connectordb
A self-hosted, extensible personal data aggregator and analysis engine for quantified self.
What is connectordb?
Heedy is a self-hosted, open-source personal data aggregator and analysis engine that consolidates metrics from various apps and fitness trackers into a single repository. It addresses the fragmentation of personal data across isolated services by providing a unified platform for storage, visualization, and extensible analysis. The project emphasizes user privacy and control through local hosting and a plugin-based architecture for customization.
Individuals engaged in quantified self or life logging who want to aggregate and analyze personal data from multiple sources while maintaining full privacy and control. It's also suitable for developers interested in building custom plugins for data integration or visualization.
Developers choose Heedy for its strong privacy guarantees through self-hosting, open-source auditability, and extensibility via a plugin system that allows deep customization beyond typical pre-built dashboards.
Overview
An aggregator for personal metrics, and an extensible analysis engine
Use Cases
Best For
- Aggregating personal fitness and health data from multiple wearables and apps
- Self-hosting personal metrics to ensure data privacy and avoid cloud services
- Building custom plugins for integrating niche data sources or visualizations
- Performing unified life logging analysis across disparate data silos
- Using Jupyter notebooks for interactive exploration of personal data
- Creating a centralized dashboard for quantified self enthusiasts
Not Ideal For
- Windows users needing a native personal data aggregator without virtualization
- Organizations seeking a turnkey, cloud-based dashboard with commercial support
- Non-technical individuals who want a one-click installation without command-line or Docker
Pros & Cons
Pros
Heedy is self-hosted and open-source, allowing users to keep all data on their own hardware and audit the software, as emphasized in the README's privacy arguments against cloud services like PRISM and data selling.
The plugin system enables adding new integrations and visualizations with Python or JavaScript, making it adaptable to individual needs beyond pre-built assumptions, as stated in the philosophy section.
Built-in visualizations and Jupyter notebook integration via plugins allow for direct data analysis within the platform, demonstrated in the provided screenshots of sleep data and notebooks.
Dedicated plugins like Fitbit enable seamless import of fitness tracker data, addressing the fragmentation of personal metrics from sources like apps and wearables.
Cons
Heedy only runs on Mac and Linux, with no native Windows support mentioned in the README, which restricts accessibility for a significant portion of users.
Core functionality relies on plugins, and the ecosystem is still nascent with only a few listed plugins (e.g., Fitbit, notebook), limiting out-of-the-box capabilities compared to mature aggregators.
Installation involves command-line execution, Docker for containerized deployment, and Python installation for plugins, as noted in the running instructions, posing a barrier for non-technical users.
Frequently Asked Questions
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