Question

Define these terms: Star Schema, Snowflake Schema, Fact Constellation, Data Marts.

Short answer

Star Schema is the simplest form of a data warehouse schema with one or more fact tables at the center and dimension tables radiating from it. Snowflake Schema is a more complex model where some dimension tables are normalized, hence reducing storage and improving query efficiencies. Fact Constellation, or galaxy schema, has multiple fact tables sharing dimension tables, suitable for more complex business scenarios. Data Marts are subsets of a data warehouse, designed with a specific business line or team in mind, often to meet a specific need.

Step-by-step solution

Star Schema

A Star Schema is the simplest form of a data warehouse schema. It is called a star schema because the diagram of the schema resembles a star, with points radiating from a center. The center of the star consists of one or more fact tables and the points of the star are the dimension tables.

Snowflake Schema

A Snowflake Schema is a more complex data warehouse schema model, where some dimension tables are normalized. This results in the schema looking like a snowflake with more branches compared to the star schema. This schema reduces the amount of storage space required, and improves the efficiency of certain queries.

Fact Constellation

A Fact Constellation is a more complex schema design where multiple fact tables share dimension tables. It represents more complex business scenarios, as multiple facts can be analyzed with different granularity levels. Fact constellations are sometimes also referred to as galaxy schema.

Data Marts

A Data Mart is a subset of a data warehouse. It contains a snapshot of operational data that helps business people to strategize based on the analysis of past trends and experiences. The important point to remember about data marts is they are designed with a specific business line or team in mind and they meet a specific need.

Key concepts

Star Schema

A Star Schema is one of the most fundamental and simplest data warehouse schema models to understand.
It gets its name because its design resembles a star, where all points radiate from a central hub.
At the heart of this star pattern, you'll find the fact table — a core component, containing measurable, quantitative data such as sales and revenue. Surrounding this central fact table are dimension tables.
Dimension tables hold descriptive data, like time, geography, or product details, which provide context to the facts.
This schema is highly efficient for simple queries since it usually requires fewer joins. However, it might sometimes lead to redundancy in data, as the dimension tables are not normalized.

Snowflake Schema

The Snowflake Schema is essentially a more complex or normalized version of the star schema.
Its structure resembles a snowflake due to its branching structure where dimension tables are normalized.
This normalization separates data into additional tables, resulting in a less redundant and potentially more efficient storage model compared to the star schema.
The primary advantage of this schema is its efficiency in handling queries that perform complex joins across tables.
However, this complexity can also lead to an increase in the time and resources required to execute queries compared to the Star Schema. This is because more tables need to be navigated and joined to extract data.

Fact Constellation

The Fact Constellation approach involves multiple facts tables that share some dimension tables.
This schema can handle more complex business processes compared to a single fact table approach.
Due to its structure, it's sometimes referred to as a "galaxy schema."
Fact Constellation is particularly useful in scenarios where different types of analytical processes or various granular levels of data aggregation are necessary.
By sharing dimension tables among different fact tables, it offers flexibility in analysis and allows the same dimensions to be available for multiple fact analyses, facilitating rich multi-dimensional views on business operations. However, this complexity often requires careful management and planning to ensure efficient data processing.

Data Marts

Data Marts are smaller, more specific slices of data drawn from the larger data warehouse ecosystem.
They're tailored to meet the analytical needs of specific business lines or teams, functioning almost like a mini data warehouse that caters to a particular niche.
Since data marts focus on narrower themes or business functions, they facilitate quicker insights and targeted performance analysis.
Businesses use them to obtain relevant datasets without the need to navigate the entire data warehouse, thus saving time and resources.
Data Marts can be set up as independent entities, with data pulled from transactional systems, or they can be dependent on a central data warehouse, reflecting a subset of data tailored to the department's needs.