dbt and Snowflake - Data modelling challenges
Curious if anyone here has experience building and deploying dbt-based bioanalytical/life sciences data models in Snowflake.
I’m particularly interested in learning how others have approached a few challenges:
• Incremental modeling when source systems lack reliable change tracking (e.g., no updated_timestamp, weak CDC support, limited audit metadata). How did you handle incrementals, late-arriving changes, reconciliation, or full vs hybrid refresh strategies?
• Architecting across multiple source systems, especially in environments supporting legacy-to-modern migrations. How did you model, harmonize, and reconcile data across systems while maintaining lineage, historical continuity, and a stable consumption layer?
• Life sciences / regulated environments (GxP, LIMS, clinical, bioanalytical, etc.): any lessons learned around validation, auditability, governance, semantic modeling, or balancing flexibility with compliance?
Would love to hear about patterns, tradeoffs, war stories, or things you wish you had done differently. Thanks in advance!