Data scientists and software engineers both work with databases, but they use them for different purposes. So if you’re a data scientist thinking about the best way to store and access data for your analytics, you’ll likely come up with a very different set of requirements than a software engineer looking to power an application. Hence the split between analytics and transactional databases—certain technologies are designed for one or the other, but no single type of database is perfect for both use cases. In this episode we’ll talk about the differences between transactional and analytics databases, so no matter whether you’re an analytics person or more of a classical software engineer, you can understand the needs of your colleagues on the other side.
In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications.