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Ep. 343
The text presents a deep-dive discussion of Prism Analytics, a modern, privacy-focused web analytics platform positioned as an alternative to traditional systems like Google Analytics.
Prism is described as open source, built in the Go programming language for high performance, and designed from the ground up with privacy and data ownership in mind. A key selling point is its strong compliance with regulations such as GDPR and Schrems II, which often allows websites to operate without cookie banners. This reduces legal complexity, improves user experience, and may increase conversion rates.
The platform emphasizes simplicity and speed. Setup reportedly takes only three minutes, and its tracking script is extremely small (around 2 kilobytes), making it about 22 times smaller than many competitors. This improves website load times and benefits SEO. An even lighter 35-byte pixel tracking option is available for environments where JavaScript is restricted.
Prism uses a “progressive analytics” model: users start with basic metrics and can scale up to advanced tracking, including custom events, funnels, and geomaps. Unlike many platforms that limit reporting to predefined dashboards, Prism provides direct SQL API access. This allows full access to raw data and integration with external tools like NumPy and Pandas, giving users complete flexibility and control.
Performance is another major highlight. Prism processes data in real time, unlike some competitors that may delay reporting by 24–48 hours. It also includes automatic bot and spam filtering through a feature called “Humans Only.” Technical benchmarks from GitHub indicate the system can handle over 50,000 requests per second.
The platform is built using open-source tools such as Grafana (for dashboards) and ClickHouse (for high-performance data storage). Users can either use Prism’s cloud service or self-host the software, avoiding vendor lock-in and retaining full ownership of their data.
The core message is that Prism represents a shift in web analytics: instead of treating user data as the product (as with many free analytics platforms), it sells software and service while ensuring privacy, speed, long-term data retention, and total user control.
The final reflection questions the hidden costs of using “free” analytics tools that monetize customer data instead of prioritizing data ownership and privacy.