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MindBridge has developed a set of set of notebooks to create a streamlined integration betweeen Databricks and MindBridge. The same data workflows can be adapted for other cloud data platforms like Snowflake and Microsoft Fabric.

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Benefits

  • Operational efficiency: Streamlines operations and eliminates the need for custom scripts.
  • Focused risk mitigation: Enables finance teams to concentrate on reviewing and mitigating risks more effectively.
  • Seamless workflow: Maintains consistent workflows within the Databricks environment.

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Key aspects of the integration

  • Pre-configured Python notebooks: Access reusable Python notebooks designed to run within Databricks for tasks such as sending data to MindBridge, triggering analyses, and querying results.
  • Customization: Easily customize these notebooks to meet specific needs, such as adjusting data frequency, integrating custom data sources, or modifying analysis parameters.
  • Resources and tutorials: Leverage supporting documentation to help your team quickly set up and customize the notebooks for your environment.
NameDescription
MindBridge_E2E_Integration_TutorialProvides a how-to guide for setting up a MindBridge integration with Databricks, covering steps like setting up an Organization and Engagement, performing a General Ledger analysis, and extracting key information from analysis results.
MindBridge_Explore_GL_TableProvides an example of querying from a Databricks database of General Ledger data to create a file that can be leveraged as input into any MindBridge analysis.
MindBridge_E2E_Analysis_Data_FlowDemonstrates a complete initial configuration of a MindBridge organization, engagement, and analysis. It includes uploading data from Databricks to MindBridge, executing an analysis, and generating a link to view results in MindBridge.
MindBridge_Update_Data_Run_Existing_AnalysisIllustrates the process of updating data (e.g., adding new transactional data) from Databricks and running an existing analysis in MindBridge without modifying the original configuration. This use case supports periodic analysis needs, allowing for refreshed data insights based on the latest data updates.
MindBridge_Results_Integration_ExampleShowcases how to pull MindBridge results back into Databricks, enabling further analysis using downstream tools. This allows users to integrate MindBridge results into dashboards or reports for deeper insight and communication.
Download the preconfigured Python notebooks for Databricks from GitHub.
Reach out to your Customer Success representative if you need assistance integrating MindBridge with your Databricks environment. We are happy to support you through the process.