One of the challenges that comes with moving to MongoDB is figuring how to best model your data. While most developers have internalized the rules of thumb for designing schemas for RDBMSs, these rules don't always apply to MongoDB. The simple fact that documents can represent rich, schema-free data structures means that we have a lot of viable alternatives to the standard, normalized, relational model. Not only that, MongoDB has several unique features, such as atomic updates and indexed array keys, that greatly influence the kinds of schemas that make sense. Understandably, this begets good questions:
In this session, we'll answer these questions and more, provide a number of data modeling rules of thumb, and discuss the tradeoffs of various data modeling strategies.
Before joining MongoDB, Jared ran Product Development at Yottaa where he developed a real-time analytics engine on top of MongoDB and Ruby on Rails. Jared began his career by dropping out of Brown University to become the founder and CTO of TAZZ Networks.