Git项目架构搭建咨询：Titan多数据源项目结构规划

Hey Trent, great question! Let's walk through a practical, scalable Git architecture and project structure for your Titan project—since it's acting as a unified hub for 5 data sources and future reporting, we need something that keeps things organized, decoupled, and easy to iterate on.

一、Project Directory Structure (Monorepo Approach) #

Given Titan's role as a centralized management layer, a monorepo makes the most sense here—it keeps all related code in one place while maintaining clear boundaries between modules. Here's a recommended layout:

• /core/: The heart of Titan, containing shared logic that all data sources and reporting tools depend on
  • /core/data_abstraction/: Defines a universal DataSourceInterface (abstract class/interface) and data transformation utilities to normalize data from all sources into a common format
  • /core/models/: Unified data entity models (e.g., UserRecord, TransactionLog) that standardize data across all sources
• /datasources/: Isolated directories for each data source, ensuring changes to one don't break others
  • /datasources/source_a/: Source A-specific code—API clients, authentication logic, data pull/update handlers, and adapters to map Source A's data to Titan's core models
  • /datasources/source_b/: Repeat the pattern for Source B, C, D, E
• /reporting/: All reporting-related code, separated from data ingestion to keep concerns clear
  • /reporting/templates/: Reusable report templates (e.g., SQL query snippets, visualization configs)
  • /reporting/generators/: Logic for building different report types (PDF, CSV, interactive dashboards) using normalized core data
• /tests/: Layered test suites to catch regressions
  • /tests/core/: Unit tests for data abstraction and model logic
  • /tests/datasources/: Integration tests for each data source's adapter and sync logic
  • /tests/reporting/: Validation tests for report data accuracy and format
• /docs/: Living documentation to onboarding new team members
  • /docs/architecture.md: High-level overview of Titan's components and how they interact
  • /docs/datasource_onboarding_guide.md: Step-by-step for adding a new data source
• Root files: README.md, .gitignore, language-specific configs (e.g., package.json, pyproject.toml)

二、Git Branch Strategy (Balanced for Iteration & Stability) #

I'd recommend a modified Git Flow that works well for teams building both core infrastructure and user-facing features:

• main: Production-ready branch—only accepts merges from develop or hotfix branches. Tag every merge here with a version number (e.g., v1.0.0) for traceability.
• develop: The main development branch. All feature branches merge into here, and it's the base for pre-release testing.
• feature/[feature-name]: Isolated branches for individual workstreams (e.g., feature/source-e-integration, feature/monthly-sales-report). Open a PR to develop once work is done, and require peer review before merging.
• hotfix/[issue-description]: Emergency fix branches pulled directly from main for critical production bugs. Once fixed, merge back to both main and develop to avoid losing fixes in future releases.
• release/[version]: Pre-release branch pulled from develop when you're ready to ship. Use this for final bug squashing, version bumping, and release notes—then merge to main and develop.

三、Key Git Practices for Data Source Management #

• Keep each data source's code contained to its own directory—this makes PRs focused, so reviewers only need to check the specific source being modified.
• Any changes to the core data abstraction layer require 2+ senior developer approvals—since every data source depends on this, breaking changes here have wide-reaching impacts.
• Add a CHANGELOG.md in each data source directory to track updates (e.g., "Added support for Source A's new API endpoint v2").

四、Reporting Module Git Tips #

• Separate report templates from generator logic: Templates can be modified by non-developers (like data analysts) without touching core code—just make sure to add validation tests to catch broken templates.
• When building a new report, create a dedicated feature branch and include sample output in your PR to show the report works as expected.
• Store historical report test data in tests/reporting/fixtures/ to ensure consistent test results as data sources evolve.

五、Collaboration Guardrails #

• Every PR must include: A clear description of the work, linked test cases, and any updated documentation (if the change affects architecture or workflows).
• Set up CI/CD to run full test suites on every PR—this catches integration issues early, especially when merging data source changes into develop.
• Prune merged feature/hotfix/release branches regularly to keep your repo clean.

内容的提问来源于stack exchange，提问作者Trent Brock