The difference between schema on write and schema on read refers to the way data is structured and defined in a data storage system.
In schema on write, the structure and definition of the data is determined before the data is written to the storage system. This means that the data must conform to a predefined schema or set of rules before it is stored. This approach is used in traditional relational databases where the schema must be defined before any data can be inserted. This approach is also used in ETL (Extract, Transform, Load) processes, where data is transformed to fit a specific schema before being loaded into a data warehouse.
In schema on read, the structure and definition of the data is determined when the data is read from the storage system. This means that the data can be stored in its raw form, without conforming to a predefined schema. This approach is used in modern big data platforms, such as Hadoop and NoSQL databases, where the schema is determined when the data is queried. This approach is also used in ELT (Extract, Load, Transform) processes, where data is loaded into a data lake in its raw form and then transformed when it is queried.
The main advantage of schema on write is that it ensures data quality and consistency by enforcing a predefined schema. This can make it easier to understand and analyze the data.