Containerized Runtime Environments for Big Data Pipelines
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Solution Overview
Problem
Traditional data processing applications are inadequate for handling exceptionally voluminous and complex 'big data' sets, requiring advanced capabilities for ingestion, cleansing, storage, analysis, sharing, transformation, and visualization.
Innovation Solution
A system that customizes an operator node in a graph representing a data processing pipeline by identifying and executing dockerfiles based on tags associated with the pipeline, generating an executable package that includes necessary components for executing data processing operations, providing a tailored runtime environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data processing applications are used, then the system structure is simple, but the application is inadequate for handling big data sets
Solution Approach 1:
The patent segments the data processing system into distinct operator nodes (ingestion, cleansing, storage, analysis, sharing, transformation, visualization) that can be independently selected and configured. Each operator node handles a specific big data processing function, allowing the system to build complexity only where needed for big data operations while maintaining simplicity in non-big data scenarios.
Solution Approach 2:
The patent creates a universal data processing application framework that can function in both traditional and big data environments. The system provides multi-functionality through optional operator nodes that can be activated based on whether big data processing is required, allowing the same base application to adapt to different data volume and complexity requirements.
2Productivity
If a customized runtime environment is provided for big data processing, then processing efficiency is improved, but resource usage increases
Solution Approach 1:
The patent applies local quality by providing customized runtime environments and operator nodes only for specific big data processing operations where they are needed. Each operator node can be configured with appropriate runtime libraries, environment variables, and dependencies only for its specific function (e.g., ingestion operators have different runtime requirements than visualization operators), avoiding the need to allocate resources for all possible operations simultaneously.
Solution Approach 2:
The system dynamically adjusts resource allocation based on the specific big data processing tasks being executed. The runtime environment configuration is not static but adapts to the current operational requirements, activating only the necessary operator nodes and their associated resources for each big data processing workflow, thereby improving processing efficiency while controlling resource consumption.
Data Source
AI summary
A method for executing a data processing pipeline may be provided. The method may include identifying a file providing a runtime environment required for executing a series of data processing operations comprising the data processing pipeline. The file may be identified based on one or more tags associated with the data processing pipeline. The one or more tags may specify at least one runtime requirement for the series of data processing operations. The file may be executed to generate an executable package that includes a plurality of components required for executing the series of data processing operations. The series of data processing operations included in the data processing pipeline may be executed by at least executing the executable package to provide the runtime environment required for executing the series of data processing operations. Related systems and articles of manufacture, including computer program products, are also provided.