Most organisations want to build GenAI applications, but their data platform wasn’t designed for it. Relational databases and data warehouses are excellent for structured analytics, but GenAI introduces new requirements: handling unstructured content, supporting rapid iteration, enabling governed access to sensitive datasets, and producing “retrieval-ready” data that can power search and RAG workflows.

In this 6-hour hands-on workshop, participants will build a GenAI-ready lakehouse on AWS. We’ll start with a traditional relational dataset and a set of unstructured documents, then design a lakehouse architecture using Amazon S3, AWS Glue Data Catalog, Athena, and Apache Iceberg. Participants will implement ingestion and transformation patterns that create both analytics-friendly tables and GenAI-friendly datasets, including chunked text outputs, metadata enrichment, and quality checks that improve retrieval performance.

The workshop is structured as a real end-to-end case study: we benchmark the starting point, build the lakehouse step-by-step, and demonstrate measurable outcomes such as faster dataset iteration, improved searchability, and better governance. Attendees will leave with reference architectures, a GenAI data readiness checklist, and templates they can apply immediately.