Snowplow vs Rudderstack: what do we mean by ‘data quality’?
You can better understand the quality of Snowplow data with the acronym ‘RECAP’: Reliable, Explainable, Compliant, Accurate and Predictive.
To rely on data, it must arrive in the timeframe you have defined. Only Snowplow can support very low latency applications since we use components optimized for such use cases (i.e. Kinesis / PubSub).
Rudderstack uses Postgres which limits its ability to support mission-critical real-time use cases.
Result: Rudderstack’s tech can solve for near-time use cases not true real time.
Snowplow’s Data Creation philosophy promotes traceability – each event is packed with rich metadata that facilitates data lineage.
Result: Data is explainable to both technical and business users, which creates more effective collaboration as well as buy-in to the value of the data. This breaks down the silos that traditionally form between these groups, a common theme for customers of CDP.
Location/GDPR: Snowplow does not process your data, it’s private SaaS. Rudderstack’s hosted solution runs on Amazon Elastic Kubernetes Service (EKS) with the cluster spanning 3 availability zones (east-1a, east-1b, east-1c), therefore data is processed outside of the EU – potentially creating a GDPR issue.
Data Storage: While Snowplow data is always stored in your private cloud environment, Rudderstack stores your data in two PostgreSQL databases in its data plane to collect and transform data for its source destinations.
GDPR consent: Snowplow’s unique approach to structuring event data allows you to associate metadata with each event, such as GDPR consent status. This provides a robust audit trail detailing the justification for data capture down to the most granular level.
Private SaaS: Snowplow’s private SaaS deployment model is the next logical step for how software is used, due to the market’s demand for transparency and increased scrutiny on how technology partners are using customer data. Rudderstack is ultimately still a public SaaS solution and so presents a continuation of the compliance risks inherent with CDPs.
Result: Snowplow allows companies to adapt to a changing regulatory climate
Learn more about compliance.
Accurate data adheres to an expected set of standards such as being of the right type, having all of the required data points, and being correctly timestamped.
This is integral to Snowplow’s approach to data creation, which is reflected in our schema validation technology. The customer retains full ownership and control over their schema to determine their exact requirements for each behavioural event created. We then provide a full QA workflow to ensure that schema can evolve over time in response to changing business requirements. Rudderstack’s Data Governance API and Tracking Plans only provide observability over bad data as opposed to the capability to actively block it from reaching destinations. Any equivalent “validation” would need to be built from the ground up using a Transformation, which clearly demonstrates that accuracy is not the priority.
Result: Snowplow helps create a culture of trust in your data and means that it can be used as soon as it hits your storage location.
Predictiveness is what sets Snowplow data apart from what’s offered by CDPs and other data-collection solutions. It is this quality that allows our customers to understand the “why?” – i.e. what is motivating a customer to behave in a certain way. It’s this understanding this motivation that allows us to predict what a customer or cohort of customers is likely to do next.
Result: Predictiveness is what allows companies keep asking ‘why’ as they evolve, eventually reaching aspirational data applications using cutting-edge AI technologies.