Query Management Pipeline for Cross-Environment Data Deployment
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Solution Overview
Problem
Existing systems face challenges in deploying data services to large numbers of field personnel efficiently, particularly when data points are stored in disparate data environments, leading to long lead times, technical bottlenecks, and unscalable solutions due to the software development life cycle (SDLC).
Innovation Solution
A Query Management Tool (QMT) that enables users to create and deploy queries across disparate data environments, bypassing SDLC through a data pipeline, query storage, and integration components, allowing near-real-time data deployment and configuration according to user-defined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data services are deployed to large numbers of field personnel using traditional systems, then data can be accessed by field personnel, but the system faces challenges in responsively scaling requests without undergoing the software development life cycle (SDLC), leading to long lead times and technical bottlenecks
Solution Approach 1:
The system segments the data deployment process into independent components: a query definition interface for users, a query storage repository, a data pipeline for automated processing, and integration components for field personnel devices. This segmentation allows each component to operate independently, enabling rapid deployment without full SDLC cycles.
Solution Approach 2:
The system performs preliminary actions by pre-configuring the data pipeline with connections to disparate data environments and pre-establishing query templates. When field personnel need data, the pre-configured pipeline can immediately process requests without requiring development time, thus reducing lead time while maintaining productivity.
2Adaptability or versatility
If data points are stored in disparate data environments and accessed through traditional platforms, then data can be retrieved, but the solution becomes unscalable and incurs high costs due to SDLC requirements
Solution Approach 1:
The data pipeline is designed as a universal component that can connect to multiple disparate data environments (databases, data lakes, cloud storage) using standardized protocols. This multi-functionality allows the same pipeline infrastructure to handle various data sources without increasing system complexity, enabling scalable integration across different platforms.
Solution Approach 2:
The system introduces a query storage repository as an intermediary layer between users and disparate data environments. Users define queries in a standardized format stored in the repository, and the data pipeline translates these into environment-specific queries. This intermediary abstraction simplifies the system architecture while maintaining adaptability to multiple data sources.
3Ease of operation
If traditional platforms are used for field personnel to interact with organizational systems, then field personnel can access data, but the platforms are not well-designed for large scale field interactions and require SDLC for any changes
Solution Approach 1:
The system enables self-service for field personnel through an intuitive query definition interface where users can directly create, modify, and deploy queries without requiring software development intervention. The automated data pipeline handles execution and data retrieval automatically, making the system easy to operate while eliminating complex deployment processes.
Data Source
AI summary
A computer-implemented method of deploying data sourced from disparate data environments in a defined form using queries. The method comprises establishing a data pipeline to each of the disparate data environments, creating a query, at a user interface, that defines a request for data from the disparate data environments, onboarding the query to a query storage, transferring the query to the data pipeline, responding to the query by preparing data sourced from the disparate data environments, and delivering the response to the query to an end user application, in which the data is deployed according to a user-selected configuration.