Piwik vs Snowplow: What are the differences?

## Introduction
In web analytics, Piwik and Snowplow are two popular tools used to track and analyze data on websites. However, there are key differences between the two that impact their functionality and usage.

1. **Data Collection Method**: Piwik collects data using JavaScript tags on websites, similar to traditional analytics tools, while Snowplow collects data via trackers that can be implemented across various platforms and languages, allowing for more flexibility and customization in data collection.

2. **Data Ownership**: Piwik allows users to host data on their own servers, providing complete control and ownership over the data collected. In contrast, Snowplow offers the option to host data on cloud-based services, which may raise data privacy and security concerns for some users.

3. **Scalability**: Snowplow is designed to handle large volumes of data and high levels of customization, making it more suitable for enterprises or organizations with complex analytics needs. Piwik, on the other hand, may not scale as effectively for big data analytics.

4. **Open Source vs. Commercial**: Piwik is an open-source tool with a free version available, while Snowplow offers both open-source and commercial versions with additional features and support. The choice between the two may depend on budget constraints and the need for specialized features.

5. **Data Schema and Enrichment**: Snowplow allows users to define and customize data schemas for detailed event tracking and enrichment, providing more flexibility in analyzing complex data sets. Piwik offers predefined data structures that may limit the depth of analysis possible.

6. **Real-time Data Processing**: Snowplow supports real-time data processing, enabling immediate insights into website activity. Piwik offers real-time tracking as well, but there may be slight delays in data processing compared to Snowplow.

In Summary, Piwik and Snowplow differ in their data collection methods, ownership models, scalability, source availability, data schema flexibility, and real-time processing capabilities, providing users with options to choose based on their specific analytical needs and preferences.