Spark Data Integration Engine for Heterogeneous Sources
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Solution Overview
Problem
Existing data integration systems face challenges in efficiently processing and integrating heterogeneous data sources due to their diverse formats and incompatible data models, requiring extensive manual effort and expertise, and are exacerbated by the increasing volume, velocity, and variety of data.
Innovation Solution
A system and method utilizing a Spark engine for seamless data reception, transformation, and storage from heterogeneous sources, combining data-driven and handcrafted approaches to achieve state-of-the-art performance and computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data integration approaches are used to handle heterogeneous data sources, then data can be integrated from various sources, but extensive manual effort and significant expertise are required
Solution Approach 1:
The system employs automated schema detection and data type inference capabilities that allow it to self-configure when connecting to new data sources. The schema evolution tracker automatically monitors and adapts to changes in source schemas without requiring manual intervention, enabling the system to serve itself in handling heterogeneous data sources.
Solution Approach 2:
The patent implements a universal data integration framework that can handle multiple data source types (relational databases, NoSQL databases, data lakes, streaming sources) through a common architecture. The schema evolution tracker serves multiple functions including detection, comparison, impact analysis, and automatic schema updates across diverse data sources, reducing the need for source-specific customization.
2Adaptability or versatility
If traditional data integration approaches are used for each unique data source format and schema, then data integration can be achieved, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system performs preliminary schema detection and validation when first connecting to a data source, storing the detected schema information for future use. The schema evolution tracker continuously monitors sources in advance, detecting schema changes before they cause integration failures, allowing the system to proactively adapt rather than reactively fix issues.
Solution Approach 2:
The patent implements schema templates and patterns that capture common data source structures. Once a schema is detected or defined for a particular source type, it is copied and adapted for similar sources, reducing the time required to integrate new data sources with comparable structures.
3Manufacturing precision
If manual data integration methods are employed for heterogeneous sources, then data transformation can be performed, but the process is error-prone
Solution Approach 1:
The schema evolution tracker implements continuous feedback loops that monitor data quality and schema compliance. When schema changes are detected, the system automatically validates transformations and adjusts mapping rules to maintain data accuracy, providing feedback-driven error prevention in the transformation process.
Solution Approach 2:
The patent replaces manual data transformation mechanics with automated transformation engines that use detected schemas to generate and execute transformation rules. The system substitutes human-operated transformation processes with machine-driven automated transformations based on schema evolution tracking, reducing errors associated with manual intervention.
4Quantity of substance
If data integration systems process increasing volume, velocity, and variety of data, then more data can be integrated, but system complexities increase
Solution Approach 1:
The system segments the data integration process into modular components: schema detection module, evolution tracking module, impact analysis module, and automatic adaptation module. Each component handles specific aspects of schema evolution independently, reducing overall system complexity while enabling processing of large volumes and varieties of data from multiple sources.
Data Source
AI summary
A system for data manipulation and management, the system comprising a data integration engine, a data acquisition module, a data transformation module, a data output module and a spark engine. The data integration engine is configured to receive a job specification, wherein the job specification comprises a set of instructions. The data acquisition module is configured to acquire a set of data from a database. The data transformation module is configured to transform the set of data based on the set of instructions defined in the job specification. The data output module is configured to store a transformed set of data to the database. The spark engine is configured to receive instructions from the data acquisition module, the data transformation module, and the data output module, wherein the data acquisition module, the data transformation module, and the data output module are configured to receive instructions from the data integration engine.