Connector Agent Framework for Secure Data Integration
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Solution Overview
Problem
Existing data integration systems for secure analytic data systems face challenges in securely connecting to external data sources, particularly due to security concerns and complexity, especially when dealing with on-premise or legacy systems, which requires technical skills and introduces security risks.
Innovation Solution
A controller API and connection agent framework is introduced, allowing for simplified connection configuration through a point-and-click interface, with a connection agent being instantiated automatically to handle data extraction or upload, leveraging existing connectors and ensuring strong authentication and encryption, thus bypassing firewall restrictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a data connector is configured between external data source and internal analytics application, then data integration capability is improved, but security risks and implementation complexity increase
Solution Approach 1:
The patent introduces a connector agent as an intermediary component that runs on the customer's premises and mediates between the analytics application and external data sources. This connector agent handles all data connection and extraction operations locally, preventing direct exposure of the analytics application to external systems and thereby reducing security risks while maintaining data integration capability.
Solution Approach 2:
The system is segmented into distinct functional components: the analytics application remains isolated in the cloud, while connector agents are deployed separately on customer premises. This segmentation allows independent management of security protocols and connection parameters, enabling secure data integration without compromising the core analytics system.
2Adaptability or versatility
If a data connector is configured between external data source and internal analytics application, then data integration capability is improved, but implementation and configuration complexity increase
Solution Approach 1:
The connector agent framework provides self-service capabilities by automatically discovering available connectors, retrieving appropriate connector code, and configuring connection parameters without requiring manual intervention from analytics application developers. This automation significantly reduces implementation and configuration complexity while maintaining versatile data integration capabilities.
Solution Approach 2:
Connector definitions and connection templates are prepared in advance within the framework. When data integration is needed, the system retrieves pre-configured connector specifications and applies them automatically, eliminating the need for complex real-time configuration and reducing implementation burden.
3Reliability
If technical skills are required for connector configuration, then security can be maintained, but ease of operation decreases
Solution Approach 1:
The framework enables non-technical users to configure data connections through intuitive graphical interfaces that automatically generate secure connector configurations. The system handles security protocols, authentication, and encryption automatically, maintaining security standards while making the system easy to operate without requiring technical expertise.
Data Source
AI summary
A method for data processing is described. The method may include configuring a connection template that defines a data extraction flow for a first data source and transmitting from an analytics application server a controller application programming interface (API) call based on a query request for data stored at the first data source. The method may further include instantiating a connector microservice based on the controller API call and the connection template, where the connector microservice is configured with a first data connector with the analytics application server and a second data connector with the first data source that is specific to the first data source. The method may further include retrieving at the analytics application server the data from the first data source based on the connector microservice.