Data Modeling in the Age of JSON
Businesses that employ the Schema-on-Write methodology know the importance of data modeling. In Schema-on-Write, a data modeler or database designer creates a structure, or “schema,” for the data before it is loaded, or “written,” into the system.
With the more recent advent of the Schema-on-Read methodology, in which the goal is to load data into the system as quickly as possible and without upfront design and modeling, data modeling has taken a backseat. Still, it remains no less important: Data modeling helps define the structure and semantics of data, so business users and data scientists can properly query, manipulate, and analyze it.
Schema-on-Read requires that data be transformed into an understandable relational model in order to allow business users to make sense of it. As it turns out, semi-structured data can be transformed into a relational model by applying data modeling best practices. We describe these best practices in detail in our new ebook, “Data Modeling Best Practices: How to Convert Schema-on-Read into Schema-on-Write.”
Download our ebook to learn how to:
- Transform JSON data into traditional relational models
- Turn JSON into three form factor (3NF)
- Turn JSON into a Data Vault model
- Handle document changes in Schema-on-Read, and more