Hey everyone 👋 we’re currently designing a data sync setup between Clay and SeaTable, but we’ve hit a structural limitation: Clay is great for enrichment, but it doesn’t natively support writing data back into an external CRM or database in a true two-way way sync.😅 So our idea is to introduce an Operational Data Store (ODS) in the middle that would: • receive enriched data from Clay via HTTP Actions or Webhooks, • expose that data via REST/SQL for SeaTable to read and write, • and continuously sync updates both ways (enrichment → CRM → re-enrichment). Setup & scale • ~20–30k rows, ~25 fields per record (Accounts, Contacts, Job Matches) • Bidirectional sync: Clay ↔️ ODS ↔️ SeaTable • Continuous enrichment and matching cycles Current plan • ODS: Postgres or Supabase (Postgres-compatible) • API: PostgREST, Hasura, or FastAPI for REST/GraphQL endpoints • Sync logic: idempotent upserts using external_id (CRM), clay_id (Clay) + timestamps for change detection Clay → ODS • Pushes via HTTP Actions/Webhooks • JSON payloads (batch or single) • Deduplication via (source, external_id, payload_hash) ODS → SeaTable • SeaTable reads via REST endpoints (with pagination + updated_since) • Writes back to ODS when changes happen on CRM side What I’d love to hear from others
Has anyone built a similar “sync hub” for Clay at this scale?
What stack did you use (Postgres + Hasura, Supabase, custom FastAPI)?
Any experience with batch sizes or rate limits for Clay’s HTTP actions?
How do you handle deduping and change detection between systems?
Any pitfalls or lessons learned around SeaTable’s API performance with 30k+ rows?
Would really appreciate examples or best practices — especially around schema design (IDs, timestamps, versioning) or retry logic for failed pushes. Would love to hear how you approached it.
