One great way to get ahead in your career is to make good bets on what technologies are going to become important in the future, and to invest time in learning them. If that sounds like something you want to do, then you should definitely be paying attention to graph databases.

Graph databases aren’t exactly new, but they’ve become increasingly important as graph data (data that describe interconnected networks of things) has become more widely available than ever. Social media, supply chains, mobile device tracking, economics and many more fields are generating more graph data than ever before, and buried in these datasets are potential solutions for many of our biggest problems.

That’s why I was so excited to speak with Denise Gosnell and Matthias Broecheler, respectively the Chief Data Officer and Chief Technologist at DataStax, a company specialized in solving data engineering problems for enterprises. Apart from their extensive experience working with graph databases at DataStax, and Denise and Matthias have also recently written a book called The Practitioner’s Guide to Graph Data, and were kind enough to make the time for a discussion about the basics of data engineering and graph data for this episode of the Towards Data Science Podcast.