Last month we dug into machine learning, which is the domain of the data scientist. Or is it? This month I talked with Jason McCollum, a certified data engineer and member of our Cloud Solutions professional services team, about a more expansive view of what leads up to data science.

In this Practical Cloud Journey chat, we talk about big data: What makes it big, and at what point does “pretty large” data turn into “big” data. Then we pivot to discuss what a data engineer does, and how that dovetails into the work of data scientists.

Because of Jason’s deep and dimensional background at the intersection of geospatial technology and agriculture, he is able to share some great examples of how to think about moving “compute” closer to “data,” and the precomputation of models close to the end user.

And if that is not enough, we discuss layered architectures, which logically leads to a discussion of baking and cakes. The result is a pretty sweet audiocast.