Get Started
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.
Legacy analytics platforms were built for a different era: static dashboards, black-box metrics, and rigid schemas. Today, data-driven teams want more than pre-built templates—they want the power to define their own metrics, design their own workflows, and adapt to rapidly changing business questions.
Composable analytics offers an alternative. Instead of relying on monolithic platforms, teams assemble modular components—collection, transformation, modeling, and visualization—to create analytics stacks that reflect how their business actually works.
Snowplow provides the behavioral data foundation for this modern approach. Our Customer Data Infrastructure (CDI) collects, enriches, and delivers high-quality, structured event data into your warehouse, lake, or stream—ready for analytics, AI, experimentation, and more.
With data now central to every team’s decisions, organizations can’t afford brittle tools or inflexible schemas. Composable analytics unlocks speed, ownership, and adaptability—at scale.
Teams across industries are moving from packaged tools to composable stacks to better understand customers, journeys, and performance.
Packaged tools restrict metric flexibility and event definitions
Difficult to migrate or evolve logic over time
Event collection, modeling, and reporting are disconnected
Teams duplicate effort across tools and have no central source of truth
Even with good pipelines, teams often struggle to get insights into the hands of decision-makers
Engineers maintain dashboards or run ad hoc queries for business teams
Snowplow’s Customer Data Infrastructure (CDI) solves the hardest part of analytics: capturing high-quality behavioral data, validating it in real time, and making it warehouse- or stream-ready for modeling and analysis.
From there, teams can layer on transformation, BI, experimentation, or activation tools based on their stack maturity and business needs.
Combines structured BI and low-code exploration by modeling Snowplow data in your warehouse or lakehouse. Powered by dbt, Sigma, Kubit, Mitzu and other warehouse-oriented tools, this pattern supports governed dashboards and self-serve access—no ingestion delays, no duplicated pipelines.
Best for teams centralizing analytics on Snowflake, Databricks, or BigQuery while enabling fast, cross-functional insights.
Use modeled behavioral data as the foundation for a conversational analytics layer. With tools like Databricks AI/BI Genie or Snowflake Cortex Analyst, users can ask questions in natural language and get structured responses grounded in trusted warehouse data.
Ideal for product, growth, or executive teams that want fast answers—without writing SQL or relying on analysts.
Sync modeled Snowplow events back into tools like Mixpanel, PostHog, or Amplitude using Event Forwarding or reverse ETL. Maintain one schema and data model across warehouse + SaaS tools. Let teams keep using familiar interfaces without sacrificing data quality. Perfect for hybrid stacks that mix composable pipelines with embedded SaaS tooling.
Replace fragile SDK tracking with data you already own—while letting teams keep the tools they love.
Composable analytics is not just a trend—it’s the future of how modern organizations generate insights.
With Snowplow at the foundation, you can break free from rigid tools, govern your metrics and events at the source, and empower every team—from data engineers to product managers—with flexible, trustworthy behavioral data.
Whether you're modeling within Snowflake, exploring in Kubit, syncing into Mixpanel, or reporting with Sigma, Snowplow gives you the power to build your analytics, your way.
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.