Data-Analysis-Platform

In context, this represents the kind of real-world, multi-service enterprise engineering history covering the full lifecycle of a data platform build-out — from initial scaffolding through infrastructure provisioning, API development, governance tooling, and testing.

Useful for research into software engineering productivity and patterns, repository-scale benchmarking, or as training/reference material for tools that need to reason about realistic, multi-repository enterprise development history rather than isolated code snippets.

Multi-domain technical coverage — the codebase spans Terraform/infrastructure-as-code, Python backend services, IAM/Cognito configuration, SQL data migrations, and Postman/Newman API test suites, giving breadth across the typical layers of a real platform rather than a single language or service type.Quantifiable scale — roughly 108,000 lines of code, 1,474 tracked files, and ~1.1M tokens of text content, already computed and ready to report as listing specifications.

The data product is delivered as a native Git repository — full version-controlled history, not a flat export. It can be cloned and browsed with standard git tooling (git log, git blame, git diff all work normally), or, if a marketplace requires a static download instead, exported as commit-level and file-level metric tables in CSV/JSON.

This data product is ideal for a variety of applications:

Application: Software engineering research — studying real commit cadence, sequencing, and team behavior across a multi-service platform build, rather than relying on synthetic or single-repo samples. Application: LLM and AI training data — fine-tuning or evaluating code-and-commit-understanding models on authentic enterprise development history, including realistic commit messages and diff patterns. Application: Repository sizing and cost estimation benchmarking — using the measured LOC, file counts, and token volumes as reference points for scoping similar platform engagements. Application: DevOps and CI/CD pattern analysis — examining how infrastructure-as-code (Terraform), IAM configuration, and application code evolved together across interdependent services. Application: Codebase migration and monorepo case studies — as a worked example of consolidating multiple independently-versioned repositories into a single repository while preserving full commit history. Application: Developer productivity and engineering-velocity modeling — commit frequency, file-churn, and cross-service coordination patterns over a defined ~3-month delivery window. Application: Training material for engineering teams — a realistic reference codebase (data platform: API services, IAM, governance, curation, infrastructure) for onboarding, tooling demos, or internal workshops.

Geographic Coverage: Development-side coverage is India — every commit in the repository carries an IST (UTC+05:30) author/committer timestamp, indicating an India-based engineering team. The platform itself was built for a global healthcare organization's cloud infrastructure (AWS-hosted), so the deployment target is global/US-centric even though the development activity captured here is India-based. Time Range: Earliest recorded commit: 2023-02-13 (data, infrastructure, client, core-api, curation). Latest recorded commit: 2025-01-28 (dm-producer-mvp). So the dataset spans roughly February 2023 – January 2025 (~23 months) in total, though individual projects have different active windows within that span

CC0

Licensee is granted a non-exclusive, worldwide, and perpetual right to:

Restrictions:

Data Scientists / ML Engineers: For training or evaluating code-and-commit-understanding models — fine-tuning LLMs on realistic commit sequencing, diff patterns, and multi-service coordination rather than synthetic code samples. Software Engineering Researchers: For academic or applied studies on developer productivity, commit cadence, monorepo consolidation patterns, or how infrastructure-as-code and application code co-evolve across interdependent services over a real ~2-year delivery window. DevOps / Platform Engineering Teams: For benchmarking — using the measured LOC, file counts, commit volume, and project structure as reference points when scoping, estimating, or comparing similar data-platform engagements. Engineering Managers / Consultancies: For internal training material — a realistic multi-service reference codebase (API services, IAM, data governance, curation, Terraform infrastructure) useful for onboarding, tooling demonstrations, or workshops without needing a live production system. AI/Code-Tooling Businesses: For product development and testing — evaluating code search, static analysis, repository-migration tooling, or git-history-analysis products against a real, non-trivial monorepo rather than toy examples.

The primary deliverable is a git repository, not inherently tabular — but for buyers who want a tabular view, two derived summary tables can be exported (commit-level and file-level). Here's the dictionary for both:

Binary/asset files (fonts, images, zip archives, compiled .pyc) are excluded from the files table and from all LOC/word/character counts throughout this listing, since line/word counts aren't meaningful for them