At the core of Holistics is a Data Modeling Layer. The modeling layer contains information about business logic and definitions and how they're being mapped to the underlying physical database (SQL data warehouse).
Based on these business definitions, when a non-technical user accesses certain metrics, Holistics use them to construct SQL queries against your data warehouse.
Data Modeling Layer is the interface between your SQL database and the BI interface.
At the fundamental level, there are 2 key concepts we need to understand: data model and dataset.
A data model is an abstract representation on top of a SQL database table that you may manipulate without directly affecting the underlying data. You can also store additional metadata that enrich the underlying data in the data table.
Data model contains metadata information such as: calculated dimensions, calculated measures, textual descriptions and relationships to other models.
Learn more about data model.
A dataset contains metadata about which data models (tables) to use and how they're joined together (relationships).
Dataset is needed to perform further reporting-related work: data exploration and building visualizations
- Data Exploration: Dataset can be shared to Explorers (non-technical users) to do self-service exploration of the data.
- Creating Charts: All Charts in Holistics have to be created from a dataset. This is done either by the Analyst or the Explorer
Conceptually, think of dataset as as a collection of models.
Learn more about dataset.