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Match Maven
The Match Maven module enables data teams to build and evaluate match-merge models using large language models (LLMs) and embedding-based similarity techniques. It is designed for experimentation, iteration, and optimization of custom entity ...
Troubleshooting Guide
This section outlines known issues encountered during setup or usage of LakeFusion, along with steps to resolve them. 1. Error Accessing the Match Maven Screen Issue: Users encounter an error when attempting to access the Match Maven screen within ...
LakeFusion Deployment Guide via Azure Marketplace
Prerequisite: Register Required Azure Resource Providers Before deploying LakeFusion from the Azure Marketplace, ensure that the necessary resource providers are registered for your Azure subscription. Follow the steps below: Navigate to ...
Householding
What is Householding? Householding in LakeFusion is the process of identifying and grouping individuals that belong to the same household, helping organizations better understand relationships within their customer base. Whether in insurance, ...
Provider 360
This section explains Provider 360, a powerful solution within LakeFusion designed to create a unified, real-time view of healthcare providers. It addresses the fragmentation of provider data across systems—improving operational performance, ...
Popular Articles
Who is LakeFusion MDM for?
LakeFusion is designed for modern data teams that are scaling their use of Databricks and need to ensure consistency, accuracy, and governance in core data entities such as customers, products, suppliers, and employees. It addresses the ...
Customer Environment
The Customer Environment represents the user's access point to the LakeFusion platform. The key steps include: User Access: Customers access LakeFusion via their web browser. Authentication: Authentication is handled using Databricks OpenID Connect ...
LakeFusion Environment
The LakeFusion Environment is the core of the platform and consists of two main layers: Micro Frontends: The user interface components. Microservices: The backend logic and processing components. a. Micro Frontends The LakeFusion Micro Frontends are ...
Databricks Workspace Integration
LakeFusion seamlessly integrates with Databricks, leveraging its robust features for data analytics and governance. Key aspects include: a. Authentication and Access Databricks OIDC: Facilitates secure communication between LakeFusion and Databricks. ...
End-to-End Flow
Here’s how the components of LakeFusion interact in a typical user session: User Login: The user accesses LakeFusion via the web browser. Their credentials are authenticated using Databricks OIDC and SSO through Okta or Azure AD. Portal Navigation: ...