Building trust in your data starts with making quality visible, testable, and automatable because without reliable data, even the most advanced platforms fall short. Data quality is a critical component of any data engineering workflow, yet it often becomes a complex and time-consuming challenge.

This session will be a practical, hands-on walkthrough of setting up data quality checks with Soda Core, an open-source framework, in a Microsoft Fabric environment, from scratch to automation. With Soda’s low-code, declarative approach, implementing quality checks becomes far more approachable, even for teams with limited engineering resources. Combined with Fabric’s own low-code experience, this makes for a powerful match and an accessible way to bring quality checks into daily workflows.

I will demonstrate how to define data quality checks including both built-in tests like data freshness and uniqueness, as well as custom checks tailored to specific business logic. Further demonstrate how to securely connect to Lakehouse tables, automate and monitor validations as part of data pipelines, all within a setup that’s suitable for production environments. We will also explore multiple quality check execution options:
- From VS Code via CLI using YAML configuration
- As a scheduled Azure Function for automated scanning
- Embedded within Spark notebooks in Fabric

By the end of this session, you’ll walk away with a practical blueprint for:
- Integrating Soda Core into Fabric
- Running various data quality checks at different stages of the pipeline
- Monitoring and operationalizing quality metrics
- Building trust in your data with a minimal learning curve

This session is ideal for data engineers, platform teams, and analytics professionals who want a hands-on, scalable approach to solving a foundational challenge in modern data platforms.