Data Source ETL App, Update & Oviond in App Query Builder Revision
(Supermetrics)
Key Principle
Minimize Disruptions: Ensuring that the current product remains operational is a top priority.
Phases of Development
note from chris: the below is a starting point, lets use an agile approach to end up with best outcome... a lot might change, but below the starting point
Phase 1: Research, Planning, and Design
Objective: Design and plan the new Oviond Data Source Server focusing on ELT specifically for Oviond.
Lead: Brendan, design Chris, guidance Lesedi
Phase 2: Query Builder Redesign
Objective: Redesign the query builder using the new ELT server, inspired by Supermetrics.
Lead: Lesedi
Documentation
Approach: Adopt a docs-first approach using Docusaurus for all documentation.
UI Design
Responsibility: Chris will handle all UI design work using the NextUI Tailwind CSS design library.
Data Source Planning
Central Source of Truth: Use a Google Sheet to meticulously plan all data sources.
here is an example.. maybe this is all handled on Docusaurus for easy management? this is the type of thing, pretty sure we can we can cleanly do all of this on Docusarus: https://docs.google.com/spreadsheets/d/1qxIoPPh8nMI4IrFr0Y8P2ZsZ3fRkRsUvgL7toZf0_-w/edit?usp=sharing
Detailed Steps
Step 1: Research and Decision on ELT Layer Library
Team: Brendan, Lesedi, and Chris
Objective: Investigate Maltano and Airbyte to choose the most suitable open-source library for our ELT layer, taking an AI-first approach.
Importance:
Data Integrity and Accuracy: Ensures reliable data extraction, transformation, and loading.
Scalability: Must handle increasing data volumes and complex transformations as Oviond grows.
Flexibility and Extensibility: Should accommodate various data sources and transformations, and integrate new data sources easily.
Community Support and Development: A vibrant community can provide ongoing support, quick bug fixes, and new features.
AI Integration: Should support AI-driven data processing and transformation tools.
Research and Evaluation Criteria
Architecture and Core Features:
Maltano:
Emphasizes version control and collaboration.
Strong integration with Singer taps and targets.
Focuses on the entire data lifecycle.
Airbyte:
Modular architecture with connectors for various data sources and destinations.
Emphasizes ease of use and rapid deployment.
Active open-source community.
Ease of Use and Deployment:
Maltano:
Complex setup but offers extensive customization.
Airbyte:
User-friendly setup with a web UI.
Performance and Scalability:
Maltano:
Highly scalable and optimized for complex ETL workflows.
Airbyte:
Efficient data processing pipelines.
Community and Support:
Maltano:
Smaller, dedicated community.
Airbyte:
Larger, rapidly growing community.
AI Integration and Extensibility:
Maltano:
Robust support for AI and machine learning tools.
Airbyte:
Strong focus on extensibility.
Decision-Making Process
Initial Evaluation:
Set up both Maltano and Airbyte in a test environment.
Perform basic ETL tasks to assess ease of use and performance.
Detailed Analysis:
Compare features, performance, and scalability using real-world data scenarios.
Evaluate AI-driven data transformations and processing.
Community and Future Prospects:
Assess community activity and support.
Evaluate development roadmap and future potential.
Final Decision:
Choose the tool that best meets Oviond’s needs for an efficient, scalable, and AI-integrated ELT solution.
Step 2: Google Sheet and Docusaurus Setup + First Draft UI Plan Sketch
Note: pretty sure we can handle this all cleanly on Docusarus.. would be better to have one place to plan...
Thorough Documentation Plan:
Setup Docusaurus:
Initialize the framework.
Customize configuration to align with Oviond’s branding.
Define a clear navigation structure.
Create Comprehensive Documentation Templates:
Introduction: Overview of the data source.
Authentication: Describe OAuth, API Key, Basic Auth, etc.
Connection Setup: Prerequisites, detailed steps, example configurations.
Monitoring and Maintenance: Monitoring setup, health checks, troubleshooting.
Metrics and Dimensions: List of available metrics and dimensions, usage examples.
Limitations: Rate limits, data availability, known issues.
Versioning: Current version details, changelog, deprecations.
API Update Notices: Monitoring updates, handling updates.
Best Practices: Optimizing queries, data management, security considerations.
Google Sheet for Integration Planning:
Create Google Sheet: Name it "Data Source Integrations".
Define Columns:
Integration Name
API Endpoint
Auth Method
Data Points
Sync Frequency
Rate Limits
Data Availability
Current Version
Update Notices
Limitations
Step 3: Start Building
Lead: Brendan
Final Bullet List Summary for the Build Step
Environment Setup
Install Node.js, MongoDB, PostgreSQL, and Docker.
Set up version control with Git and initialize a repository.
Backend Development
API Management with Hasura:
Deploy Hasura and connect to PostgreSQL.
Auto-generate GraphQL APIs.
Configure role-based access control (RBAC) and fine-grained permissions.
Extend with remote schemas and custom actions.
ETL Integration:
Set up Airbyte or Maltano for data extraction and loading.
Implement DBT for SQL-based data transformation.
Integrate AI models for advanced data processing using TensorFlow, PyTorch, or scikit-learn.
Orchestration:
Use Apache Airflow or Prefect for managing ETL workflows.
Frontend Development
Frameworks and Libraries:
Use React with Next.js for building the user interface.
Utilize Tailwind CSS for styling, and Material-UI or Ant Design for components.
Data Fetching and State Management:
Integrate Apollo Client with Hasura for optimized GraphQL queries and state management.
Implement real-time features using Hasura’s subscriptions.
Authentication:
Implement OAuth 2.0 and JWT for secure access.
Database and Storage
Primary Storage:
Use PostgreSQL for structured data and integration with Hasura.
Use MongoDB for flexible, schema-less data storage if necessary.
Data Lake:
Set up a data lake (e.g., AWS S3, Google Cloud Storage) for raw and processed data storage.
CI/CD and DevOps
Pipelines:
Set up CI/CD pipelines using GitHub Actions, GitLab CI, or CircleCI.
Integrate Hasura migrations and metadata management into CI/CD.
Containerization:
Dockerize the application for consistent deployment.
Use Kubernetes for orchestration and management of containerized applications.
Monitoring and Logging
Performance Monitoring:
Use Prometheus and Grafana for monitoring system health and performance.
Centralized Logging:
Implement logging using the ELK Stack (Elasticsearch, Logstash, Kibana) for easy searchability and tracking.
Security Implementation
Data Encryption:
Ensure all data transmissions are encrypted using HTTPS/TLS.
Secrets Management:
Use tools like HashiCorp Vault or AWS Secrets Manager for managing sensitive information.
Regular Security Audits:
Conduct regular audits and penetration tests to identify and fix vulnerabilities.
Documentation
Setup Docusaurus:
Deploy Docusaurus for internal documentation.
Populate with templates and detailed documentation for integrations.
User Guides:
Create comprehensive user guides for setting up and managing data sources.
AI and Machine Learning Integration
Model Training and Deployment:
Train AI models using TensorFlow, PyTorch, or scikit-learn.
Deploy trained models using TensorFlow Serving, TorchServe, or custom APIs.
Inference and Predictions:
Integrate AI models into the data processing pipeline for real-time predictions and insights.
AutoML Tools:
Utilize tools like Google AutoML or H2O.ai for automated model training and hyperparameter tuning.
https://drive.google.com/drive/folders/1Rops-kcbqp_7LqOBY8GQzHNSccB_3fMV?usp=drive_link
