What 'keeping data in sync' really means
Sync sounds simple until two systems disagree. The real work is deciding what happens when the same record is edited in both places, making sure updates don't get lost, and guaranteeing a record created in one system shows up correctly in the other.
- One-way sync (system A is the source, B mirrors it)
- Two-way sync (both systems can change data and stay reconciled)
- Real-time updates vs. scheduled batch syncs
- Matching records across systems by a stable key
- Handling creates, updates, and deletes consistently
The hard problems we solve
Anyone can copy a row once. Keeping two systems honest over time is where sync projects succeed or fail — conflict resolution, deduplication, and making the process idempotent so re-running it never corrupts your data.
- Conflict resolution rules: last-write-wins, source-of-truth, or field-level merge
- Deduplication so the same entity doesn't split into multiple records
- Idempotency so a retry or replay can't create duplicates
- Delta detection to sync only what changed, not everything every time
- Ordering and dependencies (create the customer before their order)
- Handling deletes safely instead of resurrecting old records
Making it trustworthy
A sync you can't see into is a sync you can't trust. We add logging, monitoring, and reconciliation so you know it's working — and get alerted when it isn't — instead of discovering a silent gap weeks later.
- Logging of every synced record and every failure
- Alerts when a sync stalls or errors instead of failing silently
- A reconciliation check that flags records out of agreement
- A backfill to bring existing data into alignment at launch
- Clear ownership: which system wins for each field
More on apis & integrations
Frequently asked questions
What happens if the same record changes in both systems at once?
That's the core question of two-way sync, and we answer it explicitly during scoping. Depending on the data we use last-write-wins, a designated source of truth, or field-level merging — so the outcome is a deliberate rule, not a coin flip that overwrites good data.
Real-time or nightly batch — which should we use?
It depends on how fresh the data needs to be and the systems' limits. Real-time via webhooks or events is great when minutes matter; nightly batch is simpler, cheaper, and fine for reporting-style data. We match the approach to the actual business need.
How do I know the sync is actually working?
We build in logging, failure alerts, and a reconciliation check that compares both systems and flags mismatches. You shouldn't have to trust it blindly — you get visibility, and you get told when something needs attention.