Free yourself from
rigid data structures.

With Snowplow your event data collection fits your business, not the other way around.

Your data,
your structure

You decide what events and entities you want to track and what data points should describe each of those events and entities. Our flexible schema’ing technology allows you to design data structures that reflect your business and use case, not a vendor’s legacy view of your industry.

Data easy
to consume

Data is loaded into tidy tables in your data warehouse and real-time event stream in an easy to consume format. This makes it easy for you to work directly with the data, consume it to your favourite BI tool or write real-time applications to consume the data in real-time.

Data collection that evolves with your business

As your business develops, so will your data needs. Your use cases will evolve and so will your tracking. Our schema’ing technology means that you can evolve your data collection with your business. Your analysts can easily query data over long periods of time during which the structure has changed, and you never have to "discard" of old data because of a change in how you collect new data.

From the Snowplow blog

An introduction to event data modeling

If you want to combine behavioral data from the web with other data sets, such as customer data from a CRM system, product data from a merchandising system, or content data from a CMS, it can be incredibly difficult (if not impossible) without a highly experienced developer.

Improving A/B testing with event data modeling

Broadly speaking, there are two approaches to tracking and measuring A/B tests. The first involves defining the metrics in advance that will be compared between the different test groups, and instrumenting dedicated tracking specifically for those metrics based on what segment a user belongs in (test, control, or neither). The second is the event analytics approach.

How a clear data taxonomy drives insight and action

How data is modelled and schema’d, both at data collection time, and at analysis time, makes an enormous difference to how easily insight and value can be derived from that data. In this post, I will explain why data models and schemas matter, and why being able to define your own event data model in Snowplow is a much better approach than squeezing your data into standard schemas.