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Understanding the Hypercube

A hypercube is a powerful data structure for organizing and analyzing data from multiple perspectives. Think of a simple cube with three dimensions: length, width, and height. A hypercube extends this concept to an unlimited number of dimensions, allowing you to model complex business scenarios.

Each axis of the hypercube represents a key dimension of your data, such as:

  • Time (e.g., year, month, day)

  • Geography (e.g., region, country, city)

  • Product (e.g., category, brand, item)

  • Customer (e.g., segment, demographics)

The hypercube enables you to calculate metrics (like sales, revenue, or user counts) across any combination of these dimensions, ensuring your calculations are always consistent and reusable. For example, you can analyze:

  • Revenue by Month
  • Active users by Month and Product Category
  • Margin by Month, Product Category, and Region

How Cube Alchemy Builds the Hypercube

In Cube Alchemy, the hypercube is not limited to a single table. It combines multiple DataFrames into a unified, logical model that you can query seamlessly. You don't need to write the complex joins to connect them; the hypercube handles the relationships for you.

Key Concepts:

  • Data Model as a Graph: The underlying structure is a Directed Acyclic Graph (DAG).

    • Nodes: Your DataFrames.
    • Edges: The shared columns that link them.

  • Effortless Queries: When you request a metric across certain dimensions, the hypercube automatically traverses these connections (even across multiple "hops") to gather the necessary data.

  • Consistency: Define your metrics and dimensions once, and they can be reliably used across the entire data model.

Important Note: Avoid Circular Dependencies

For the hypercube to function correctly, your data model must be a Directed Acyclic Graph (DAG). This means you must avoid cyclic relationships (or circular references). Such cycles break the logic of data traversal and can lead to incorrect or infinite aggregations.