22-25 April 2026 
Michael Victor
I am a Consultant in Data Engineering and Data Science, passionate about solving complex data challenges and enabling businesses to leverage the power of analytics. With over a decade of professional experience, I specialise in designing scalable data pipelines, optimizing work flows, and building predictive models that drive strategic decision-making.
Michael Victor's Sessions
Building a Feature Store in Fabric to Support Model ReproducibilitySQLBits 2026
Learn to build a feature Store in Fabric to prevent model reproducibility nightmares and promote feature reuse. Covers point-in-time correctness, feature versioning, and reusable schemas. For data engineers and data scientists building production ML.
Learning to Spot and Circumvent Paradoxes in Data Analysis to avoid flawed conclusionsSQLBits 2026
Your data might be lying to you. Learn to detect Simpson's Paradox, the Will Rogers Phenomenon, and other analytical traps where aggregation and confounding variables flip conclusions. Walk away with practical strategies to spot and avoid costly mistakes
Selecting the Right Tools for Deploying a CI/CD Workflow in FabricSQLBits 2026
Fabric CI/CD tool selection doesn't have to be daunting. This session compares three deployment approaches: native Git integration, fabric-cicd/REST API, and Terraform across common patterns to help you choose the right tool for your needs.
The Power of Naming: Setting up a Naming Convention for SuccessSQLBits 2026
Naming is more than labeling—it shapes what things become. This session explores naming conventions for tables, columns, variables, and artifacts, focusing on readability and clarity.
Great expectations for your data qualitySQLBits 2025
Poor data quality can cost up to 25% of profits, yet it’s often overlooked. This session presents a framework using Great Expectations, an open-source Python library, to test and maintain data quality. Learn practical tips and hands-on techniques to improve outcomes.