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    CONNECTIONS / Databricks / Databricks to Google Store Sales

    Your Databricks lakehouse, wired to Google Store Sales matching.

    Signals reads the Delta table through a Unity Catalog grant and matches in-person transactions to Google accounts as store-sales conversions, hashed on the way out and governed like any other lakehouse job.

    Talk to our team Explore the setup

    databricks -> google-store-sales :: live

    1. ● databricks → google.sales :: live
    2. > read main.sales.pos_ledger rows=35,421
    3. hash sha256(email,phone) consent=filtered
    4. route google deliver
    5. ✓ delivered · 29,754 matched

    WHAT THIS ENABLES

    Databricks to Google Store Sales

    Databricks to Google Store Sales: A notebook job reads the offline and in-store sales in your Databricks Delta tables, and Signals matches in-person transactions to Google accounts as store-sales conversions, hashed and consent-checked each time the model rebuilds.

    • Signals matches in-person transactions to Google accounts as store-sales conversions, reading your Databricks Delta tables each time the model rebuilds.
    • Counter and showroom revenue inside the same ROAS view as online orders, fed by data engineers modeling in Databricks notebooks.
    WHAT FLOWS WHERE

    The click, the conversion, and the credit.

    The conversion happens off Google, away from any pixel. Here is how Databricks closes the loop.

    Google credits the campaign that sourced the conversion conversion happens off-platform Ad click on Google D Delta table sync recorded in Databricks S Signals hash · dedupe Store Sales Direct API delivered · credited
    WHO THIS IS FOR

    Built for the teams that own the number.

    Omnichannel retailers reconciling point-of-sale against Google spend, with the numbers already modeled in Databricks Delta tables governed by Unity Catalog.

    Data engineers modeling in Databricks notebooks who need counter and showroom revenue inside the same ROAS view as online orders.

    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' Store Sales Direct API, sized for the store-sales match rate.

    2. Map

      A notebook job reads your Databricks Delta tables, and its columns map to Google Ads' Store Sales Direct API in the visual mapper, hashed and checked for the store-sales match rate.

    3. Deliver

      Each time the model rebuilds, Signals matches in-person transactions to Google accounts as store-sales conversions, reads only the rows the model rebuilt, with the store-sales match rate watched in the debugger.

    WHAT YOU GET

    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 Store Sales 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.
    ON THE WIRE

    What Store Sales actually receives.

    signals :: databricks.google-store-sales :: wire

    1. ● signals :: store sales upload
    2. > POST /offline_user_data_jobs source=databricks.main.sales.pos_ledger
    3. transaction_time 2026-07-19T22:30:22 · transaction_amount_micros 17727000000
    4. currency_code GBP
    5. hashed_email sha256 "6d2e91…" · hashed_last_name sha256 "cc410a…"
    6. ✓ accepted match=84%
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    FAQ

    Asked on almost every call.

    How often does the Databricks Google Store Sales 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, counter and showroom revenue inside the same ROAS view as online orders keeps pace on the store-sales match rate and job-cluster time stays modest.

    What match rate should a Databricks-sourced Google Store Sales 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' Store Sales Direct API flags the gap for omnichannel retailers reconciling point-of-sale against Google spend tracking the store-sales match rate.

    NEXT STEP

    Databricks to Google Store Sales, in production this week.

    Talk to our team All Databricks use cases