At this moment I do not have a personal relationship with a computer.
– Janet Reno
Semi-Structured Data Modelling
Data warehousing modeling methodologies for semi-structured data are designed to handle data that doesn’t conform strictly to the tabular structure of traditional relational databases. Semi-structured data is characterized by its flexibility, often represented in formats like JSON, XML, or key-value pairs.
Modeling such data for a data warehouse requires specialized techniques. Here are some methodologies and approaches for modeling semi-structured data in a data warehouse:
Star Schema with JSON/XML Columns:
- One common approach is to use a star schema, similar to what’s used for structured data warehouses. However, in this case, you include JSON or XML columns to store semi-structured data.
- In this approach, you maintain a few structured tables (dimension and fact tables) along with one or more columns that store semi-structured data.
- This allows you to query and analyze both structured and semi-structured data together.
Schema-on-Read:
- Instead of enforcing a strict schema upfront, adopt a “schema-on-read” approach.
- Store the raw semistructured data as-is in your data warehouse without trying to fit it into predefined tables.
- Use schema inference and flexible query mechanisms to work with the data when you need to retrieve or analyze it.
Schema-on-Write:
In this approach, you extract the semi-structured data, transform it into a structured format, and then load it into your data warehouse. You can use ETL (Extract, Transform, Load) processes to achieve this.
Hybrid Approach:
Combine elements of both schema-on-read and schema-on-write, depending on the specific use case and data source.