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Data Modeling Approach

How we design our data structures for accessibility and performance using the Star Schema approach.

Our Data Modeling Approach

Flipside models on-chain data around a common set of principles known as the star schema, an industry-standard model for relational data warehouses.
A star schema is a data modeling technique commonly used in data warehousing and business intelligence to organize and represent data in a way that is easily accessible and understandable for end-users. The schema consists of a central fact table surrounded by dimension tables that provide additional context to the data in the fact table. The star schema design helps users quickly and easily analyze data by:
  • reducing the complexity of the data structure
  • enabling clear and meaningful data visualization
  • improving query performance
This approach requires modelers to classify their tables as either dimension or fact tables.
Fact tables store observations or events, and can be blocks, transactions, mints, swaps, etc. If you were modeling sales for a restaurant chain, for example, each sale might be represented by a row in a fact table.
Dimension ("dim") tables describe entities—the things you analyze. Entities can include labels, prices, decimals, tags, etc. Extending the restaurant metaphor: if each individual sale is a fact, then which restaurant the sale occurred in would be a dimension, describing that fact.
The key here is that:
  • Facts support summarization ("what is the total number of sales?")
  • Dimensions support filtering and grouping ("what are the sales totals grouped by restaurant?")
This kind of schema design is incredibly popular, and we see it as a key part of our strategy to provide the most accessible and performant blockchain data possible.