Data powers scientific discovery and innovation. But data is only as good as its data management strategy, they key factor in ensuring data quality, accessibility, and reproducibility of results - all requirements of reliable scientific evidence.
To build a research and development (R&D) platform that addresses these challenges, consider the lakehouse. A well-architected lakehouse adheres to the principles of data governance (FAIR), ensures security and compliance to protect sensitive data (HIPAA) and fosters collaboration via data sharing. It also supports operational excellence, reliability, performance efficiency and cost optimization.
In this workshop, you will:
- Learn the guiding principles for the lakehouse
- Learn the implications for system architecture and healthcare-specific considerations for a R&D platform
- See an end-to-end platform demo through the lens of genomics, highlighting workloads scientists can do more efficiently only on the lakehouse
- See a demo of using large language models for scientific question answering