Any data science initiative depends on data to work. That data has to be modeled and managed well in order to get value out of it. However, the approach we take around data modelling can be influenced by the ways that we store data over time. Understanding this in advance can make it easier to get to answers from our data, and also avoid problems caused by bad decisions or overlooking data modelling at the start.
This session will look at how to take a conceptual data model and application workflows together to make a logical data model, then how to optimise this physically based on the infrastructure that you choose, in this case Apache Cassandra.
An understanding of the basic principles around data modelling and databases would be helpful, but not essential.