Telemetry-Driven Data Orchestration for Adaptive Pipeline Reconfiguration

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Solution Overview

Problem

Existing data management marketplaces are highly fragmented, requiring businesses to invest significant time and resources in building data processing pipelines that are complex, cumbersome, and prone to errors due to inconsistent tools and processes.

Innovation Solution

A data orchestration system that dynamically configures data processing pipelines based on telemetry, allowing for the retrieval, processing, and emission of data through a series of operations, and includes a recommendation engine to suggest optimized data flows using machine learning models.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If businesses build custom data processing pipelines using fragmented data management marketplaces, then they can acquire and process data assets from various sources, but the complexity and time investment increase significantly

Engineering Contradiction:

Improvedata processing capabilityVSAvoidpipeline complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the data processing pipeline into standardized, modular components that can be independently selected and configured. Each component handles a specific data operation (transformation, enrichment, validation, etc.), allowing businesses to assemble pipelines from pre-built blocks rather than creating everything from scratch, thereby reducing overall complexity while maintaining versatility.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system creates a universal data processing framework that can handle multiple data sources, formats, and operations through a single standardized interface. The common data model and unified pipeline architecture enable the same infrastructure to process diverse data assets across different business functions, reducing the need for separate custom pipelines for each use case.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Productivity

If businesses build custom data processing pipelines from fragmented solutions, then they can process data through multiple operations, but the time and resources required increase significantly

Engineering Contradiction:

Improvedata processing throughputVSAvoidpipeline development time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-building and pre-validating pipeline components during system initialization. Data schemas, transformation rules, and processing logic are established in advance through the common data model, so that when actual data processing is needed, the infrastructure is already in place and ready to execute, significantly reducing both development time and processing delays.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses templates and reusable pipeline configurations that can be copied and adapted for different data processing needs. Instead of building unique pipelines for each business requirement, organizations can replicate proven pipeline patterns and modify them as needed, dramatically reducing the time and resources required to deploy new data processing capabilities.

Inventive Principle:

Principle #26Copying

3Adaptability or versatility

If fragmented data management solutions are used, then specific data operations can be performed, but errors increase due to inconsistent tools and processes

Engineering Contradiction:

Improvedata operation capabilityVSAvoidprocessing reliability

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system enforces homogeneity through a common data model that standardizes how data is represented, validated, and processed across all operations. All data assets conform to the same schemas and processing rules, eliminating the inconsistencies that cause errors in fragmented systems. This uniform approach maintains versatility in handling different data types while ensuring consistent, reliable processing outcomes.

Inventive Principle:

Principle #33Homogeneity

Data Source

PatentUS20250363115A1Performance management in data orchestrated environments

Publication Date: 2025.11.27 VIEW SYSTEMS INC

AI summary

This disclosure provides methods, devices, and systems for data management. The present implementations more specifically relate to a data orchestration system that can dynamically reconfigure a data processing pipeline based on telemetry received from various steps or data operations in the pipeline. For example, the telemetry may indicate a success, failure, time of entry, time of exit, or total duration of a given step or data flow in the processing pipeline. In some aspects, the data orchestration system may dynamically invoke new data flows based on the received telemetry. In some implementations, the new data flows may allocate additional memory and/or processing resources for the data processing pipeline. In some other implementations, the new data flows may deallocate memory and/or processing resources for the data processing pipeline. Still further, in some implementations, the new data flows may trigger an alert to a user or manager of the data processing pipeline.