Working with data cubes in Snowflake schema

05 Oct 2023

In this blog post, we will explore the concept of data cubes and how they can be implemented in a Snowflake schema. A data cube is a multidimensional data structure that allows for efficient analysis and querying of data from different perspectives. The Snowflake schema is a popular multidimensional modeling technique that organizes data using a star schema with multiple levels of hierarchies.

What is a Snowflake Schema?

Before understanding data cubes, let’s first briefly discuss the Snowflake schema. The Snowflake schema is a variation of the star schema, commonly used in data warehousing. In a Snowflake schema, dimensions are normalized into multiple tables, resulting in a more normalized structure compared to the star schema.

What is a Data Cube?

A data cube extends the two-dimensional concept of a spreadsheet or table into n-dimensions, allowing for analytical processing and reporting across multiple dimensions. Each dimension represents an attribute, and the combination of all dimensions creates a unique point in the cube, known as a cell.

Implementing a Data Cube in Snowflake Schema

To implement a data cube in a Snowflake schema, we need to model the dimensions and measures appropriately. Here’s how you can do it:

Identify the dimensions: Start by identifying the dimensions that are relevant to your analysis. These could be time, geography, product category, etc.
Create dimension tables: Create separate dimension tables for each dimension identified in the previous step. These dimension tables will contain the attributes related to each dimension. For example, a time dimension table could have attributes like year, quarter, and month.
Create a fact table: Create a fact table that contains the measures or metrics you want to analyze. This table will have foreign keys referencing the dimension tables and the corresponding measures.
Build the Snowflake schema: Normalize the dimension tables by creating additional dimension tables for each level of hierarchy. For example, if your product category dimension has subcategories, create a separate table for subcategories.
Create materialized views or cubes: Snowflake schema allows for materialized views or cubes, which store pre-aggregated data across different dimensions. This helps improve query performance. You can create cubes using the CREATE MATERIALIZED VIEW statement in Snowflake.

Querying and Analyzing Data Cubes in Snowflake

Once you have implemented a data cube in a Snowflake schema, you can query and analyze the data using SQL statements. Here are a few examples:

-- Query total sales by year and quarter
SELECT
    YEAR,
    QUARTER,
    SUM(SALES_AMOUNT) AS TOTAL_SALES
FROM
    FACT_TABLE
JOIN
    TIME_DIMENSION ON FACT_TABLE.TIME_KEY = TIME_DIMENSION.KEY
GROUP BY
    YEAR,
    QUARTER;

-- Query average sales by product category and year
SELECT
    PRODUCT_CATEGORY,
    YEAR,
    AVG(SALES_AMOUNT) AS AVERAGE_SALES
FROM
    FACT_TABLE
JOIN
    PRODUCT_DIMENSION ON FACT_TABLE.PRODUCT_KEY = PRODUCT_DIMENSION.KEY
JOIN
    TIME_DIMENSION ON FACT_TABLE.TIME_KEY = TIME_DIMENSION.KEY
GROUP BY
    PRODUCT_CATEGORY,
    YEAR;

Conclusion

Data cubes offer a powerful way to analyze multidimensional data, and the Snowflake schema provides an efficient way to organize and query this data. By implementing a data cube in a Snowflake schema, you can gain valuable insights and make informed business decisions. Hopefully, this blog post has provided you with a basic understanding of working with data cubes in a Snowflake schema.