Sidecar Proxy Integration for Data Confidence Fabrics
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Solution Overview
Problem
Existing applications require modification of source code to access data confidence fabrics, leading to lower deployment and adoption rates.
Innovation Solution
A sidecar or proxy is used to connect applications to data confidence fabrics, intercepting communications and applying trust functions without modifying the application's source code, enabling seamless integration with data confidence fabrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If applications are modified to directly access data confidence fabrics, then data trustworthiness is improved, but deployment complexity and adoption difficulty increase
Solution Approach 1:
A sidecar proxy component is introduced as an intermediary between applications and the data confidence fabric. The sidecar handles all interactions with the fabric, including authentication, data ingestion, and confidence score retrieval, while applications simply send data to the sidecar via standard interfaces. This mediator pattern resolves the contradiction by encapsulating complexity in the sidecar while maintaining simple application interfaces.
Solution Approach 2:
The system is segmented into distinct components: applications, sidecars, and the data confidence fabric. Each component has a specific responsibility - applications generate data, sidecars manage fabric interactions, and the fabric processes trust functions. This segmentation allows applications to remain simple while the sidecar handles the complex fabric integration, resolving the deployment complexity issue.
2Reliability
If source code modification is required for fabric integration, then data confidence scoring is achieved, but adoption rate decreases
Solution Approach 1:
The sidecar acts as an intermediary that enables data confidence scoring without requiring application source code modification. Applications maintain their original code and simply interact with the sidecar through standard interfaces. The sidecar then handles all fabric-specific operations, making the system adaptable to existing applications while still achieving confidence scoring.
Solution Approach 2:
The sidecar creates a copy or wrapper around the application's data output interface. Instead of modifying application code, the sidecar intercepts data at the interface level and adds confidence scoring functionality. This copying approach allows existing applications to be integrated with the fabric without any source code changes, significantly improving adoption rate.
3Measurement precision
If direct application-fabric connection is established, then data processing accuracy is improved, but system complexity increases
Solution Approach 1:
The sidecar serves as a specialized intermediary that maintains high data processing accuracy by implementing proper authentication, data formatting, and protocol handling. While it adds a component to the system, it actually reduces overall complexity by providing standardized interfaces and handling fabric-specific complexities internally, allowing applications to maintain simple integration while achieving accurate processing.
Data Source
AI summary
A proxy or sidecar configured to connect an application to a data confidence fabric is discloses. The sidecar is configured to perform functions related to the transmission of data. As the sidecar intercepts communications to/from an associated application, the sidecar determines which of several functions to perform related to the transmission of data. In addition, the sidecar ensures that data confidence related operations and trust functions, as specified by the application, are applied to the data. Further, the sidecar identifies lineage when data is changed by an application.