Extensible Data Pipeline Manager for Modular Transform Configuration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data processing systems face challenges in handling atypical data requirements without increasing regression testing risks and maintaining additional code bases, leading to deployment difficulties.
Innovation Solution
An extensible data pipeline manager (EDPM) that detects incoming data and initiates a sequence of transforms, allowing for custom plugins and built-in functionality to handle specific operations, with a configuration API for defining data pipelines and supporting unlimited data sources and destinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom plugins are added to handle atypical data requirements, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system segments data processing functionality into modular transforms that can be independently configured and executed. Each transform handles a specific operation (filtering, formatting, enrichment) and can be arranged in configurable sequences, allowing custom plugins to be added without affecting core functionality.
Solution Approach 2:
The backend system provides a universal data processing framework that handles both standard and custom data requirements through a common transform execution engine. This engine can process built-in transforms and custom plugin transforms uniformly, eliminating the need for separate code bases.
2Adaptability or versatility
If core software is modified to handle specific user needs, then adaptability is improved, but reliability deteriorates due to increased regression testing risks
Solution Approach 1:
The system transitions from static core software to a dynamic configuration model where data processing behavior is defined by configurable transform sequences. Users can modify processing logic by changing pipeline configurations rather than core code, and these changes can be tested independently before deployment.
Solution Approach 2:
The system uses template-based transform definitions that can be copied and reused across different data sources and destinations. Custom plugins are registered as reusable transform templates that can be instantiated multiple times without modifying the underlying core software.
3Adaptability or versatility
If additional code bases are maintained for different use cases, then adaptability is improved, but ease of manufacture deteriorates due to deployment difficulties
Solution Approach 1:
The system merges multiple use-case-specific code bases into a single unified backend platform. Different data processing requirements are handled by configuring different transform sequences within the same execution engine, eliminating the need to maintain separate code bases for different use cases.
Solution Approach 2:
The system uses parameter-based configuration to adapt behavior for different use cases. Transform sequences are defined with configurable parameters that can be adjusted to handle various data sources, formats, and destinations without changing the underlying code structure.
Data Source
AI summary
Disclosed is an extensible data pipeline. The extensible data pipeline can include detecting, by a backend system, incoming data at a spigot. The backend system can initiate a sequence of transforms upon detecting the incoming data. Each of the transforms can be operative to perform an operation on the data and output both transformed data and a status identifier. The status identifier can indicate a status of a transform operation for a respective one of the transforms. The transformed data from a final transform can be stored along with a final identifier indicating a status of the final transform.