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Welcome to our engineering documentation hub. This resource provides comprehensive information about our technical infrastructure, processes, and best practices across our engineering domains.
Documentation Structure
Our documentation is currently organized into these main areas:
- Data
- Full Stack
- DevOps
- AI & ML
- General Practices
Capabilities Framework
Within each engineering area, we organize our capabilities using the Diátaxis framework, which provides a systematic approach to technical documentation.
For each capability, you will find documentation organized into the following categories as per the Diátaxis framework:
Tutorials
- Purpose: Aimed at beginners, these are learning-oriented documents.
- Content: Step-by-step guides that help users get a hands-on understanding of how to use a specific capability in a practical scenario.
How-To Guides
- Purpose: These are goal-oriented guides.
- Content: Practical steps describing how to achieve specific tasks or solve specific problems using the capability.
Explanation
- Purpose: Understanding-oriented documentation.
- Content: Provides background information, clarifies concepts, and explains decisions behind a capability. This section is not about 'how' but 'why'.
Reference
- Purpose: Information-oriented.
- Content: Technical descriptions of the machinery and how it works. This includes API documentation, code snippets, configuration options, and other factual information.
Data
Our data engineering documentation covers the following key capabilities:
- Ingestion: How we collect and import data into our systems
- Transforms: Our data transformation processes and tools
- Orchestration: Our data orchestration processes and tools
- EHR Integrations: Our EHR integrations processes and tools
Full Stack
Our full stack engineering documentation includes:
- Authentication: How we verify the identity of users and systems
- Authorization: How we manage access control and permissions
DevOps
Our devops documentation includes:
- CI/CD: How we build, test, and deploy our systems
AI & ML
Our AI & ML documentation includes:
- LLMOps: How we build and maintain our LLMs
General Engineering Practices
This section covers our overarching engineering processes and philosophies:
- Development Workflows: Our standard procedures for development and deployment
- Linting and Formatting Practices: Guidelines and tools for maintaining consistent code style and catching potential issues early.
- Code Review Practices: Guidelines and best practices for code reviews
- Architecture Diagrams: Diagrams of our systems
- Testing Strategies: How we ensure the quality and reliability of our systems
Need Help?
If you can't find what you're looking for or need additional assistance:
- Create a pull request adding the missing documentation or submit an issue in our documentation repository
- Reach out to the engineering team on #eng-documentation Slack channel
Thank you for using our engineering documentation. We're committed to making this a valuable resource for our team and hope you find it useful in your work.