Multi-step BI Code Generation for Cross-Cloud ETL
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Solution Overview
Problem
Business intelligence operations across different data storage and reporting requirements within an organization are challenging due to varying database schemas and formats, necessitating a solution that can manage BI operations across multiple cloud environments and on-premise systems efficiently.
Innovation Solution
A model-driven, tool-agnostic multi-step code generation approach is employed by a BI administrator to manage BI operations, utilizing a software stack that includes a modeler, metadata repository, and application configurator to define schemas, perform ETL transformations, and generate executable jobs, allowing for cross-cloud BI processing without requiring sophisticated BI tools at the cloud-based application provider.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a company maintains its own data management applications and hardware resources, then it can have full control over BI operations, but the expense and complexity increase considerably
Solution Approach 1:
The patent introduces a cloud-based application as an intermediary between the company's BI needs and the underlying infrastructure. The company interacts with the cloud application through standardized interfaces, while the cloud provider manages the complex hardware and software resources, thus maintaining control without direct infrastructure management.
Solution Approach 2:
The patent enables companies to access and use cloud-based data management applications that replicate the functionality of on-premise solutions. Through virtualization and cloud deployment, the same BI capabilities are made available without requiring physical ownership of the underlying infrastructure.
2Adaptability or versatility
If different departments use separate databases with different schemas, then each department can have customized data organization, but integration and reporting across departments becomes challenging
Solution Approach 1:
The cloud-based data management application provides universal data access and integration capabilities that work across multiple departments and database types. It implements a unified data model and standardized interfaces that enable cross-departmental reporting while preserving individual departmental data organization requirements.
Solution Approach 2:
The cloud application serves as an intermediary layer between departmental databases with different schemas. It provides data virtualization and transformation capabilities that enable integrated reporting without requiring physical consolidation of the underlying databases.
3Device complexity
If cloud-based applications are used to reduce complexity, then hardware and software expenses are reduced, but the ability to perform sophisticated BI operations may be limited
Solution Approach 1:
The cloud-based application implements dynamic resource allocation and scalable architecture that allows BI capabilities to adapt to varying complexity requirements. Companies can start with basic functionalities and scale to sophisticated BI operations as needed, paying only for the capabilities they actually use.
Solution Approach 2:
The patent segments BI capabilities into modular components that can be selectively deployed. The cloud application provides a tiered architecture where basic data management is available to all users, while advanced BI operations are available as optional add-on services, allowing companies to balance complexity and capability according to their needs.
Data Source
AI summary
A source computing environment administering business intelligence (BI) processing at a target computing environment. The source computing environment storing intermediate design plans for creating dimensional models, reports, and ETL jobs for deployment in a plurality of different computing environments. At the source computing environment, target specific modules translating the one or more intermediate design plans into one or more target specific plans for dimensional models, reports, and ETL job plans which are interpretable or executable by the target computing environment. The source computing environment, in consultation with a workflow engine, instructing the target computing environment to provision data repositories, create reports, and perform ETL processing based on the one or more target specific plans.