Databases · Destination connector
Databricks ELT Connector
Extract delivers approved, modeled business data to Databricks. Teams use it to publish governed analytical and operational models for downstream use and keep those downstream actions current on a flexible schedule.
View setup guide
Use cases
What teams use this connector for
Modeled table delivery
Use Extract to deliver approved analytical and operational models to Databricks.
Reporting data products
Publish tested customer, finance, product, or marketing models in Databricks for downstream reporting.
Operational data sharing
Make approved modeled records available in Databricks for applications and operational workflows.
Model version control
Keep the transformation logic upstream while updating the resulting governed datasets in Databricks.
Governed scheduled delivery
Keep Databricks supplied every 15 minutes, hourly, daily, or on a custom schedule using data models that teams can test and approve.
Connector data
Data accepted by this destination
This destination receives the modeled records selected in the connected Extract workflow.
Connector pairings
Popular sources
Commonly paired with Databricks in Extract workflows.
Why Extract
Why Extract for Databricks
- Rust-native performanceDeliver approved records to Databricks with Rust-native data movement on a schedule that matches the operational workflow.
- Full run transparencyInspect Databricks delivery execution, timing, and failures in Extract's run-level logs.
- ELT + Reverse ETL in oneBuild governed models from inbound data, then use Extract Reverse ETL to activate the approved records in Databricks.
FAQ
Frequently asked questions
How does Extract handle incremental and full Databricks deliveries?
An incremental delivery processes only new or changed data since a stored checkpoint or new date partition, while a full delivery processes the complete selected dataset or date range again. By avoiding repeated processing and writes for the full dataset, incremental deliverys can reduce destination database updates and warehouse compute costs. Destination loading behavior depends on the incoming stream extract mode and the destination load mode; source incremental extraction is not applicable. Extract records run details so teams can verify each delivery.
Can I backfill historical Databricks data with Extract?
Historical coverage for Databricks: No connector-specific backfill parameter is exposed. Initial historical coverage depends on the stream and provider. Use the setup guide and run details to confirm the available source records and selected settings.
How should teams prepare data for Databricks?
Prepare and validate the modeled records before delivery, including any eligibility, consent, or suppression rules required by the workflow. Then use the Databricks setup guide and Extract's run-level logs to validate each delivery.
How often can Extract deliver data to Databricks?
Extract supports schedules every 15 minutes, hourly, daily, or on a custom cadence. Teams can choose a frequency that matches routine reporting, time-sensitive monitoring, or operational activation needs.
What does the Databricks data model look like in the destination?
Databricks receives the approved modeled records selected in the connection. The destination shape follows that validated model; use the setup guide to confirm any connector-specific field requirements.