Data Orchestration System Real-Time State Propagation
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Solution Overview
Problem
Traditional data processing applications struggle to fully leverage and extract meaning from the vast and dispersed datasets in modern organizations, as they are not equipped to handle the sheer scale and variety of data across distributed data landscapes, requiring specialized approaches for data ingestion, transformation, and collaboration.
Innovation Solution
A data orchestration system that incorporates data processing pipelines to refine, analyze, and modify data, allowing administrators to design user interfaces and propagate changed data-source states to front-ends and repositories, using operators that can transform and visualize data, and manage connections through a connection manager to facilitate real-time updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data processing applications are used, then system simplicity is maintained, but the ability to handle vast and dispersed datasets is insufficient
Solution Approach 1:
The system segments the distributed data landscape into multiple data sources that can be independently managed and processed. Each data source is treated as a separate entity that can be connected to the orchestration system individually, allowing the system to handle vast quantities of dispersed data without requiring monolithic processing capabilities.
Solution Approach 2:
The data orchestration system acts as an intermediary layer between traditional data processing applications and the distributed data landscape. This intermediary manages data ingestion, transformation, and distribution, enabling traditional applications to access and process vast quantities of dispersed data without direct complexity.
2Loss of information
If data orchestration systems integrate vast amounts of data from distributed sources, then data extraction capability is improved, but system complexity increases
Solution Approach 1:
The data orchestration system provides universal functionality for managing diverse data sources through standardized interfaces and operations. By implementing a unified framework that can handle multiple data types, formats, and sources through common data ingestion and transformation mechanisms, the system reduces the apparent complexity despite integrating vast amounts of distributed data.
Solution Approach 2:
The system creates virtual representations or copies of data source states that can be manipulated and analyzed without directly modifying the underlying distributed data sources. This copying mechanism allows efficient data extraction and transformation while maintaining the integrity and independence of the original data sources, thereby managing complexity.
3Productivity
If real-time data propagation is implemented across multiple front-ends, then collaboration efficiency is improved, but network overhead increases
Solution Approach 1:
The system extracts and propagates only the specific data source state changes that are relevant to each front-end user, rather than transmitting all data changes universally. This selective extraction of necessary information reduces network overhead while maintaining real-time collaboration efficiency by delivering only the data updates that each user needs.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for providing access to changed states in a data source to front-ends and back-ends in a data orchestration system. When displaying a scene including transformed data, a data orchestration system may receive a change to the data source from the data repository or a client viewing the data. The data orchestration system may transform data based on configured data pipelines and propagate the changed state to all viewing clients and to the backend data repository. By managing the connections, pipelines, operators, data sources, clients, backends, and other components, the data orchestration system may provide intelligent data solutions and advanced analytic capabilities across the entirety of an organization's data landscape.