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    CONNECTIONS / Databricks / Databricks to Google Customer Match

    Your Databricks lakehouse, wired to Google Customer Match.

    Signals reads the Delta table through a Unity Catalog grant and projects your segments into Google Ads as Customer Match lists, hashed on the way out and governed like any other lakehouse job.

    Talk to our team Explore the setup

    databricks -> google-customer-match :: live

    1. ● databricks → google.match :: live
    2. > read main.audiences.segment_tier rows=50,367
    3. hash sha256(email,phone) consent=filtered
    4. route google deliver
    5. ✓ delivered · 36,768 matched

    WHAT THIS ENABLES

    Databricks to Google Customer Match

    Databricks to Google Customer Match: A notebook job reads the customer segments in your Databricks Delta tables, and Signals projects your segments into Google Ads as Customer Match lists, hashed and consent-checked each time the model rebuilds.

    • Signals projects your segments into Google Ads as Customer Match lists, reading your Databricks Delta tables each time the model rebuilds.
    • Customer Match lists that add joiners and drop lapsed members each sync, fed by data engineers modeling in Databricks notebooks.

    WHAT FLOWS WHERE

    Databricks to Customer Match, mapped.

    The notebook job streams customer segments into Signals for hashing and consent screening, then Google Ads' Customer Match API records the match-ready audiences with the serving-size threshold accounted for.

    The notebook job streams customer segments into Signals for hashing and consent screening, then Google Ads' Customer Match API records the match-ready audiences with the serving-size threshold accounted for.

    WHO THIS IS FOR

    Built for the teams that own the number.

    Growth teams feeding Google audiences from governed first-party data, with the numbers already modeled in Databricks Delta tables governed by Unity Catalog.

    Data engineers modeling in Databricks notebooks who need Customer Match lists that add joiners and drop lapsed members each sync.

    HOW DATAHASH SETS IT UP

    From kickoff to verified events.

    1. Connect

      A Unity Catalog-scoped service principal on the single Delta table in play to feed Google Ads' Customer Match API, sized for the serving-size threshold.

    2. Map

      A notebook job reads your Databricks Delta tables, and its columns map to Google Ads' Customer Match API in the visual mapper, hashed and checked for the serving-size threshold.

    3. Deliver

      Each time the model rebuilds, Signals projects your segments into Google Ads as Customer Match lists, reads only the rows the model rebuilt, with the serving-size threshold watched in the debugger.

    WHAT YOU GET

    What ships with this use case.

    Customer Match lists that add joiners and drop lapsed members each sync on every run, since Signals reads only the rows the model rebuilt.

    Lists that self-maintain across syncs, delivered each time the model rebuilds without job-cluster time climbing.

    Each run returns a match report on the serving-size threshold from Google Ads' Customer Match API, read from your Databricks Delta tables each time the model rebuilds, at flat job-cluster time.

    ON THE WIRE

    What Customer Match actually receives.

    signals :: databricks.google-customer-match :: wire

    1. ● signals :: audience upload
    2. > POST /user_list_operations source=databricks.main.audiences.segment_tier
    3. operation ADD · user_list "segment_58478"
    4. hashed_email sha256 "b02cf1…" · hashed_phone sha256 "3ad990…"
    5. members_uploaded 50,367 · members_matched 36,768
    6. ✓ accepted match=73%
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    FAQ

    Asked on almost every call.

    How often does the Databricks Google Customer Match 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, Customer Match lists that add joiners and drop lapsed members each sync keeps pace on the serving-size threshold and job-cluster time stays modest.

    What match rate should a Databricks-sourced Google Customer Match 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' Customer Match API flags the gap for growth teams feeding Google audiences from governed first-party data tracking the serving-size threshold.

    NEXT STEP

    Databricks to Google Customer Match, in production this week.

    Talk to our team All Databricks use cases