What are key considerations when choosing a behavioral data pipeline?

Choosing a behavioral data pipeline requires evaluating technical capabilities, governance features, total cost of ownership, and alignment with your AI and analytics strategy.

Key Evaluation Criteria:

Consideration
Questions to Ask
Data Quality
Does it provide schema validation, failed event recovery, and real-time alerting?
Data Governance
Can you maintain clear data ownership, auditability, and version control?
Deployment Options
Can you run it in your own cloud (VPC) for full control and compliance?
Scalability
Can it handle billions of events without performance issues or spiraling costs?
Real-time Processing
Does it deliver data in real time or just daily batches?
AI-Readiness
Does it output data in formats optimized for ML feature engineering?
First-Party Tracking
Does it support first-party cookies and server-side tracking to avoid ITP/cookie deprecation?
Total Cost of Ownership
How do costs scale as event volumes grow? Are there hidden egress fees?

With Snowplow, organizations gain a pipeline built for data teams—with real-time processing, Private Managed Cloud deployment, comprehensive data quality tooling, and predictable pricing that doesn't penalize growth. Companies like Strava, Burberry, and AutoTrader trust Snowplow to process their most critical behavioral data.

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Whether you’re modernizing your customer data infrastructure or building AI-powered applications, Snowplow helps eliminate engineering complexity so you can focus on delivering smarter customer experiences.