Data Streaming Pipeline for Remote Compute Mapping
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Solution Overview
Problem
Accessing and processing vast quantities of data is challenging due to storage and transmission inefficiencies, particularly in remote locations, and existing systems face difficulties in integrating and processing data from diverse sources with different filing and storage procedures, leading to high costs and time consumption.
Innovation Solution
A data pipeline system that enables direct streaming and mapping of data to compute services using pre-trained models, allowing for efficient data processing and integration across various data sources, including remote locations, while maintaining data at its original storage, and incorporating just-in-time data loaders and pre-trained libraries for seamless execution of compute operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is transmitted from remote locations for analysis, then data can be accessed and processed, but transmission costs and time consumption increase significantly
Solution Approach 1:
The patent introduces a data pipeline as an intermediary component that enables direct streaming of data from remote sources to compute services without requiring full data transmission. The pipeline acts as a mediator that facilitates data access while minimizing the actual data movement, thus reducing transmission costs and time while maintaining data accessibility.
Solution Approach 2:
The system performs preliminary actions by establishing data pipelines and pre-processing data streams before they reach the compute services. Data is prepared and staged in the pipeline in advance, allowing compute services to consume data efficiently without requiring costly and time-consuming transmission of complete datasets.
2Adaptability or versatility
If data from multiple users with different storage procedures is integrated, then comprehensive analysis is enabled, but data organization and processing complexity increase
Solution Approach 1:
The data pipeline is designed as a universal intermediary that can handle multiple data sources with different storage procedures and formats. It provides a standardized interface for ingesting diverse data streams, normalizing them into a common format that compute services can process uniformly, thus enabling comprehensive multi-user data integration without proportionally increasing processing complexity.
3Quantity of substance
If vast quantities of data are transmitted locally, then complete data is available for analysis, but transmission time and resources are excessive
Solution Approach 1:
The system extracts only the essential data elements needed for analysis from the complete datasets. The data pipeline enables selective streaming of relevant data portions to compute services, extracting necessary information while leaving redundant or less critical data at remote storage locations, thus maintaining analytical completeness while dramatically reducing transmission time.
Data Source
AI summary
Approaches presented herein provide systems and methods for a data platform to identify, evaluate, and map data for processing via one or more compute instances. The data platform may receive an instruction and retrieve data from a variety of remote data locations. The data may be processed, such as to label or file the data, and then streamed to a compute instance for further evaluation. The data platform may be used to provide a centralized system for managing system data that combine both legacy and modern storage solutions into a single integrated platform.