Elon Musk famously said, “Manufacturing is 10,000% harder than prototypes.” The same is true for data. The manufacturing of raw data into actionable information is much harder than prototyping a single analytic. But just like manufacturing a car, you have to put in the R&D time to get your manufacturing process right.
How do you enable the R&D data teams (data analytics and data science teams) in your Snowflake environment without impacting your critical manufacturing of data-into-information? In this session, Rich Hathaway and Arkady Kleyner explore how you can open your data landscape to these R&D parties while segregating these experimental environments from your critical data-to-information factory.
In this session you will learn:
- How to create lab environments
- What critical automation needs to be in place
- How to segregate data sets
- How to promote R&D findings into your production environment