CONNECTIONS / Databricks / Databricks to Google EC for Leads
Your Databricks lakehouse, wired to Google Enhanced Conversions for Leads.
Signals reads the Delta table through a Unity Catalog grant and sends down-funnel lead milestones matched to the originating Google click, hashed on the way out and governed like any other lakehouse job.
- ● databricks → google.leads :: live
- > read main.crm.pipeline_stages rows=13,343
- hash sha256(email,phone) consent=filtered
- route google deliver
- ✓ delivered · 10,007 matched
WHAT THIS ENABLES
Databricks to Google EC for Leads
Databricks to Google EC for Leads: A notebook job reads the qualified-lead and pipeline signals in your Databricks Delta tables, and Signals sends down-funnel lead milestones matched to the originating Google click, hashed and consent-checked each time the model rebuilds.
- Signals sends down-funnel lead milestones matched to the originating Google click, reading your Databricks Delta tables each time the model rebuilds.
- Lead-value bidding fed by the stages your pipeline actually reaches, fed by data engineers modeling in Databricks notebooks.
WHAT FLOWS WHERE
Databricks to EC for Leads, mapped.
The notebook job streams qualified-lead and pipeline signals into Signals for hashing and consent screening, then Google Ads' Enhanced Conversions API records the lead conversions with GCLID and hashed-email matching accounted for.
The notebook job streams qualified-lead and pipeline signals into Signals for hashing and consent screening, then Google Ads' Enhanced Conversions API records the lead conversions with GCLID and hashed-email matching accounted for.
Built for the teams that own the number.
Data engineers modeling in Databricks notebooks who need lead-value bidding fed by the stages your pipeline actually reaches.
From kickoff to verified events.
-
Connect
A Unity Catalog-scoped service principal on the single Delta table in play to feed Google Ads' Enhanced Conversions API, sized for GCLID and hashed-email matching.
-
Map
A notebook job reads your Databricks Delta tables, and its columns map to Google Ads' Enhanced Conversions API in the visual mapper, hashed and checked for GCLID and hashed-email matching.
-
Deliver
Each time the model rebuilds, Signals sends down-funnel lead milestones matched to the originating Google click, reads only the rows the model rebuilt, with GCLID and hashed-email matching watched in the debugger.
What ships with this use case.
Lead-value bidding fed by the stages your pipeline actually reaches on every run, since Signals reads only the rows the model rebuilt.
Lead-value signals matched to the paid click, delivered each time the model rebuilds without job-cluster time climbing.
Each run returns a match report on GCLID and hashed-email matching from Google Ads' Enhanced Conversions API, read from your Databricks Delta tables each time the model rebuilds, at flat job-cluster time.
What EC for Leads actually receives.
- ● signals :: event payload
- > POST /conversion_action source=databricks.main.crm.pipeline_stages
- gclid "Cj0KCQjw…" · conversion_action "sql_reached"
- conversion_value 7 · currency GBP
- hashed_email sha256 "9c41af…"
- ✓ accepted match=gclid+hashed_email
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How often does the Databricks Google EC for Leads sync run?
Cadence follows the job that rebuilds the table: a nightly model gives Google Ads a nightly feed, an hourly one an hourly feed. Because a run picks up only the rows the model rebuilt, lead-value bidding fed by the stages your pipeline actually reaches keeps pace on GCLID and hashed-email matching and job-cluster time stays modest.
What match rate should a Databricks-sourced Google EC for Leads 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 Google Ads' Enhanced Conversions API flags the gap for Google lead-gen advertisers bidding past the first form submit tracking GCLID and hashed-email matching.
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