Snowflake to Star Schema Conversion for In-Memory Analytics
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Solution Overview
Problem
Designing and maintaining a proper star schema implementation for business analytics is challenging, especially with modern in-memory databases like SAP HANA, where the link between logical multidimensional data models and database implementation is unclear, leading to performance issues with data load and query performance.
Innovation Solution
A method and system for converting a Data Mart with a snowflake schema to a star schema, specifically using a framework that creates a temporary fact table, copies and inserts data, and eliminates dimension tables, allowing for a simplified metadata-driven approach that reduces the need for complex dimension tables and improves data loading efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a snowflake schema is used in classic RDBMS for business analytics, then data can be organized in a normalized multidimensional structure, but data load time and query performance deteriorate due to complex dimension tables requiring cross-referencing and lookup operations
Solution Approach 1:
The patent extracts and eliminates dimension tables from the traditional snowflake schema, keeping only the fact table with embedded dimension attributes. This removes the complex cross-referencing structure while preserving the multidimensional data organization, directly reducing schema complexity and improving data load performance.
Solution Approach 2:
The patent merges dimension table data directly into the fact table, creating a flattened structure where dimension attributes are stored inline with fact data. This consolidation eliminates the need for separate dimension tables and join operations, reducing both schema complexity and query performance overhead.
2Adaptability or versatility
If traditional star schema implementation is designed and maintained for business analytics, then multidimensional data modeling is achieved, but the strong link between logical model and database implementation creates rigidity and maintenance challenges
Solution Approach 1:
The patent extracts the dimensional structure from the physical database schema and represents it purely as metadata (grouping sets). This separation allows the logical multidimensional model to exist independently from the physical table structure, enabling flexible modeling without rigid implementation constraints and simplifying maintenance.
Solution Approach 2:
The patent segments the star schema into two independent components: a simplified fact table containing all necessary attributes and metadata definitions for dimensional structure. This segmentation decouples the logical model from physical implementation, providing both flexibility and ease of maintenance.
3Productivity
If dimension tables are eliminated in favor of a simplified fact table structure, then data loading efficiency improves, but the ability to maintain normalized data relationships may deteriorate
Solution Approach 1:
The patent introduces metadata (grouping sets) as an intermediary that defines dimensional relationships and data relationships without requiring physical dimension tables. This metadata layer maintains data relationship integrity and normalization principles while allowing the simplified fact table structure to improve data loading efficiency.
Data Source
AI summary
Conversion of a snowflake-like schema to a star-like schema includes a distribution of the snowflake-like schema across several host computer systems. Each host computer system has a fragment of the snowflake-like schema, including a fragment of constituent fact tables. Dimension keys in the fact table fragments are mapped to data tables via the constituent dimension tables. Data table keys are identified and populate the fact table for the converted star-like schema, thus obviating the dimension tables in the converted star-like schema.