CONNECTIONS / Databricks / Databricks to TikTok Offline eAPI
Your Databricks lakehouse, wired to TikTok's Offline Events API.
Signals reads the Delta table through a Unity Catalog grant and sends register and phone sales so offline joins TikTok's web optimization, hashed on the way out and governed like any other lakehouse job.
- ● databricks → tiktok.eapi :: live
- > read main.sales.pos_ledger rows=18,685
- hash sha256(email,phone) consent=filtered
- route tiktok deliver
- ✓ delivered · 16,443 matched
WHAT THIS ENABLES
Databricks to TikTok Offline Events API
Databricks to TikTok Offline Events API: A notebook job reads the offline and in-store sales in your Databricks Delta tables, and Signals sends register and phone sales so offline joins TikTok's web optimization, hashed and consent-checked each time the model rebuilds.
- Signals sends register and phone sales so offline joins TikTok's web optimization, reading your Databricks Delta tables each time the model rebuilds.
- Counter sales deduped against any TikTok web event for the same buyer, fed by data engineers modeling in Databricks notebooks.
The click, the conversion, and the credit.
The conversion happens off TikTok, 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 counter sales deduped against any TikTok web event for the same buyer.
From kickoff to verified events.
-
Connect
A Unity Catalog-scoped service principal on the single Delta table in play to feed TikTok's Offline Events API, sized for web-to-offline deduplication.
-
Map
A notebook job reads your Databricks Delta tables, and its columns map to TikTok's Offline Events API in the visual mapper, hashed and checked for web-to-offline deduplication.
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Deliver
Each time the model rebuilds, Signals sends register and phone sales so offline joins TikTok's web optimization, reads only the rows the model rebuilt, with web-to-offline deduplication 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 eAPI 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 eAPI actually receives.
- ● signals :: event payload
- > POST /event/track source=databricks.main.sales.pos_ledger
- event "CompletePayment" · event_time 1784520302
- user.email sha256 "c02fa8…" · user.phone sha256 "6b91dd…"
- properties.value 32937 · properties.currency GBP
- ✓ accepted · dedupe=event_id
Adjacent moves on the same stack.
from Databricks
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TikTok Offline eAPI
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How often does the Databricks TikTok Offline eAPI sync run?
Cadence follows the job that rebuilds the table: a nightly model gives TikTok a nightly feed, an hourly one an hourly feed. Because a run picks up only the rows the model rebuilt, counter sales deduped against any TikTok web event for the same buyer keeps pace on web-to-offline deduplication and job-cluster time stays modest.
What match rate should a Databricks-sourced TikTok Offline eAPI 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 TikTok's Offline Events API flags the gap for brands whose TikTok conversions finish at a till, not a checkout tracking web-to-offline deduplication.
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