Snowplow vs. Adobe Analytics

How does packaged analytics tool Adobe Analytics compare to Snowplow Behavioral Data Platform (BDP)?

What’s the difference
between Snowplow and
Adobe Analytics?


  • Warehouse and lake first (creating a single source of truth)
  • Full ownership of your pipeline and data, plus full privacy
  • 100% flexible to customize in line with your business needs
  • A fair and scalable pricing model without vendor or eco-system lock in


  • Not warehouse first – stores your data in a black box
  • Limited transparency with a lack of full data ownership
  • Restricted customization due to being preconfigured/’packaged’
  • Vendor lock-in: a high-cost solution, locking teams not just into the tool, but the entire Adobe ecosystem

“We would not have achieved our current level of self-serve data without Snowplow. It has enabled us to democratize our data culture, significantly improving our analytics coverage and deepening our insights”


Key Snowplow Features

Create a culture of trust in data
Snowplow has pioneered data validation and testing tech that creates full trust in the data across your business
Quality assurance
Trial tracking in a sandbox environment – like software testing. Data is also pre-validated with JSON schemas to ensure quality
Data optimized for AI & BI
Our highly-curated behavioral datasets are granular and accurate enough to power advanced AI and BI use cases
Compliant first-party data creation
Mitigate against tightening regulatory requirements through ‘private SaaS’ deployment model – no customer data ever leaves your infrastructure
Gain a customer 360
Understand your customers’ behavior during their anonymous journeys and across devices, maximizing the efficiency of your marketing funnel
Free your data team
Data lands in your warehouse ready for use, freeing your data team from endless cleaning and wrangling and ensuring projects see production

Snowplow for warehouse-first analytics

The warehouse is becoming the center of data gravity, with the global data warehousing market set to be worth $30 billion by 2025 (Global Market Insights).

In terms of data modeling in the warehouse, Snowplow is the third-largest publisher of dbt models on the planet, after dbt and Fivetran.

While Adobe can load to centralized storage, the structure and quality of the data are not good enough to effectively scale up your data projects and create a reliable single source of truth.

Adobe can create data quality issues

Adobe Analytics is presented as an Enterprise-grade advanced analytics tool, but the data quality simply doesn’t justify this. 

Blocked by privacy controls
Adobe has not kept up with the changes to browsers and adblockers over the last 5 years, limiting the lifetime of tracking cookies. There will be gaps in the data collected
Bad data cleaning
Once bad data has been collected by Adobe, there is no way to get it out or clean the data. One Snowplow customer migrated from Adobe after accidentally-collecting customer PII, forcing them to discard business-critical data
Lack of customization
Adobe forces everything to conform to a fixed model (‘name=value pairs’ of strings), when your tracking doesn’t naturally fit into this format, the result is lossy data that doesn’t accurately reflect your site/app
Inflexible to business requirements
Once there’s live data in a dimension, you can’t use that dimension for something else in a new version without it intermingling with the existing data – i.e., data cannot evolve with your business
Misleading and incomplete data
When unique values in a dimension reach a certain threshold, Adobe hides a proportion of them, rendering the data incomplete
A diagram showing the weaknesses of Adobe Analytics

Adobe lacks flexibility and customization

Tools should adapt to the user, not the other way around. Adobe makes it hard for less linear use cases and business contexts.

Mobile and IoT analytics: Adobe analytics has missed the boat on the explosion in mobile-first, wearables and IoT businesses, as they can’t offer the granular and customized picture needed
Trending business models: Adobe’s data structures can’t create custom concepts for businesses, websites, or apps where the business model isn’t simply media or e-commerce. Examples include two-sided marketplaces, aggregator sites, and subscription businesses
Custom metrics: customers are forced into pre-built vanity metrics rather than relevant / tailored metrics or use cases. This limits use cases to generic web analytics. The result – different tools for different use cases and multiple versions of the truth

Who does your Adobe data belong to?


Adobe: collects a large amount of PII automatically, requiring customization of their default settings, which is not an easy process.

Snowplow: define your own data – you decide what to include in your tracking plan and PII can be fully pseudonymized.


Adobe: as a packaged tool, your data is stored by Adobe, reducing privacy and raising compliance questions.

Snowplow: the option to deploy all Snowplow infra in your own cloud environment for full privacy and ownership – we don’t have access to your data.


Adobe: as it is not possible to reprocess data, if a data subject changes their preferences after collection, large amounts of data may have to be discarded.

Snowplow: fully configurable – adjust your tracker when a user changes consent preferences.
A Venn diagram showing what's needed for data ownership

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