🤖 AI Summary
To address the challenges of fragmented engineering data and inefficient, inconsistent manual reporting in large-scale software development, this paper proposes and implements a centralized engineering productivity analytics framework supporting near-real-time aggregation and visualization. Methodologically, the framework introduces a dual-mode data storage architecture coupled with a precomputation engine, integrated with scheduled data ingestion (cron), proactive alerting, and role-based access control (RBAC). It unifies heterogeneous data from multiple source systems via a dual-schema design and leverages Metabase to enable cross-dimensional visual analytics—spanning development efficiency, software quality, and operational effectiveness. Empirical evaluation demonstrates that the deployed system reduces average weekly manual reporting effort by 20 person-hours, significantly accelerates bottleneck identification latency, and concurrently improves engineering decision responsiveness and platform scalability.
📝 Abstract
In modern, large-scale software development, engineering leaders face the significant challenge of gaining a holistic and data-driven view of team performance and system health. Data is often siloed across numerous disparate tools, making manual report generation time-consuming and prone to inconsistencies. This paper presents the architecture and implementation of a centralized framework designed to provide near-real-time visibility into developer experience (DevEx) and Key Performance Indicator (KPI) metrics for a software ecosystem. By aggregating data from various internal tools and platforms, the system computes and visualizes metrics across key areas such as Developer Productivity, Quality, and Operational Efficiency. The architecture features a cron-based data ingestion layer, a dual-schema data storage approach, a processing engine for metric pre-computation, a proactive alerting system, and utilizes the open-source BI tool Metabase for visualization, all secured with role-based access control (RBAC). The implementation resulted in a significant reduction in manual reporting efforts, saving an estimated 20 person-hours per week, and enabled faster, data-driven bottleneck identification. Finally, we evaluate the system's scalability and discuss its trade-offs, positioning it as a valuable contribution to engineering intelligence platforms.