Databases · Source connector
Databricks ELT Connector
Extract brings Databricks databases data into data warehouses and databases for analysis with the rest of your business data. Teams use it to analyze operational records alongside the rest of their business data. Flexible scheduling keeps reporting current for routine analysis and time-sensitive decisions.
View setup guide
Use cases
What teams use this connector for
Operational analytics
Analyze Databricks records alongside customer, finance, and product data to support business decisions.
Data reconciliation
Compare Databricks records with downstream models to find missing, duplicated, or inconsistent data.
Historical trend analysis
Use Databricks history to benchmark current activity and investigate changes over time.
Flexible reporting cadence
Refresh Databricks reporting every 15 minutes, hourly, daily, or on a custom schedule as business needs change.
Warehouse and database analysis
Use Extract to make Databricks databases data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Connector data
Configured streams
Configure the database tables or views to replicate when creating the Extract connection.
Connector pairings
Popular destinations
Commonly paired with Databricks in Extract workflows.
Why Extract
Why Extract for Databricks
- Rust-native performanceUse Rust-native data movement to refresh Databricks reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect Databricks API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine Databricks data with customer and finance models, then send approved outputs through a separately configured Reverse ETL destination.
FAQ
Frequently asked questions
How does Extract handle incremental and full Databricks syncs?
An incremental sync processes only new or changed data since a stored checkpoint or new date partition, while a full sync processes the complete selected dataset or date range again. By avoiding repeated processing and writes for the full dataset, incremental syncs can reduce destination database updates and warehouse compute costs. Dynamic stream configuration supports full refresh, incremental changes. Behavior depends on stream settings. Extract records run details so teams can verify each sync.
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 can teams monitor Databricks data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect Databricks API activity, execution steps, and timing. This helps teams distinguish a source-data delay from a failed or incomplete run.
How often can Extract sync data from 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?
The Available streams table describes the datasets Extract can read from Databricks. Use the connector documentation link for the available schema details and configuration requirements.