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MetricSynth: Framework for Aggregating DORA and KPI Metrics Across Multi-Platform Engineering

Pallav Jain, Yuvraj Agrawal, Ashutosh Nigam, Pushpak Patil

TL;DR

The paper tackles the challenge of fragmented engineering data across diverse tools by proposing a six-layer, open-source framework that aggregates DevEx and KPI metrics. It combines a cron-based data ingestion pipeline, a dual-schema MongoDB storage model, a pre-computation layer, alerting, and a Metabase-based presentation layer with RBAC to deliver near-real-time visibility. Its contributions include a scalable architecture, a curated set of DORA-aligned and internal KPIs across five domains, and empirical evidence of substantial time savings (≈40 hours/week) plus actionable case studies demonstrating improved bottleneck identification. The work provides a practical blueprint for building engineering intelligence platforms in large, multi-platform organizations while acknowledging scalability, governance, and ethical considerations.

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.

MetricSynth: Framework for Aggregating DORA and KPI Metrics Across Multi-Platform Engineering

TL;DR

The paper tackles the challenge of fragmented engineering data across diverse tools by proposing a six-layer, open-source framework that aggregates DevEx and KPI metrics. It combines a cron-based data ingestion pipeline, a dual-schema MongoDB storage model, a pre-computation layer, alerting, and a Metabase-based presentation layer with RBAC to deliver near-real-time visibility. Its contributions include a scalable architecture, a curated set of DORA-aligned and internal KPIs across five domains, and empirical evidence of substantial time savings (≈40 hours/week) plus actionable case studies demonstrating improved bottleneck identification. The work provides a practical blueprint for building engineering intelligence platforms in large, multi-platform organizations while acknowledging scalability, governance, and ethical considerations.

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.

Paper Structure

This paper contains 32 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Architecture
  4. Data Ingestion Layer
  5. Data Storage Layer
  6. Data Processing Layer
  7. Alerting Layer
  8. Secure Ingestion API
  9. Presentation Layer (Metabase)
  10. Security Layer: Role-Based Access Control (RBAC)
  11. Key Metrics for Engineering Excellence
  12. Developer Productivity
  13. Quality and Reliability
  14. Operational Efficiency (DORA Alignment)
  15. Automation Tooling
  16. ...and 17 more sections

Figures (4)

  • Figure 1: High-Level System Architecture. The diagram illustrates the flow of data from various sources through the ingestion, storage, and processing layers to the Application presentation layer, with security and alerting as integral components.
  • Figure 2: Correlation of PR Cycle Time and Main Fail Rate during a CI/CD incident, illustrating the application's diagnostic power.
  • Figure 3: Application View for a Digital Platform. This view consolidates key quality and reliability metrics, including Blockers, Fixes, Crash Rate, and the overall Bug Mix, providing an at-a-glance health assessment.
  • Figure 4: Application View for a Digital Platform. This visualization focuses on operational efficienct metrics such as Deployment Frequency and Fail Rate.