Metadata-Driven Data Flow Pipeline Configuration
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Solution Overview
Problem
The development of data engineering solutions for machine learning models is complex due to the involvement of multiple teams, lack of clear handovers, and challenges in testing, versioning, and deploying pipelines across hybrid environments.
Innovation Solution
A configuration-based framework that enables plug-and-play modification and creation of data flow pipelines, hiding complexity and reducing boilerplate code, while allowing for easy sharing and understanding of pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If configuration-based framework is used for pipeline development, then productivity and ease of operation are improved, but device complexity increases due to the need for metadata management and component orchestration systems
Solution Approach 1:
The patent uses metadata files (configuration copies) to represent pipeline definitions instead of requiring engineers to write actual pipeline code. The metadata serves as a simplified copy that can be version-controlled and shared, while the framework automatically generates the actual pipeline execution code, thereby improving productivity without requiring engineers to master complex pipeline coding
Solution Approach 2:
The framework introduces a metadata intermediary layer between the pipeline engineer and the actual pipeline execution. The metadata file acts as a mediator that captures essential pipeline characteristics in a simplified format, and the framework's code generation system translates this metadata into executable pipeline code, resolving the contradiction by hiding complexity behind this intermediary interface
2Ease of operation
If code-based pipeline development is used, then manufacturing precision and reliability are maintained, but ease of operation and productivity deteriorate due to boilerplate code and complex coding tasks
Solution Approach 1:
The framework enables self-service pipeline generation by automatically creating pipeline code from metadata configurations. The system serves itself by generating the necessary boilerplate code, data flow definitions, and stage configurations automatically, eliminating the need for engineers to manually write repetitive code while maintaining configuration accuracy through structured metadata schemas
Solution Approach 2:
The framework performs preliminary actions by pre-defining pipeline templates, stage configurations, and data flow patterns in the metadata. Common pipeline structures are prepared in advance with best practices embedded, allowing engineers to simply select and customize pre-vetted configurations rather than creating pipelines from scratch, thereby maintaining precision while improving ease of operation
3Adaptability or versatility
If traditional data engineering approaches are used, then adaptability to different environments is achieved, but device complexity and difficulty of detecting and measuring pipeline issues increase
Solution Approach 1:
The patent segments the pipeline into distinct, independently configurable stages (data collection, preparation, processing, prediction, distribution) that can be defined in metadata and executed separately. Each stage has explicit input-output contracts defined in the metadata, making it easier to detect issues at specific stages without having to trace through complex end-to-end data flows, while maintaining adaptability through configurable stage parameters
Data Source
AI summary
This disclosure describes techniques relating to development of data flow pipelines. In one example, this disclosure describes a method that includes accessing, by a computing system, metadata identifying characteristics of a data flow pipeline; generating, by the computing system and based on the metadata, the data flow pipeline, wherein the data flow pipeline includes a plurality of stages, and wherein the plurality of stages includes a multi-sourced stage in which data output by each of a subset of stages in the plurality of stages are used as input to the multi-sourced stage; executing, by the computing system, the data flow pipeline; detecting, by the computing system, modifications to the metadata; generating, based on the modifications, an updated data flow pipeline; and executing, by the computing system, the updated data flow pipeline.