Auto-ETL Accelerator for Azure Data Factory Pipeline Automation
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Solution Overview
Problem
Existing data management solutions, such as Azure Data Factory, face complexity and inefficiency in developing and testing new data pipelines, especially when dealing with large implementations that require thousands of datasets, leading to prolonged development and testing times.
Innovation Solution
An auto-ETL accelerator tool that selects and configures the most common data operations, reduces the need for repetitive pipeline creation, and employs a finite set of Azure Data Factory pipelines to streamline data processing, eliminating the need for extensive development and testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a pipeline is created for each dataset in Azure Data Factory, then data processing functionality is achieved, but development and testing time increases significantly
Solution Approach 1:
The patent implements a universal template system where a single ETL pipeline template can be configured to process multiple datasets through parameterization. Instead of creating separate pipelines for each dataset, the system allows one template to serve multiple datasets by accepting configuration parameters, thereby reducing development time while maintaining the ability to process diverse data sources
Solution Approach 2:
The patent applies preliminary action by pre-configuring pipeline templates with common ETL operations, transformations, and settings before actual data processing begins. The template structure, including extract, transform, and load operations, is prepared in advance and can be quickly instantiated for different datasets, eliminating the need to build pipelines from scratch each time
2Quantity of substance
If thousands of pipelines are created to handle large implementations, then comprehensive data coverage is achieved, but system complexity increases
Solution Approach 1:
The patent reduces system complexity by replacing thousands of individual pipelines with a small set of reusable templates. Each template can handle multiple datasets through parameter configuration, transforming a many-to-many relationship into a many-to-one relationship that is far easier to manage, maintain, and understand
Solution Approach 2:
The patent merges multiple similar pipeline functions into a single template structure. By combining common ETL operations, transformation logic, and configuration patterns into one unified template, the system achieves comprehensive data coverage without the complexity of managing numerous separate pipelines
3Adaptability or versatility
If new pipelines are developed for new datasets, then data processing capability is maintained, but maintenance effort increases
Solution Approach 1:
The patent makes pipeline maintenance easier by creating a universal template that handles multiple datasets. When changes are needed in ETL logic, transformations, or configurations, modifications are made once in the template rather than in numerous individual pipelines, automatically propagating to all datasets that use the template
Solution Approach 2:
The patent uses template copying where a single validated pipeline template can be copied and instantiated for new datasets. This ensures consistency across all pipelines while reducing maintenance burden, as the template serves as a single source of truth that can be replicated without manual recreation of each pipeline
Data Source
AI summary
A software development accelerator tool having an automatic extract, transform and load accelerator is described that can be employed for building, testing, deploying and managing software applications and services. Employ the accelerator can include selecting software development operations from a multiplicity of preconfigured existing operations; ordering operations as part of a configuration; determining whether dependencies are met for operations before processing the configuration; generating a batch ID to track the processing; and performing operations in the order specified in the configuration.