Automated Data Lake Parser and Object Class Generation
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Solution Overview
Problem
Integrating large amounts of diverse data into a data lake is time-consuming due to the need for manual data format discernment and object creation, which requires significant manual effort and is inefficient for ongoing data integration needs.
Innovation Solution
An automated method using a computing device to process input data samples, identify fields, determine data types, and generate object classes and parsers to automatically integrate data into a data lake, with unit tests ensuring proper parsing and data handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data format discernment and object creation is performed, then data integration accuracy is maintained, but integration time and manual effort increase significantly
Solution Approach 1:
The system performs self-service by automatically discerning data formats and generating appropriate object classes without human intervention. The data itself provides the information needed to determine its format through pattern recognition and metadata extraction, eliminating the need for manual format identification while maintaining accuracy.
Solution Approach 2:
The manual mechanical process of data format discernment and object creation is replaced with an automated computational system. The system uses algorithms to analyze data patterns, identify formats, and generate object classes programmatically, substituting human manual work with automated software processes that maintain precision while dramatically reducing time.
2Reliability
If manual object creation is performed for each data source, then data integration reliability is ensured, but productivity decreases due to repetitive manual effort
Solution Approach 1:
Instead of manually creating unique objects for each data source, the system creates template object classes that can be copied and adapted for multiple data sources. Once a object class template is generated for a particular data format, it can be reused and instantiated for multiple similar data sources, ensuring consistency and reliability while dramatically increasing productivity through template-based generation.
Solution Approach 2:
The system creates universal object class templates that can handle multiple data sources with the same format characteristics. These templates serve multiple functions by accommodating various instances of similar data types, ensuring reliable integration across multiple sources while reducing the need for repetitive manual object creation for each individual data source.
3Measurement precision
If comprehensive data format analysis is performed manually, then parsing accuracy is improved, but the complexity of the integration process increases
Solution Approach 1:
The complex data format analysis process is segmented into distinct automated stages: data sampling, pattern recognition, format identification, object class generation, and parser creation. Each segment handles a specific aspect of the analysis, reducing the perceived complexity by breaking down the overall process into manageable, automated steps that collectively achieve high parsing accuracy.
4Productivity
If automated parser generation is implemented, then integration speed increases, but the difficulty of ensuring proper data handling increases
Solution Approach 1:
The system implements feedback mechanisms where generated parsers are automatically tested against sample data from the source systems. Unit tests are created to verify parser correctness, and the system uses the results of these tests to refine and validate the generated code. This feedback loop ensures that automated parser generation maintains high accuracy while preserving integration speed.
Data Source
AI summary
Exemplary embodiments may provide an automated approach for processing an input data sample to yield a set of object classes, a parser and one or more unit tests for input data that is to be integrated into a data lake. The objects may be readily queried and, in some instances, may be Plain Old Java Objects (POJO's). The exemplary embodiments may process an input data sample to better understand the format of the input data. The input sample may be processed to identify entities, such as records, objects or the like, in the input data sample. The input data sample may be processed on a line by line basis to identify fields in the entities. Once the format of the input data is determined from the input data sample, a parser may be generated to parse the input data.