We are pleased to present how JMP plays a major part in our data environment at Soitec. With more than 600 users at our company, it is always a challenge to enhance the user experience and industrialize our products deployed on JMP. 

Today, JMP allows many data sources to be targeted but collecting data from AWS services can still be overly complex. 

Starting with JMP 15 and thanks to Python integration, we were able extend the capabilities of JMP further. We have internally validated a set of custom libraries (including S3, Athena, and RDS) that allows us to target the AWS services needed for end users. Today, with JMP 18's embedded integration with Python, it's much easier to manage the architecture.

In this presentation, we explain a bit more about the role that JMP plays in our data engineering pipeline and how we can benefit even more from the Python integration with JMP 18.