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Integrations Databricks

Integration · Engineering and data

Databricks + Finn.

Databricks integration with Finn. Databricks brings data engineering, analytics, and machine learning together in a shared data platform.

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The work behind the connection

What you can do with Databricks.

Databricks brings data engineering, analytics, and machine learning together in a shared data platform.

Deliver the recurring business report

Finn queries the agreed business datasets, explains the results, and delivers the finished report to its audience.

Typically handled byBusiness intelligence analyst

Starts when

The weekly or monthly reporting deadline arrives.

What Finn handles

  1. Read the report's questions, definitions, audience, and reporting period.
  2. Run the required Databricks SQL queries and collect their completed results, including source freshness and missing-data notes.
  3. Calculate the agreed comparisons and write the explanation with the dataset and query references.
  4. Save the finished report through connected SharePoint and send it through Outlook or Slack.

The result

The audience receives a completed business report with traceable figures, definitions, and reporting dates.

Answer a business question with evidence

Finn investigates a specific business question and produces an answer that shows the data behind its conclusion.

Typically handled byData analyst

Starts when

A manager asks why a business measure changed or how a customer segment is performing.

What Finn handles

  1. Clarify the decision, metric definition, time range, and permitted dataset scope.
  2. Query the Databricks tables and compare the requested periods or groups.
  3. Test the proposed explanation against the available data and distinguish observed relationships from unresolved causes.
  4. Publish the answer with the supporting tables and query references, then answer the manager's follow-up questions.

The result

The business question has a documented analysis with evidence and clearly stated limits.

Repair a failed data pipeline

Finn investigates a failed Databricks job, corrects its cause, and follows the repaired run through the expected output.

Typically handled byData engineer

Starts when

A scheduled data job fails or its required output is missing.

What Finn handles

  1. Read the failed task, run output, inputs, and recent changes to identify the cause.
  2. Correct the input issue or change the owning pipeline code through connected GitHub and complete its review.
  3. Repair or rerun the affected job with the accepted parameters after any required production change approval.
  4. Check the completed output and update the incident and dataset owner with the repair result.

The result

The failed pipeline has a completed repair and its expected output is verified.

Backfill an accepted missing data period

Finn runs the organization's backfill job for an agreed period and verifies the dataset's resulting coverage.

Typically handled byData operations engineer

Starts when

A data owner accepts a backfill after an outage or delayed source delivery.

What Finn handles

  1. Read the missing period, affected datasets, and the existing job's backfill contract.
  2. Collect the required production-data decision and invoke the job with the exact accepted range.
  3. Follow the execution and inspect the output's dates and counts against the expected source coverage.
  4. Publish the backfill result and update the data incident with any periods still missing.

The result

The accepted data period is restored or precisely accounted for in the completed backfill record.

Resolve a recurring data-quality problem

Finn traces a reported data discrepancy to its source or transformation and completes the correction with evidence.

Typically handled byAnalytics engineer

Starts when

A business report identifies missing, duplicated, or inconsistent records.

What Finn handles

  1. Read the affected records and compare the source data with the Databricks transformation output.
  2. Identify whether the correction belongs in the source system or pipeline, then obtain the source owner's decision where business facts change.
  3. Complete the accepted source correction or reviewed GitHub pipeline fix and rerun the affected processing.
  4. Query the corrected output and update the report owner and quality issue with the verified result.

The result

The reported discrepancy has a verified correction and an explanation of its cause.

Fulfill a governed dataset access request

Finn gets the data owner's decision and applies the exact Unity Catalog access needed for an accepted request.

Typically handled byData access administrator

Starts when

An employee requests access to a named dataset for a business task.

What Finn handles

  1. Read the requested catalog, schema, tables, and business purpose together with the employee's identity.
  2. Get the data owner's decision on the permission scope and any time limit.
  3. Apply the accepted Unity Catalog permission change and read back the resulting grants.
  4. Tell the employee how to locate the dataset and complete the connected service request with the decision and access result.

The result

The accepted dataset permissions are in place and the request contains their owner decision and scope.

Prepare the account review from product data

Finn combines customer usage facts with the account record and delivers a sourced briefing for the customer team.

Typically handled byCustomer success operations analyst

Starts when

A customer account review is due.

What Finn handles

  1. Read the account and review questions from connected Salesforce and identify the agreed customer-data mapping.
  2. Query the Databricks usage and service datasets for the review period.
  3. Summarize the recorded adoption, activity changes, and open issues without inventing customer intent or a health score.
  4. Save the briefing to the account and create the agreed follow-up tasks for the customer owner.

The result

The account team has a completed review briefing linked to the underlying customer facts and follow-up work.

Deliver a defined dataset extract

Finn produces the exact dataset requested by an authorized business team and completes its delivery with the necessary context.

Typically handled byData fulfillment analyst

Starts when

A business owner accepts a specific data extract and recipient list.

What Finn handles

  1. Read the requested fields, filters, reporting period, and recipient access decision.
  2. Run the Databricks query and collect the completed result in the required format.
  3. Check the export against the requested scope and document its field definitions and source date.
  4. Save the extract to the designated connected Box or SharePoint location and send the authorized recipients its access link.

The result

The team receives the requested data extract with its definitions, source period, and delivery record.

Connection setup

Set up the connection around your work.

Contact Finn to discuss the records your workflow needs and the actions you want it to perform.

Your team chooses what Finn can access, what agents can change, and when a person must approve the work. The connection needs the appropriate account access and permissions from Databricks.

The Finn platform

Finn Agents

Help customers. Work with your team. Finish the job.

Explore Agents

Finn Systems

Business apps for the work behind the conversation.

Explore Systems

Finn AI Gateway

Control connected AI apps, models, access, and spending.

Explore AI Gateway

Meet Finn

Put your Databricks integration to work.

Tell us which records, actions, and teams the connection needs to support.

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