# Data Modeling for Data Engineering (Data Engineering)

> Star schema, snowflake, Data Vault, normalization, SCD types, grain, additive metrics

- 20 interview questions
- Mid-Level
- [Interview Questions: Data Engineering](https://sharpskill.dev/en/technologies/data-engineering/interview-questions.md)

## 1. What is a star schema?

**Answer**

A star schema is a dimensional data model where a central fact table is surrounded by dimension tables. The fact table contains metrics and foreign keys to dimensions, which hold descriptive attributes. This simple, denormalized structure optimizes analytical query performance by minimizing joins.

## 2. What is the difference between a fact table and a dimension table?

**Answer**

A fact table contains quantitative measures (metrics) and foreign keys to dimensions. It records events or transactions. A dimension table contains descriptive attributes (who, what, where, when) enabling filtering and grouping of facts. Facts are numeric and aggregatable, dimensions are textual and descriptive.

## 3. What is the grain of a fact table?

**Answer**

The grain defines the level of detail of a row in the fact table. It answers the question: what exactly does one row represent? For example, one sale per row, one sale per day per product, or one sale per hour. Defining the grain is the first step in dimensional modeling as it determines which dimensions are needed and what level of aggregation is stored.

## 17 more questions available

- What is the difference between a star schema and a snowflake schema?
- What is a conformed dimension?

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