Scenario / 01
One-way update
The master system publishes changes; receivers get a mapped copy.
1Source2Change3Map4Copy
Data Ops and data teams with the same entities in CRM, sheets, catalogs, and databases
One current record across every working system
We define a source of truth per field and build exchange that does not turn version drift into new duplicates and manual checks.
01Record A
02Key
03Conflict
04Record B
OutputAgreed record
Each field has a source of truth and an update rule.
Practical outcome
Records match on keys, conflicts follow rules, and reruns stay safe
Problem
One record has several versions, the primary field is unclear, and conflicts appear from nowhere
Primary route
record A → match → conflict rule → record B
Working scenarios
How records converge without duplicates
Demonstration routes, not client results. Exact logic depends on your rules, data, and systems.
Scenario / 02
Two-way exchange
Each field has an owner; conflicts are logged and resolved by an explicit rule.
1System A2Reconcile3Rule4System B
Scenario / 03
Quality control
After each cycle we check counts, required fields, checksums, and data age.
1Load2Check3Report4Signal
Solution scope
Rules, keys, and exchange log
- Source-of-truth map
- Field mapping table
- Sync service
- Data quality report
- Conflict monitoring and log
Data and systems in the loopREST APIGoogle SheetsCSV / XMLDatabasesWebhooksCloud storage
A good fit for
What exactly we synchronize
Ops and data teams with the same entities in CRM, sheets, catalogs, and databases
Catalogs and directories
Shared products, counterparties, statuses, and other entities across work tools.
Sheets and databases
Recurring exchange between sheets, APIs, and stores without manual import overwrite.
Migration with continuity
Staged data move with quality checks and temporary sync of old and new systems.
Engagement trigger
Signs of multiple versions of truth
You need to stop manual reconciliation and define a source of truth per field
- 01Different systems hold different values for one field
- 02Import creates duplicate records
- 03The team does not know which version is current
- 04Updating large sets takes hours of manual work
Fit / boundaries
What must not go unchecked
Two-way sync starts only after every field has an owner
01Two-way sync is impossible without explicit field ownership rules
02Historical duplicates may need cleanup before launch
03Refresh rate is limited by APIs and source volume
How we launch
From a real process to a working loop
Tools are chosen after we verify inputs, exceptions, and the success criterion.
- 01
We inventory data
Entities, fields, identifiers, and current sources.
- 02
We assign owners
Source of truth and allowed change direction per field.
- 03
We lock transforms
Formats, directories, cleanup rules, and duplicate merges.
- 04
We launch safely
Dry run, discrepancy report, then write mode.
- 05
We watch freshness
Last successful cycle, errors, gaps, and conflicts.
How is sync different from API integration?+
API integration connects actions and events. Sync keeps datasets aligned over time — conflicts, duplicates, and freshness.
Can sync run both ways?+
Yes if every field has a source of truth and a conflict rule. Without that, two-way sync damages data quickly.
How does the first launch work?+
Read-only discrepancy report first. After review: limited write run, then regular mode.
What about existing duplicates?+
Define match keys, auto-merge unambiguous rows, and park disputed ones for manual review.
How is freshness controlled?+
We track successful update time, processed volume, lag, and errors per system.
First step
Map a sync design
We'll map inputs, exceptions, and constraints. You leave with a priority scenario and a next step — no obligation to start a project.
Map a sync designService: Data synchronization