Data Linting Ruleset Generation for ETL Pipelines
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Solution Overview
Problem
Users without a technical or programming background find it difficult to manipulate data in ETL/ELT/EL pipelines, as they require a high-level understanding and programming skills, even for simple tasks like validating a field's data type.
Innovation Solution
A system that allows users to create and apply rulesets to databases within ETL pipelines, using a specific transformation language like SQL, enabling users to validate and transform data without needing programming expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users use traditional ETL pipeline tools based on programming languages like Python or SQL, then data manipulation capability is achieved, but user accessibility deteriorates because programming expertise is required
Solution Approach 1:
The patent introduces an intermediary layer (visual interface with drag-and-drop components) between the user and the complex ETL pipeline system. This intermediary translates simple user actions into complex backend operations, allowing non-programmers to manipulate data without writing code while the system handles the programming complexity internally
Solution Approach 2:
The patent replaces the mechanical system of manual programming and code writing with an automated visual interface. Instead of users manually coding ETL operations, the system uses visual components and automated pipeline generation to achieve the same data manipulation goals, eliminating the need for programming expertise
2Productivity
If ETL pipelines use automated data transformation, then productivity is improved, but data quality control worsens without proper validation rules
Solution Approach 1:
The patent implements preliminary action by automatically generating validation rules and quality checks before data transformation occurs. The system pre-configures data quality validation mechanisms as part of the ETL pipeline setup, ensuring that data quality control is embedded in the automation process from the beginning rather than added as an afterthought
Solution Approach 2:
The patent incorporates feedback mechanisms that continuously monitor data quality during automated transformation processes. The system provides real-time feedback on data validation status and automatically adjusts transformation operations to maintain data quality standards, creating a closed-loop control system that balances productivity with reliability
Data Source
AI summary
In the present disclosure, systems and methods are described for allowing a non-code user to create to transform a database in an ETL pipeline. Specifically, as disclosed herein, a user can take a database and receive a ruleset to apply to the database in an ETL pipeline. The data linting system may take the database and extract a schema and a data sample from it. Further, the data linting system may use the schema and data sample to create two rulesets. With these rulesets, the data linting system combines them to create a final ruleset which may be validated using the data sample. The data linting system then sends the final ruleset and the validation report to the user. With this system, the user only needs to give it a database and will receive a ruleset that is able to be immediately used in an ETL pipeline.