CONNECTIONS / Databricks / Databricks to Snap Custom Audience
Your Databricks lakehouse, wired to Snapchat audience matching.
Signals reads the Delta table through a Unity Catalog grant and matches your segments into Snapchat for targeting and customer suppression, hashed on the way out and governed like any other lakehouse job.
- ● databricks → snap.audience :: live
- > read main.audiences.segment_tier rows=51,308
- hash sha256(email,phone) consent=filtered
- route snap deliver
- ✓ delivered · 46,177 matched
WHAT THIS ENABLES
Databricks to Snapchat Custom Audience
Databricks to Snapchat Custom Audience: A notebook job reads the customer segments in your Databricks Delta tables, and Signals matches your segments into Snapchat for targeting and customer suppression, hashed and consent-checked each time the model rebuilds.
- Signals matches your segments into Snapchat for targeting and customer suppression, reading your Databricks Delta tables each time the model rebuilds.
- Snapchat audiences that stay in step as the segment rebuilds, fed by data engineers modeling in Databricks notebooks.
WHAT FLOWS WHERE
Databricks to Custom Audience, mapped.
The notebook job streams customer segments into Signals for hashing and consent screening, then Snapchat's Custom Audiences API records the match-ready audiences with list sizing on Snapchat accounted for.
The notebook job streams customer segments into Signals for hashing and consent screening, then Snapchat's Custom Audiences API records the match-ready audiences with list sizing on Snapchat accounted for.
Built for the teams that own the number.
Data engineers modeling in Databricks notebooks who need Snapchat audiences that stay in step as the segment rebuilds.
From kickoff to verified events.
-
Connect
A Unity Catalog-scoped service principal on the single Delta table in play to feed Snapchat's Custom Audiences API, sized for list sizing on Snapchat.
-
Map
A notebook job reads your Databricks Delta tables, and its columns map to Snapchat's Custom Audiences API in the visual mapper, hashed and checked for list sizing on Snapchat.
-
Deliver
Each time the model rebuilds, Signals matches your segments into Snapchat for targeting and customer suppression, reads only the rows the model rebuilt, with list sizing on Snapchat watched in the debugger.
What ships with this use case.
Snapchat audiences that stay in step as the segment rebuilds on every run, since Signals reads only the rows the model rebuilt.
Audiences that rebuild as the segment does, delivered each time the model rebuilds without job-cluster time climbing.
Each run returns a match report on list sizing on Snapchat from Snapchat's Custom Audiences API, read from your Databricks Delta tables each time the model rebuilds, at flat job-cluster time.
What Custom Audience actually receives.
- ● signals :: audience upload
- > POST /audience_match source=databricks.main.audiences.segment_tier
- schema ["EMAIL_SHA256","PHONE_SHA256"]
- external_audience_id "aud_97241"
- id_list_size 51,308 · matched 46,177
- ✓ accepted match=90%
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How often does the Databricks Snap Custom Audience 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, Snapchat audiences that stay in step as the segment rebuilds keeps pace on list sizing on Snapchat and job-cluster time stays modest.
What match rate should a Databricks-sourced Snap Custom Audience 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 Custom Audiences API flags the gap for Snapchat advertisers building lookalikes and excluding current buyers tracking list sizing on Snapchat.
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