Multi-Hub Dataset Architecture for Data Integration
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Solution Overview
Problem
Enterprise users face challenges in extracting and integrating data from horizontal and vertical business applications into a data warehouse, a process that is both time and resource intensive, especially in cloud and SaaS environments.
Innovation Solution
A system and method for providing multi-hub and/or multi-table datasets within a data analytics environment, enabling efficient data transformation, enrichment, and analysis by using a hub table as a fact table that can be joined with other tables, and supporting data visualization and business intelligence applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data extraction and integration methods are used from horizontal and vertical business applications into a data warehouse, then data can be integrated, but the process is time and resource intensive
Solution Approach 1:
The patent segments data integration by introducing hub tables that act as intermediate fact tables. Instead of directly integrating all tables, the system divides the integration process into modular segments where hub tables serve as central connection points for joining multiple dimension tables, thereby streamlining the extraction and integration workflow
Solution Approach 2:
The patent employs hub tables as intermediary structures between source systems and the data warehouse. These hub tables act as mediators that simplify complex many-to-many relationships by providing standardized join interfaces, reducing the computational complexity and time required for data integration operations
2Productivity
If traditional data extraction and integration methods are used from horizontal and vertical business applications into a data warehouse, then data can be integrated, but resource requirements are high
Solution Approach 1:
By segmenting the data model into hub tables and dimension tables with clear separation of facts and dimensions, the system reduces computational overhead. Each hub table handles specific fact types independently, allowing for more efficient processing and lower resource consumption during data integration and query operations
Solution Approach 2:
The patent implements preliminary action by pre-defining hub tables with standardized schemas and join relationships before data integration occurs. This pre-structuring of the data model eliminates the need for complex runtime computations to determine relationships, thereby reducing computational resources required during actual data extraction and integration operations
3Productivity
If hub tables are used as fact tables to enable joining with other tables, then data transformation and enrichment efficiency is improved, but system complexity increases
Solution Approach 1:
Hub tables serve multiple functions simultaneously: they act as fact tables storing metrics, provide join interfaces for dimension tables, enable data transformation, and support business logic implementation. This multi-functionality reduces the need for separate specialized structures, actually simplifying the overall system despite the enhanced capabilities
Data Source
AI summary
In accordance with an embodiment, described herein is a system and method for providing multi-hub and/or multi-table datasets with a computing environment such as, for example, a business intelligence environment, database, data warehouse, or other type of environment that supports data analytics. An analysis can be used to query data to provide information in the form of tables, graphs, pivot tables, or other data views. A hub table operates as fact table and carries the data metrics for analysis, enabling a user to join two tables, the data from which can be further transformed or enriched to prepare it for analysis.