Declarative Data Warehouse Modeling for Faster ETL Buildout
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Solution Overview
Problem
Existing data warehousing systems face challenges in integrating and homogenizing data from different sources, managing time-variant data, and developing complex transformations, requiring manual expert intervention and extensive computational resources, leading to inefficient database development and analysis.
Innovation Solution
Adaptable data warehousing systems utilize a declarative scripting engine and domain-specific language (DSL) to automatically generate logical and physical data models and ETL processes, enabling users to model and build databases through a graphical user interface without specialized knowledge, reducing computational intensity and errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual expert intervention is used for data modeling and ETL process development, then data integration and transformation quality is improved, but development time and computational resource consumption increase significantly
Solution Approach 1:
The system enables self-service data modeling where users can define conceptual data models using a graphical interface without requiring expert knowledge. The automated model generation process allows the system to serve itself by translating user-defined concepts into complete ETL processes and physical data models, eliminating the need for manual expert intervention in technical modeling tasks.
Solution Approach 2:
The patent replaces manual mechanical processes of data modeling and ETL development with an automated computational system. The declarative scripting engine and automated model generation mechanisms substitute for manual expert work, transforming the mechanical process of building data warehouses from manual coding and configuration to automated generation based on high-level conceptual definitions.
2Manufacturing precision
If manual expert intervention is used for ETL process development, then transformation accuracy is improved, but computational resource consumption and complexity increase
Solution Approach 1:
The system replaces complex manual ETL development with automated computational processes. The declarative scripting engine automatically generates transformation logic based on conceptual data models, substituting manual programming tasks with automated code generation. This maintains transformation accuracy while significantly reducing the complexity of system development and maintenance.
Solution Approach 2:
The patent introduces a declarative scripting engine as an intermediary between user conceptual definitions and actual ETL implementations. This intermediary layer automatically translates high-level conceptual models into detailed transformation processes, acting as a mediator that preserves accuracy while simplifying the overall system architecture by abstracting away implementation details.
3Manufacturing precision
If traditional data modeling methods are used, then data model accuracy is improved, but development efficiency and productivity decrease
Solution Approach 1:
The system enables users to directly define conceptual data models through intuitive graphical interfaces without requiring specialized modeling skills. The self-service capability allows business users to create accurate data models by simply defining their conceptual requirements, while the system automatically handles the technical translation to accurate physical models and ETL processes, thereby improving both accuracy and productivity.
Solution Approach 2:
The patent performs preliminary automated generation of physical data models and ETL processes based on conceptual definitions. By preparing the technical implementation details in advance through automated model generation, the system eliminates the time-consuming manual steps of creating detailed models and transformation processes, thereby significantly improving development efficiency while maintaining accuracy through automated validation.
Data Source
AI summary
Adaptable data warehousing computing systems and methods are disclosed for improved and flexible declarative control for programmatic modeling and database building, including in compute intense environments. A declarative scripting engine executes computing commands defined in a domain specific language (DSL), which is configured to programmatically configure a conceptual data model defining business object(s). A logical data model may be generated based on a declarative command defining the conceptual data model. A physical data model may be generated based on the logical data model, where the physical data model defines stage, core layer, and/or a data mart layers that may be deployed to a target database or data warehouse, and where the stage, core layer, and/or a data mart layers are the same or similar to those of the logical data model.