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MindBridge's ability to integrate with Snowflake enables seamless automation of financial data ingestion, transformation, and analysis, enhancing financial risk detection capabilities.

This document provides an overview of how your team can integrate MindBridge with Snowflake, and offers additional context and guidance for implementation and developer teams within MindBridge customer organizations. This guide keeps the process straightforward and includes links to downloadable Python notebook templates (available on GitHub) for detailed implementation.

How MindBridge’s Snowflake integration works

MindBridge has developed a set of Python-based notebook templates for Snowflake to support common use cases, ensuring a streamlined integration between Snowflake and MindBridge.

Key aspects of the integration

• Pre-built Snowflake notebooks: Access pre-configured Snowflake notebooks for common tasks such as sending data to MindBridge, triggering scheduled 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 detailed tutorials and documentation to support your team in setting up and customizing these notebooks for quick implementation.

Getting started

For Snowflake users, a one-time setup step is required to enable secure API access and data formatting. This setup code is available upon request from your Customer Success representative and can be quickly implemented with the support of the MindBridge Data Solutions team.

Downloadable Python notebooks for Snowflake

MindBridge has created a series of Python-based notebooks that you can download and import into your Snowflake environment. These notebooks allow you to orchestrate MindBridge workflows directly from Snowflake. Documentation within the notebooks provides guidance for user-friendly setup and customization.

1. MindBridge_E2E_Integration_Tutorial - Provides a how-to guide for setting up a MindBridge integration with Snowflake, covering steps like setting up an Organization and Engagement, performing a General Ledger analysis, and extracting key information from analysis results.
2. MindBridge_Explore_GL_Table - Provides an example of querying from a Snowflake database of General Ledger data to create a file that can be leveraged as input into any MindBridge analysis.
3. MindBridge_E2E_Analysis_Data_Flow - Demonstrates a complete initial configuration of a MindBridge organization, engagement, and analysis. It includes uploading data from Snowflake to MindBridge, executing an analysis, and generating a link to view results in MindBridge.
4. MindBridge_Update_Data_Run_Existing_Analysis - Illustrates the process of updating data (e.g., adding new transactional data) from Snowflake 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.
5. MindBridge_Results_Integration_Example - Showcases how to pull MindBridge results back into Snowflake, enabling further analysis using downstream tools. This allows users to integrate MindBridge results into dashboards or reports for deeper insight and communication.

Access the notebook templates

Download the preconfigured Python notebooks for Snowflake from GitHub

Reach out to your Customer Success representative if you need assistance integrating MindBridge with your Snowflake environment. We are happy to support you through the process.

Example use case

Scenario

A global technology company uses Snowflake as its centralized data platform, updating it nightly with ERP and operational data. To streamline workflows, they prefer MindBridge to source data directly from Snowflake, reducing the need for multiple systems to access operational environments.

Example Workflow

Using the MindBridge integration notebooks with Snowflake, the company enables a fully automated, end-to-end financial analytics workflow:

• Data preparation and transformation

A scheduled Snowflake job prepares ERP and operational data by applying reusable, predefined transformation logic. Cleaned and structured datasets are formatted to match MindBridge requirements and pushed directly into MindBridge via API.

• Automated analysis orchestration

The same Snowflake Notebook automates MindBridge API calls to trigger new analyses as soon as data is ready. This orchestration ensures that every data refresh (e.g., nightly or weekly) results in timely, consistent financial analysis—without manual intervention.

• Frictionless insights delivery

With automated data delivery and analysis execution, business users can access the latest results in MindBridge as part of their daily workflows. No separate data pulls or manual uploads are required.

• Integrated risk intelligence

Risk scores and analysis results are programmatically retrieved via API back into Snowflake. The company leverages MindBridge outputs to conduct deeper investigations and enrich internal dashboards—enabling continuous risk monitoring and integrated reporting alongside other business KPIs.

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 Snowflake environment.