Data Orchestration Extension Points for Pipeline Customization
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Solution Overview
Problem
Traditional data processing applications fail to fully leverage and extract meaning from vast and dispersed datasets across organizations, as they are not designed to handle the sheer scale and diversity of data in distributed data landscapes.
Innovation Solution
A data orchestration system that incorporates extension points to modify data-source states in a pipeline, allowing administrators to apply customized functions via before-state-change or after-state-change extension points, enabling efficient data processing and transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data processing applications are used, then system simplicity is maintained, but the ability to leverage and extract meaning from vast and dispersed datasets is insufficient
Solution Approach 1:
The system segments data processing into discrete extension points (before-state-change and after-state-change) that can be independently configured. Each extension point handles specific transformation logic, allowing complex data processing to be divided into manageable, customizable segments without overwhelming the core system architecture.
Solution Approach 2:
The data orchestration system provides a universal framework that supports multiple data sources, formats, and processing requirements through a single platform. The extension point mechanism enables any administrator to customize processing logic for any data source, making the system multi-functional and adaptable to diverse organizational needs without requiring separate specialized systems.
2Productivity
If custom data processing functions are added to handle diverse data, then data processing capability is improved, but system complexity increases
Solution Approach 1:
The system applies custom processing functions at predetermined extension points in the data pipeline (before-state-change and after-state-change). By pre-defining these extension points, the system prepares the infrastructure in advance so that administrators can immediately plug in custom processing logic without having to redesign the entire pipeline architecture, thus improving productivity without proportionally increasing complexity.
Solution Approach 2:
The extension point mechanism acts as an intermediary layer between the core data orchestration system and custom processing requirements. This intermediary interface allows administrators to inject custom processing logic without directly modifying the core system, isolating complexity at the extension point level while maintaining system stability and simplicity at the core level.
3Quantity of substance
If data is integrated from multiple distributed sources, then data comprehensiveness is improved, but data management difficulty increases
Solution Approach 1:
The data orchestration system provides a universal integration framework that handles multiple data sources, formats, and schemas through a single unified interface. The extension point mechanism allows administrators to configure processing logic that works consistently across all data sources, managing diversity and volume without requiring separate management approaches for each source type.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for providing extension points to alter a data source in a data pipeline. By providing a before-state-change extension point, a data orchestration system may allow connected clients to aggregate delta from a changed state source, allowing the data orchestration system to include only changed or added data in a transferred data state. By providing an after-state-change extension point, a data orchestration system may harness additional customizations, for example, effectively and efficiently handle changes to the state made by a viewing client following the transmission of a delta of changes.