ETL Processing Objects Reduce Resource Consumption
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Solution Overview
Problem
Existing data management technologies require excessive system resources and time when processing large amounts of data from diverse sources, making them inefficient for handling and transforming data for storage and usage.
Innovation Solution
A data management system that employs processing objects, in-memory processes, and batch processing to efficiently perform Extract, Transform, and Load (ETL) activities by identifying dependencies and sequencing tasks, using discrete data structures and in-memory data structures to reduce resource usage and processing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data management technologies are used to process large amounts of data from different sources, then data transformation and loading can be completed, but excessive system resources (processing capacity, memory capacity) and time are required
Solution Approach 1:
The patent segments the ETL process into distinct processing objects (extract objects, transform objects, load objects) that can be independently managed and executed. Each processing object handles specific data transformation tasks, allowing the system to process data in manageable units rather than as a monolithic operation, thereby reducing memory capacity requirements and improving processing efficiency.
Solution Approach 2:
The patent implements preliminary action by analyzing ETL instructions and identifying activity dependencies before actual data processing begins. The system determines the processing sequence in advance, creating a structured plan that guides subsequent data extraction, transformation, and loading operations. This preliminary analysis reduces processing time and optimizes resource utilization during the actual ETL execution.
2Productivity
If traditional data management technologies are used to process large amounts of data from different sources, then data transformation and loading can be completed, but excessive time is required
Solution Approach 1:
The system performs preliminary analysis of ETL instructions to identify activity dependencies and determine the optimal processing sequence before actual data processing begins. This advance planning allows the system to execute ETL operations more efficiently, reducing overall processing time by avoiding unnecessary sequential operations and optimizing the execution flow.
Solution Approach 2:
The patent implements dynamic processing by allowing the ETL system to adaptively manage processing objects and their execution sequences. The system can dynamically allocate processing resources and adjust the execution order of ETL activities based on identified dependencies, enabling more flexible and efficient data processing that reduces time loss compared to rigid traditional approaches.
Data Source
AI summary
A device receives instructions for extracting, transforming, and loading data; identifies a plurality of data processing activities based on the instructions; and determines a processing sequence for performing the plurality of data processing activities. The device further creates a plurality of processing objects corresponding to the plurality of data processing activities. Each one of the plurality of processing objects including a discrete data structure that is produced by performing a corresponding one of the plurality of data processing activities. The device performs the plurality of data processing activities in accordance with the processing sequence and by using the plurality of processing objects.