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    CONNECTIONS / Databricks / Databricks to LinkedIn Offline CAPI

    Your Databricks lakehouse, wired to LinkedIn account-revenue reporting.

    Signals reads the Delta table through a Unity Catalog grant and reports closed B2B deals so LinkedIn optimizes on account revenue, hashed on the way out and governed like any other lakehouse job.

    Talk to our team Explore the setup

    databricks -> linkedin-offline-capi :: live

    1. ● databricks → linkedin.capi :: live
    2. > read main.sales.pos_ledger rows=23,077
    3. hash sha256(email,phone) consent=filtered
    4. route linkedin deliver
    5. ✓ delivered · 18,923 matched

    WHAT THIS ENABLES

    Databricks to LinkedIn Offline CAPI

    Databricks to LinkedIn Offline CAPI: A notebook job reads the offline and in-store sales in your Databricks Delta tables, and Signals reports closed B2B deals so LinkedIn optimizes on account revenue, hashed and consent-checked each time the model rebuilds.

    • Signals reports closed B2B deals so LinkedIn optimizes on account revenue, reading your Databricks Delta tables each time the model rebuilds.
    • Pipeline revenue tied to the LinkedIn campaigns that sourced it, fed by data engineers modeling in Databricks notebooks.
    WHAT FLOWS WHERE

    The click, the conversion, and the credit.

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

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

    Built for the teams that own the number.

    B2B teams whose deals close long after the impression, with the numbers already modeled in Databricks Delta tables governed by Unity Catalog.

    Data engineers modeling in Databricks notebooks who need pipeline revenue tied to the LinkedIn campaigns that sourced it.

    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 LinkedIn's Conversions API, sized for long B2B attribution windows.

    2. Map

      A notebook job reads your Databricks Delta tables, and its columns map to LinkedIn's Conversions API in the visual mapper, hashed and checked for long B2B attribution windows.

    3. Deliver

      Each time the model rebuilds, Signals reports closed B2B deals so LinkedIn optimizes on account revenue, reads only the rows the model rebuilt, with long B2B attribution windows 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 Offline CAPI 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 Offline CAPI actually receives.

    signals :: databricks.linkedin-offline-capi :: wire

    1. ● signals :: event payload
    2. > POST /conversionEvents source=databricks.main.sales.pos_ledger
    3. conversion "urn:lla:llaPartnerConversion:82232"
    4. conversionHappenedAt 1784449232000
    5. user.userIds [{ idType: "SHA256_EMAIL", idValue: "9c41af…" }]
    6. ✓ accepted · conversionValue 23342 USD
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    FAQ

    Asked on almost every call.

    How often does the Databricks LinkedIn Offline CAPI sync run?

    Cadence follows the job that rebuilds the table: a nightly model gives LinkedIn a nightly feed, an hourly one an hourly feed. Because a run picks up only the rows the model rebuilt, pipeline revenue tied to the LinkedIn campaigns that sourced it keeps pace on long B2B attribution windows and job-cluster time stays modest.

    What match rate should a Databricks-sourced LinkedIn Offline CAPI 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 LinkedIn's Conversions API flags the gap for B2B teams whose deals close long after the impression tracking long B2B attribution windows.

    NEXT STEP

    Databricks to LinkedIn Offline CAPI, in production this week.

    Talk to our team All Databricks use cases