Discover how to supercharge analytics and AI workflows using Azure Databricks and Microsoft Fabric. This hands-on lab explores native AI/BI features in Azure Databricks, including ML-powered insights and real-time analytics. Learn multiple ways to serve data to Power BI, with a deep dive into Direct Lake mode with Fabric. Ideal for developers, data scientists, data analysts, and engineers modernizing BI with lakehouse architecture in the AI era.

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

Discover how to supercharge analytics and AI workflows using Azure Databricks and Microsoft Fabric. This hands-on lab explores native AI/BI features in Azure Databricks, including ML-powered insights and real-time analytics. Learn multiple ways to serve data to Power BI, with a deep dive into Direct Lake mode with Fabric. Ideal for developers, data scientists, data analysts, and engineers modernizing BI with lakehouse architecture in the AI era.

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

Discover how to supercharge analytics and AI workflows using Azure Databricks and Microsoft Fabric. This hands-on lab explores native AI/BI features in Azure Databricks, including ML-powered insights and real-time analytics. Learn multiple ways to serve data to Power BI, with a deep dive into Direct Lake mode with Fabric. Ideal for developers, data scientists, data analysts, and engineers modernizing BI with lakehouse architecture in the AI era.

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

Discover how to supercharge analytics and AI workflows using Azure Databricks and Microsoft Fabric. This hands-on lab explores native AI/BI features in Azure Databricks, including ML-powered insights and real-time analytics. Learn multiple ways to serve data to Power BI, with a deep dive into Direct Lake mode with Fabric. Ideal for developers, data scientists, data analysts, and engineers modernizing BI with lakehouse architecture in the AI era.

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

Mastercard is a global technology company whose role is anchored in trust. It supports 3.4 billion cards and over 143 billion transactions annually. To address customers’ increasing data volume and complex privacy needs, Mastercard has developed a novel service atop Databricks’ Clean Rooms and broader Data Intelligence Platform. This service combines several Databricks components with Mastercard’s IP, providing an evolved method for data-driven insights and value-added services while ensuring a unique standalone turnkey service. The result is a secure environment where multiple parties can collaborate on sensitive data without directly accessing each other’s information. After this session, attendees will understand how Mastercard used its expertise in privacy-enhancing technologies to create collaboration tools powered by Databricks’ Clean Rooms, AI/BI, Apps, Unity Catalog, Workflows and DatabricksIQ — as well as how to take advantage of this new privacy-enhancing service directly.

The emergence of secure data sharing is already having a tremendous economic impact, in large part due to the increasing ease and safety of sharing financial data. McKinsey predicts that the impact of open financial data will be 1-4.5% of GDP globally by 2030. This indicates there is a narrowing window on a massive opportunity for financial institutions and it is critical that they prioritize data sharing. This session will first address the ways in which Delta Sharing and Unity Catalog on a Databricks Lakehouse architecture provides a simple and open framework for building a Secure Data Sharing platform in the financial services industry. Next we will use a Databricks environment to walk through different use cases for open banking data and secure data sharing, demonstrating how they will be implemented using Delta Sharing, Unity Catalog, and other parts of the Lakehouse platform. The use cases will include examples of new product features such as Databricks to Databricks sharing, change data feed and streaming on Delta Sharing, table/column lineage, and the Delta Sharing Excel plugin to demonstrate state of the art sharing capabilities.

In this session, we will discuss secure data sharing on Databricks Lakehouse and will demonstrate architecture and code for common sharing use cases in the finance industry.

Talk by: Spencer Cook

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