Denis Dubeau

A Fortune 500 semiconductor manufacturer generates hundreds of terabytes of production data across facilities in two international regions. That data must stay on-premises for regulatory compliance, latency sensitivity, and economics of scale — but the data science team depends on Databricks for the AI modeling that drives yield improvement and quality assurance. Rather than replicating data to the cloud or building custom ETL pipelines, they deployed MinIO AIStor with native Delta Sharing embedded at the storage layer. Databricks queries live Delta and Iceberg tables on-premises through Unity Catalog with zero data movement — no copies, no pipelines, no additional infrastructure. In this session, hear how the solution was architected, what decisions were made and why, and what the team learned operating it in production. Includes perspectives from MinIO and Databricks, a technical walkthrough, and a live demo of AIStor Table Sharing connected to a Databricks workspace.

Many organizations struggle to provide a data platform that makes data ready for analytics. Learn how to use Databricks with Delta Lake to make the data in your Amazon S3 data lake more reliable with higher performance so it can support all of your analytics across data science, machine learning, and BI/reporting.