
Copy-on-write capture. Incremental, encrypted. Terabytes in seconds, not hours of pg_dump.
- Branch
Branch per PR, agent, or CI job
Every workflow gets an isolated, production-identical clone. Spin up hundreds in parallel.
- Teardown
Tear down when done
Merge, TTL, or pipeline end: environments self-destruct. Zero cleanup scripts. Zero orphaned cost.
This isn't a nice-to-have.
It's how fast teams ship now.
Ephemeral data environments aren't a feature. They're the missing layer that unlocks the parallel workflows you already built everywhere else.
6sto fork a full cloneNot days of restore windows
100+parallel branchesPer source, on demand
0shared staging queuesEvery engineer & agent self-serves
Built for builders, not gatekeepers
You don't want a six-month data platform project or a shared staging queue owned by ops. You want a precision tool: snapshot once, branch per PR or agent, tear down when done from CLI, API, or CI.
Support from people who ship data envs
When restore scripts fail at 2am, forum threads won't save your release. Guepard is built by teams who've run production DataOps, with direct access to engineers who understand branching, masking, and your engines.
Resilience you can audit
GFS is open source and battle-tested. Self-host in your VPC, keep data in-region, and prove isolation to security, not a black-box restore from last Tuesday's backup.
Compare
Traditional data environments vs. Guepard DataOps
Proof
Built to earn trust at every scale
Open-source GFS core
Copy-on-write branching engine on GitHub: inspect the primitives, extend the stack, or run fully self-hosted.
Measured in seconds
Terabyte-scale forks in under 6s. Hundreds of parallel branches without duplicating storage upfront.
Your perimeter, your rules
VPC deploy, RBAC, masking policies that travel with every branch, built for regulated teams from day one.
Startup to enterprise
Same engine for a 5-person squad and global platform teams: credits for startups, enterprise controls when you scale.
Simple to start. Intuitive to extend.
Connect production read-only, snapshot once, branch everywhere your stack runs, then let TTL and CI tear environments down for you.
