I will briefly discuss the concept of Data Lakehouse, its architecture and components. One of their characteristics is that they have some functionality like consistency similar to a Data warehouse, but they can process unstructured and semi-structured data. We did already some investigations in a number of projects encompassing scientific fields such as earth observation, sentiment analysis, and bio-imaging. I will share some preliminary insights to what kind of scientific use workflows it can be applied. And will show how we can use the various Data Lakehouse components in the workflow. The talk will also touch upon commercial solutions like Data Bricks that have full stack, including an ML-ops component, and open source solutions.

I will share some preliminary insights to what kind of scientific use workflows it can be applied. And will show how we can use the various Data Lakehouse components in the workflow. The talk will also touch upon commercial solutions like Data Bricks that have full stack, including an ML-ops component, and open source solutions.