Implementing a lakehouse architecture often presents key challenges, such as managing the complexity of data ingestion from diverse sources, maintaining operational excellence, ensuring robust governance and security controls, and enabling monitoring and auditing across all medallion layers.

With Microsoft Fabric’s unified analytics SaaS solution, these challenges can be significantly mitigated. Fabric accelerates and scales lakehouse implementation using a metadata-driven framework that seamlessly integrates engineering, analytics, and data science functions. This approach promotes interoperability, automation, security, and governance to deliver reliable, high-quality outcomes.

In this session, we’ll explore a metadata-driven methodology for lakehouse implementation, drawing from real-world experiences and field-tested best practices. The approach leverages a modular, reusable, and standardized framework for:
- Metadata-driven data ingestion
- Alert notifications
- Metrics reporting
- Data validation and profiling
- PII data anonymization and governance
- End to End Auditing across all layers for reprocessing
- Security patterns implementation

This framework not only addresses the complexities of data management but also delivers a cost-effective, efficient solution for businesses, enhancing the productivity of both data engineers and analysts.
Join us for this deep dive to learn how to overcome common lakehouse challenges while unlocking scalable and consistent results using Microsoft Fabric.