Datasource Creation via Embedded Application Metadata
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Solution Overview
Problem
Cloud-based analytics applications fail to function properly when transported from development to production systems due to the absence of metadata table entries and associated views in the production database, causing data object usage failures.
Innovation Solution
A computer system and method that loads a data object with application metadata, sends a metadata call to a database, extracts query metadata, initiates datasource creation, and queries the database to obtain results, ensuring seamless data source creation behind the scenes and preventing user errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data objects are transported from development system to production system, then data objects can be used in production environment, but metadata table entries and associated views are not available in production database causing data object usage to fail
Solution Approach 1:
The system performs preliminary actions by detecting the absence of metadata during data object loading and automatically creating the necessary metadata table entries and associated views in the production database before the data object can be used. This preliminary metadata creation ensures that when data objects are transported from development to production system, the required metadata infrastructure is already in place, preventing functionality failures.
2Reliability
If manual metadata creation is required in production system, then metadata can be properly established, but user intervention is needed causing operational complexity and potential errors
Solution Approach 1:
The system implements self-service by automatically detecting when metadata table entries and views are missing in the production database and autonomously creating them without requiring any user intervention. The embedded application monitors metadata availability and triggers automatic metadata creation processes, eliminating manual operations and reducing the potential for user errors while ensuring complete and accurate metadata establishment.
3Device complexity
If metadata is not automatically created, then system complexity is reduced, but data source availability fails causing application functionality to break
Solution Approach 1:
The system introduces an intermediary metadata detection and creation mechanism that acts as a bridge between the embedded application and the production database. This intermediary component monitors whether required metadata table entries and views exist, and if not, automatically creates them. This intermediary layer adds minimal complexity while ensuring data source availability, as it only activates when needed and operates transparently to the main application functionality.
Data Source
AI summary
An application embedded within a cloud application may load a data object which accesses information from a database to generate visualizations of data. When the data object is transported from a development system to a production system, the database may not have table entries and views as expected and loading the data object might fail. In such cases, the datasource may be created from metadata. A first metadata call is sent to the database using the application metadata and an indication that a datasource was not found is received from the database. Query metadata is extracted from the application metadata of the data object and a datasource creation call is sent to the database using the extracted query metadata, thereby initiating creation of a datasource artifact in the database. Then, query results are obtained based on the datasource artifact and query results are provided in the cloud application.