Vendor-Agnostic Data Pipeline Language for Portable ETL Workflows
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Solution Overview
Problem
Existing data integration processes, such as ETL and ELT, struggle with vendor lock-in issues due to the lack of portability and compatibility across different data sources and processing components, particularly in cloud computing environments, limiting the flexibility and scalability of data pipelines.
Innovation Solution
A domain-specific language (DSL) for creating data pipelines that abstracts away vendor-specific technologies, allowing for human-readable configuration files and automated mapping to third-party components, ensuring compatibility and portability across various data sources, warehouses, and processing components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a graphical user interface is used to build data pipelines, then ease of operation is improved, but device complexity increases due to the need for visual drag-and-drop components and configuration interfaces
Solution Approach 1:
The patent introduces an intermediary translation layer that converts high-level domain-specific language commands into low-level vendor-specific API calls. This mediator handles the complexity of vendor-specific protocols while presenting a simplified interface to users, resolving the contradiction between ease of operation and device complexity.
Solution Approach 2:
The patent extracts the complexity of vendor-specific configurations and API protocols into a separate translation module. By isolating these complex elements from the user interface, the system maintains simplicity for end users while still supporting multiple vendors through the extracted translation layer.
2Manufacturing precision
If vendor-specific configurations are used for data pipeline components, then manufacturing precision is improved through optimized vendor-specific parameters, but adaptability deteriorates because pipelines cannot be easily ported between different vendors
Solution Approach 1:
The patent segments the configuration into two distinct layers: a vendor-agnostic domain-specific language layer that maintains precision through optimized parameters, and a vendor-specific implementation layer that handles portability. This segmentation allows each layer to optimize for its specific purpose without compromising the other.
Solution Approach 2:
The patent uses parameter changes by maintaining abstract parameter definitions in the domain-specific language that can be automatically mapped to different concrete parameter sets for different vendors. This allows the same high-level configuration to adapt to different vendors through parameter transformation rather than requiring complete reconfiguration.
3Adaptability or versatility
If abstract domain-specific language is used to define data pipelines, then adaptability is improved for vendor-agnostic definitions, but manufacturing precision may deteriorate due to loss of vendor-specific optimization details
Solution Approach 1:
The translation module serves as an intermediary that preserves manufacturing precision by automatically mapping abstract domain-specific language parameters to vendor-optimized specific parameters. This mediator ensures that no optimization details are lost during the abstraction process.
Solution Approach 2:
The system performs preliminary action by pre-defining parameter mappings and transformations between the domain-specific language and vendor-specific configurations. This preliminary setup ensures that precision is maintained through automated, pre-validated parameter translations rather than manual configuration.
4Manufacturing precision
If manual configuration of each data pipeline component is required, then manufacturing precision is improved through detailed parameter control, but productivity deteriorates due to increased time and effort for configuration
Solution Approach 1:
The system enables self-service by allowing users to define data pipelines using high-level domain-specific language commands that automatically generate detailed vendor-specific configurations. This self-service approach maintains precision through automated parameter mapping while dramatically improving productivity by eliminating manual configuration of each component.
Solution Approach 2:
The translation layer performs preliminary action by pre-configuring parameter mappings and transformations, so that when users define pipelines in the domain-specific language, the detailed parameter control is automatically applied without requiring manual intervention, thus maintaining precision while improving productivity.
Data Source
AI summary
A domain specific language, the “data pipeline language” (DPL), has been developed for programmatic creation of data pipelines for ETL/ELT with a human-readable format. The DPL syntax is abstracted from the specific technologies used for building a data pipeline, so the DPL that defines various components of a data pipeline (e.g., data sources, data warehouses, and/or cloud-based processing components) is independent of the specific vendors used to provide these components. A user that defines a data pipeline in the DPL specifies any external vendors that will be leveraged for execution of the data pipeline in a configuration file. During execution of a data pipeline defined in with the DPL, a pipeline execution platform maps data pipeline components identified from the DPL file to the respective external vendor(s) indicated in the configuration and “translates” the DPL components into their corresponding commands that are sent to the external vendor(s).