Snowplow vs. Rudderstack
Find out why thousands of data-first organizations use Snowplow to deliver sophisticated data applications using behavioral data created for purpose.
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What’s the difference
between Snowplow and
Rudderstack is a Customer Data Platform (CDP), fundamentally designed to send data to many downstream systems, bypassing central storage. Their recent change to incorporate “warehouse first” messaging highlights a deep contradiction – CDP data is not designed to be consumed from central storage.
When you optimize for short-term convenience with a CDP, the small print is a lack of data governance and discoverability, which translates over time into a data swamp. This leads to long-term technical debt worsened by vendor lock-in.
That’s why Snowplow created a new type of tool called a Behavioural Data Platform (BDP).
You get clean, rich, and complete behavioral data modeled out of the box to be AI- and BI-ready. Most importantly, it’s warehouse and lake-first – by design.
Snowplow vs Rudderstack overview
Choose Rudderstack for:
- A small step up from another CDP, particularly Segment
- Basic use cases and low organizational complexity
- Limited aspirations around AI or real-time applications
- A focus on forwarding data to 3rd-party systems
Choose Snowplow for:
- Using the warehouse/lake as your single source of truth
- Best-in-class data governance, discoverability, and observability
- Advanced analytics, AI, and real-time use cases
- Complete ownership of your data and pipeline
The business case: return on data investment
Data teams can spend up to 50% of their time cleaning and wrangling data (State of Behavioral Data, 2022)
87% of data science projects never make it to production (VentureBeat)
There’s a disconnect between the expectations on data teams and what they can actually deliver. A significant factor here is poor-quality data – which requires too much cleaning and wrangling. Fixing these issues at source is the best way to reverse this trend.
Snowplow’s emphasis on quality sets your projects up for success. This goes beyond projects making it into production, as quality data also drives top-line growth and cost-reduction metrics.
To accelerate time to value, Snowplow’s Data Product Accelerators now help you get next-level analytics in record time.
Data discovery and evolution
Being able to effectively understand and grow your data holds massive business value
Snowplow sends all data to a central data table called “atomic events”. Rudderstack, in comparison, creates a new table for every single event created – requiring literally thousands of joins for complex queries. This is a headache for very small implementations but a guillotine at scale.
Further, when it comes to evolving data, Snowplow’s schemas allow you to carefully version events and communicate this across complex teams in a well-documented way. Versioning with Rudderstack is impractical due to a lack of proper documentation and event schemas.
Without effective data discovery, your data team is slowed to a crawl and an agile self-serve culture is impossible.
Building your data stack to allow room for growth is essential; untying yourself from tools is expensive
Snowplow data applications are built on a fully transparent framework and are completely customizable and able to evolve.
A great example comes from Sophi, part of the Globe and Mail. Their team started using Snowplow for content analytics, but grew this single use case to the point where they were offering personalized content recommendations in real-time using AI – not part of their initial scope.
Rudderstack’s tech is not appropriate for these advanced use cases, so you effectively have a roof blocking your long-term progress, as well as vendor lock-in, compromising business-critical use cases when it comes time to move on.
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
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.
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