Heterogeneous Data Source Management With Batch-Streaming Acquisition
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Solution Overview
Problem
There is a lack of a convenient, efficient, scalable, and easy-to-operate platform for the acquisition, aggregation, query, and search of multi-source heterogeneous data in the era of big data.
Innovation Solution
A multi-source heterogeneous data management system comprising a data source management module for classifying and configuring data sources, a data acquisition module for querying and reading data, and a data storage module for storing data, utilizing batch and streaming data modes, along with modules for query, statistical analysis, and task management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a platform for multi-source heterogeneous data management is built, then data acquisition and processing capability is improved, but system complexity increases
Solution Approach 1:
The system is divided into multiple independent modules: data source management module, data acquisition module, data storage module, data query module, statistical analysis module, and task management module. Each module handles specific functions independently, reducing overall system complexity while maintaining comprehensive data management capability.
Solution Approach 2:
A unified data interface layer is introduced as an intermediary between diverse data sources and the processing modules. This interface standardizes data access methods, allowing the system to handle multiple heterogeneous data sources without increasing complexity in the core processing logic.
2Productivity
If data classification and configuration management are implemented, then data organization efficiency is improved, but operation complexity increases
Solution Approach 1:
Data sources are pre-classified into structured, semi-structured, unstructured, and binary categories during system initialization. Configuration templates and connection parameters are pre-configured for common data sources, eliminating the need for manual classification and configuration during operation.
Solution Approach 2:
The system automatically discovers and configures data sources based on pre-defined connection profiles and classification rules. Configuration parameters are auto-filled from templates, and the system self-adjusts connection settings without requiring complex manual intervention.
3Adaptability or versatility
If batch and streaming data processing modes are supported, then data processing versatility is improved, but system complexity increases
Solution Approach 1:
The data acquisition module dynamically switches between batch processing and streaming processing modes based on the characteristics of the data source and the requirements of the task. The processing mode is not fixed but adapts dynamically, allowing the system to handle different data types with appropriate processing strategies without maintaining separate dedicated systems.
Data Source
AI summary
A multi-source heterogenous data management system and method are provided. The system includes a data source management module and a data acquisition module that are connected to each other. The data source management module is configured to classify multi-source heterogeneous data to obtain classified data sources, where the classified data sources include a structured data source, a semi-structured data souce, an unstructured data source, and a binary data source; and perform information configuration of a predetermined configuration rule on the classified data sources to obtain data source information, where the predetermined configuration rule includes a URL, a username, a password, and a driver. The data acquisition module is configured to query, retrieve, and read the data source information by using a predetermined data processing method, such as a batch data mode or a streaming data mode, to obtain read data.