CONNECTIONS / Databricks / Databricks to Snap Offline CAPI
Your Databricks lakehouse, wired to Snapchat in-store conversions.
Signals reads the Delta table through a Unity Catalog grant and reports in-store purchases against the Snapchat campaigns that reached the buyer, hashed on the way out and governed like any other lakehouse job.
- ● databricks → snap.capi :: live
- > read main.sales.pos_ledger rows=51,657
- hash sha256(email,phone) consent=filtered
- route snap deliver
- ✓ delivered · 43,392 matched
WHAT THIS ENABLES
Databricks to Snapchat Offline CAPI
Databricks to Snapchat Offline CAPI: A notebook job reads the offline and in-store sales in your Databricks Delta tables, and Signals reports in-store purchases against the Snapchat campaigns that reached the buyer, hashed and consent-checked each time the model rebuilds.
- Signals reports in-store purchases against the Snapchat campaigns that reached the buyer, reading your Databricks Delta tables each time the model rebuilds.
- Physical-world sales counted inside Snapchat performance, fed by data engineers modeling in Databricks notebooks.
The click, the conversion, and the credit.
The conversion happens off Snap, away from any pixel. Here is how Databricks closes the loop.
Built for the teams that own the number.
Data engineers modeling in Databricks notebooks who need physical-world sales counted inside Snapchat performance.
From kickoff to verified events.
-
Connect
A Unity Catalog-scoped service principal on the single Delta table in play to feed Snapchat's Conversions API, sized for offline event timing.
-
Map
A notebook job reads your Databricks Delta tables, and its columns map to Snapchat's Conversions API in the visual mapper, hashed and checked for offline event timing.
-
Deliver
Each time the model rebuilds, Signals reports in-store purchases against the Snapchat campaigns that reached the buyer, reads only the rows the model rebuilt, with offline event timing watched in the debugger.
What changes when the CSV goes away.
| Capability | Manual CSV upload | Datahash |
|---|---|---|
| Reporting | Offline sales sit in a separate export, reconciled by hand. | Revenue counted in the same Offline CAPI reporting as web conversions. |
| Deduplication | A re-uploaded file risks counting the same conversion twice. | Deduped delivery, so a resent record never counts as a second conversion. |
| Match visibility | Match quality is a guess until the numbers look off. | Match rate reported per upload, tied to the source event set that produced it. |
| Effort and latency | An analyst exports and uploads on a manual cadence. | Server-side and automatic the moment Databricks records the event. |
What Offline CAPI actually receives.
- ● signals :: event payload
- > POST /v2/conversion source=databricks.main.sales.pos_ledger
- event_type "PURCHASE" · event_conversion_type "OFFLINE"
- hashed_email "5d8b22…" · hashed_phone_number "a179fe…"
- timestamp 1784522842 · price 25343 · currency GBP
- ✓ accepted · match=true
Adjacent moves on the same stack.
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Snap Offline CAPI
Deliver offline conversions to Snap on a schedule that fits your operations.
Learn moreAsked on almost every call.
How often does the Databricks Snap Offline CAPI sync run?
Cadence follows the job that rebuilds the table: a nightly model gives Snapchat a nightly feed, an hourly one an hourly feed. Because a run picks up only the rows the model rebuilt, physical-world sales counted inside Snapchat performance keeps pace on offline event timing and job-cluster time stays modest.
What match rate should a Databricks-sourced Snap Offline CAPI batch expect?
Identifier quality in the Delta table drives it, not the cluster size. Rows carrying a live hashed email or phone land; rows with neither will not, and no rebuild changes that. The opening run's feedback from Snapchat's Conversions API flags the gap for retail and QSR advertisers whose Snapchat audience buys in person tracking offline event timing.
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