Loading…
A Salesforce Databricks integration is usually driven by an AI or advanced analytics goal: forecasting, lead scoring, churn prediction or an LLM application that needs CRM context. We build pipelines that land Salesforce objects and history into Delta Lake tables, keep them fresh with incremental or streaming loads, and feed features to models built in Databricks. The outputs, whether scores, recommendations or next-best-actions, are written back to Salesforce so they show up where sellers work. Where you already use Salesforce Data Cloud or Zero Copy, we design the pipeline to complement rather than duplicate it.
Outcomes
Most analytics and training workloads are fine with incremental batches every 15 to 60 minutes. Where a model needs to react to changes as they happen, for example a deal moving to a late stage, we stream Change Data Capture events into Databricks. Write-backs are scheduled to match model refresh cadence.
Schema evolution is enabled on the Delta tables with alerts on new or changed fields. Deletes and merges are captured so counts reconcile. We use the Bulk API for large loads and monitor daily API consumption so the pipeline never starves other integrations.
Yes. An MCP server can expose Databricks SQL and model endpoints alongside scoped Salesforce actions, so an assistant can explain why an account was scored high-risk and create a follow-up task once a rep agrees. Unity Catalog permissions govern what data the assistant can see.
Landing core objects and history into Delta tables with a clean silver layer typically takes a few weeks. Streaming CDC, feature engineering and write-back of model outputs are usually delivered as follow-on phases alongside the modelling work itself.
Other pairs involving Salesforce or Databricks.
Share your objects, volumes, and timing needs — we'll come back with a written scope.