Distributed Data Processing ETL Framework
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Solution Overview
Problem
Existing ETL tools for big data management in distributed systems, such as Hadoop, are complex and insufficient for handling large volumes of data, requiring extensive effort for understanding and custom programming, and lack efficient mechanisms for data extraction, transformation, and loading while maintaining data integrity and structure.
Innovation Solution
A system and method for analytical processing in a distributed data storage system that includes a data extraction module for performing analytical operations, a processing engine with mapping and transformation modules to categorize and transform data based on constraints and business rules, enabling data to be extracted, refined, and transformed in a single stage, and stored in a target area within the distributed system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing ETL tools are used for big data management in distributed systems, then data extraction and transformation can be performed, but the system complexity increases and the tools become insufficient for handling large volumes of data efficiently
Solution Approach 1:
The patent combines data extraction, transformation, and loading operations into a unified ETL framework that leverages the distributed processing power of Hadoop. By merging these previously separate operations into a single coordinated process that utilizes map-reduce algorithms, the system achieves efficient handling of large data volumes without proportionally increasing complexity.
Solution Approach 2:
The invention creates a universal ETL tool that can handle multiple data sources, formats, and transformation requirements through a single platform. The system provides multi-functional capabilities including various extraction methods, transformation operations (aggregation, filtering, pivoting), and loading mechanisms, all within one distributed processing framework that can scale to handle big data volumes.
2Productivity
If map reduce codes are used for ETL operations in distributed environment, then data processing capability increases, but understanding and coding requires immense effort and customized programming
Solution Approach 1:
The patent introduces an intermediary layer that sits between the user and the map-reduce execution engine. This intermediary provides a simplified configuration interface where users can define ETL operations through declarative specifications rather than writing custom map-reduce code. The system automatically translates these high-level specifications into optimized map-reduce jobs, eliminating the need for users to understand complex programming details while retaining the processing power of distributed computing.
Solution Approach 2:
The ETL tool implements self-service capabilities by automatically generating, optimizing, and executing map-reduce programs based on user-defined parameters. The system handles code generation, job scheduling, resource allocation, and result aggregation without requiring manual intervention or deep programming knowledge, allowing users to focus on data transformation logic rather than implementation details.
3Quantity of substance
If distributed data storage systems are used to manage big data, then data storage capacity increases, but data management and maintenance become more difficult
Solution Approach 1:
The patent segments the data management process into distinct modular components: data extraction from source systems, transformation processing through various operations, and loading into target destinations. Each segment can be independently configured, executed, and monitored. The system also segments the processing workload across multiple distributed nodes, allowing parallel execution while maintaining centralized coordination through the ETL framework.
Solution Approach 2:
The ETL tool implements comprehensive feedback mechanisms that monitor data quality, processing progress, and system performance throughout the ETL pipeline. Automated error detection, validation rules, and quality checks provide continuous feedback to ensure data integrity. The system tracks transformation metrics, identifies issues in real-time, and enables corrective actions without manual intervention, simplifying the management of distributed data storage and processing operations.
Data Source
AI summary
The present disclosure in general relates to technologies for processing data in a distributed data storage system, and more particularly, to a method, a system, and a computer program product for analytical processing of data by using the processing power of the distributed data storage system. In one embodiment, a system for analytical processing of data in a distributed data storage system is disclosed. The system comprises: a data extraction module configured to perform analytical operations to extract data from source databases in one or more data formats; and a processing module configured to perform data refinement operations to categorize the data while the data is being extracted. The processing module comprises: a mapping module configured to perform mapping operations of the categorized data; and a transformation module configured to perform an analytical transforming operation of the mapped categorized data to obtain a transformed categorized data.