Achieving flexibility in analytics workflows requires infrastructure that supports multiple tools, custom schemas, and evolving business requirements without vendor lock-in.

Flexibility Enablers:

• Custom event schemas: Define tracking that matches your unique product and business model, not generic templates.
• Multiple destination support: Stream data to warehouses (Snowflake, Databricks, BigQuery), lakes (S3, GCS), or real-time streams (Kafka, Kinesis) simultaneously.
• Customizable data models: Modify or extend pre-built dbt models to match your specific analytics needs.
• BI tool agnosticism: Query data with any SQL-compatible tool (Tableau, Looker, Mode, Power BI, custom dashboards).
• Version-controlled tracking: Update tracking definitions without code deployments using schema evolution and remote configuration.

Workflow Examples:

• Product teams self-serve feature analytics without engineering support
• Data science teams access raw events for ML feature engineering
• Marketing teams activate segments in real-time via reverse ETL
• Finance teams build attribution models with complete customer journeys

With Snowplow, teams gain maximum flexibility: event data flows to your chosen warehouse in your schema, ready for any downstream tool. Unlike packaged analytics products that lock you into their UI and metrics, Snowplow delivers raw, granular data that powers any analytics workflow you design.