Case study: Automated contact and campaign data sync between GuruDB and EnumHQ using Python and Google Apps Script.
Contact records and campaign data existed in two separate systems — GuruDB and EnumHQ — with no synchronization between them. This led to mismatched audience data, duplicate outreach, and inconsistent campaign results. Manual reconciliation was periodic, error-prone, and time-consuming.
Implement a reliable, automated sync layer that keeps contact and campaign data consistent between the two platforms in near real-time, with conflict resolution logic when records diverge.
| Layer | Tools |
|---|---|
| Scripting | Python, Google Apps Script |
| Source system | GuruDB |
| Target system | EnumHQ |
| Scheduling | Trigger-based (time-driven, Apps Script) |
| Data format | JSON via REST APIs |
[GuruDB — Source of truth]
↓
[Python extract script]
↓
[Data validation + conflict detection]
↓
[Google Apps Script — orchestration layer]
↓
[EnumHQ API — write / update records]
↓
[Error log + notification on failures]
- Extract — Python script pulls updated records from GuruDB via API, filtered by modification timestamp
- Transform — Field mapping converts GuruDB schema to EnumHQ-compatible format; normalizes phone numbers and email casing
- Conflict detection — Compares last-modified timestamps; newer record wins; ambiguous conflicts flagged for review
- Load — Google Apps Script handles the write to EnumHQ API with retry logic on rate limit errors
- Error handling — Failed records logged to a Google Sheet for manual review; notification triggered on batch failures
- Scheduling — Apps Script time-driven trigger runs sync on a regular cadence
- Actual API credentials and endpoint URLs
- Customer data and PII
- Proprietary field structures and business logic specifics
- Client-specific campaign configurations
- Consistent contact data across both platforms — no more manual reconciliation
- Reduced campaign errors caused by stale or mismatched audience data
- Error logging gave visibility into data quality issues that previously went unnoticed
- Eliminated a recurring manual task from the operations workflow
- Timestamp-based conflict resolution works well but requires both systems to use UTC — timezone mismatches cause subtle sync failures
- Google Apps Script execution quotas (6 min/run) shape how batch sizes are structured; chunking is essential for large record sets
- REST APIs with rate limits need exponential backoff, not simple retry — naive retries amplify the rate limit problem
- A lightweight logging layer (even a Google Sheet) dramatically reduces time spent debugging sync failures
[Add sanitized architecture diagram and workflow screenshots here — no PII or credentials visible]
Sumit Bansal — sumit.mft@gmail.com | LinkedIn