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    CONNECTIONS / Databricks / Databricks to LinkedIn Leads CAPI

    Your Databricks lakehouse, wired to LinkedIn lead-stage optimization.

    Signals reads the Delta table through a Unity Catalog grant and reports deal-stage progress so LinkedIn favors opportunities that advance, hashed on the way out and governed like any other lakehouse job.

    Talk to our team Explore the setup

    databricks -> linkedin-leads-capi :: live

    1. ● databricks → linkedin.capi :: live
    2. > read main.crm.pipeline_stages rows=47,391
    3. hash sha256(email,phone) consent=filtered
    4. route linkedin deliver
    5. ✓ delivered · 37,439 matched

    WHAT THIS ENABLES

    Databricks to LinkedIn Leads CAPI

    Databricks to LinkedIn Leads CAPI: A notebook job reads the qualified-lead and pipeline signals in your Databricks Delta tables, and Signals reports deal-stage progress so LinkedIn favors opportunities that advance, hashed and consent-checked each time the model rebuilds.

    • Signals reports deal-stage progress so LinkedIn favors opportunities that advance, reading your Databricks Delta tables each time the model rebuilds.
    • LinkedIn focus on the deals that actually advance, fed by data engineers modeling in Databricks notebooks.

    WHAT FLOWS WHERE

    Databricks to Leads CAPI, mapped.

    The notebook job streams qualified-lead and pipeline signals into Signals for hashing and consent screening, then LinkedIn's Leads Conversions API records the lead conversions with opportunity-stage weighting accounted for.

    The notebook job streams qualified-lead and pipeline signals into Signals for hashing and consent screening, then LinkedIn's Leads Conversions API records the lead conversions with opportunity-stage weighting accounted for.

    WHO THIS IS FOR

    Built for the teams that own the number.

    B2B advertisers whose LinkedIn forms fill faster than they qualify, with the numbers already modeled in Databricks Delta tables governed by Unity Catalog.

    Data engineers modeling in Databricks notebooks who need LinkedIn focus on the deals that actually advance.

    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 Leads Conversions API, sized for opportunity-stage weighting.

    2. Map

      A notebook job reads your Databricks Delta tables, and its columns map to LinkedIn's Leads Conversions API in the visual mapper, hashed and checked for opportunity-stage weighting.

    3. Deliver

      Each time the model rebuilds, Signals reports deal-stage progress so LinkedIn favors opportunities that advance, reads only the rows the model rebuilt, with opportunity-stage weighting watched in the debugger.

    WHAT YOU GET

    What ships with this use case.

    LinkedIn focus on the deals that actually advance on every run, since Signals reads only the rows the model rebuilt.

    Campaign focus on the deals that advance, delivered each time the model rebuilds without job-cluster time climbing.

    Each run returns a match report on opportunity-stage weighting from LinkedIn's Leads Conversions API, read from your Databricks Delta tables each time the model rebuilds, at flat job-cluster time.

    ON THE WIRE

    What Leads CAPI actually receives.

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

    1. ● signals :: event payload
    2. > POST /conversionEvents source=databricks.main.crm.pipeline_stages
    3. conversion "urn:lla:llaPartnerConversion:85820"
    4. conversionHappenedAt 1784256820000
    5. user.userIds [{ idType: "SHA256_EMAIL", idValue: "b19e04…" }]
    6. ✓ accepted · conversionValue 4690 USD
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    FAQ

    Asked on almost every call.

    How often does the Databricks LinkedIn Leads 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, LinkedIn focus on the deals that actually advance keeps pace on opportunity-stage weighting and job-cluster time stays modest.

    What match rate should a Databricks-sourced LinkedIn Leads 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 Leads Conversions API flags the gap for B2B advertisers whose LinkedIn forms fill faster than they qualify tracking opportunity-stage weighting.

    NEXT STEP

    Databricks to LinkedIn Leads CAPI, in production this week.

    Talk to our team All Databricks use cases