Data Transformation Pipeline Optimization via Satellite Data Segmentation
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Solution Overview
Problem
Existing ETL processes are inefficient due to the need to copy and process entire data sets through multiple transformation operations, leading to excessive memory reads and writes, which increases computational costs and reduces data throughput.
Innovation Solution
The data transformation pipeline optimizes by identifying and removing 'satellite data' that is not required for each transformation, maintaining links to ensure no information is lost, and patching the output with the removed data as needed, thereby reducing unnecessary data copying and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire data set is copied and passed through each transformation operation, then all transformation operations can be performed, but the number of memory reads and writes increases excessively
Solution Approach 1:
The patent segments the data set into two distinct parts: satellite data (columns not required by the transformation) and non-satellite data (columns required by the transformation). This segmentation allows the system to process only the necessary non-satellite data through each transformation operation, eliminating redundant processing of satellite data while ensuring all required transformations are completed.
Solution Approach 2:
The patent extracts satellite data from the input buffer before processing and stores it separately. By taking out the satellite data that is not needed for the current transformation, the system reduces the amount of data that needs to be read and written during processing, while preserving the satellite data for potential later use in other transformations.
2Productivity
If satellite data is removed from the input buffer, then computational resources are reduced, but data completeness may be compromised
Solution Approach 1:
The patent performs preliminary action by identifying and removing satellite data from the input buffer before the transformation operation begins. This preliminary removal reduces the data volume that needs to be processed, improving throughput. The satellite data is preserved separately so it can be restored later if needed, thus maintaining data completeness without compromising processing efficiency.
3Adaptability or versatility
If data is copied multiple times through transformation operations, then all transformations can be applied, but the number of memory operations increases
Solution Approach 1:
The patent segments data handling into two pathways: satellite data is extracted once and preserved separately, while non-satellite data is processed through transformations. This segmentation eliminates the need to copy and process satellite data multiple times through each transformation operation, significantly improving processing efficiency while maintaining the flexibility to apply various transformations to the non-satellite data.
Data Source
AI summary
A system, method and computer-readable medium for data transformation pipeline optimization including storing a transformation pipeline comprising data transformation components having associated input buffers and associated output buffers and being configured to apply data transformations to data in the associated input buffers and output results of the data transformations to the associated output buffers, receiving a data set comprising data fields, identifying satellite data fields for at least one transformation component, each satellite data field comprising data that is not utilized during the data transformations of the transformation component, and processing, by the data transformation engine, the data set through each transformation component in the transformation pipeline, the processing including removing satellite data fields from the input buffers, linking the removed satellite data fields to the remaining data in the input buffers, and applying the data transformations to data in input buffers and writing results to output buffers.