Data movement patterns explained (ETL, ELT, CDC & more)

The formalization of data movement patterns—ETL, ELT, CDC, reverse ETL—signals a maturation in how we architect analytics infrastructure. What strikes me is that dbt Labs is positioning transformation as the connective tissue between raw data and business outcomes, not an afterthought. This matters operationally because teams can no longer afford ambiguity about *when* and *where* transformation happens. CDC patterns, for instance, enable real-time analytics without full table scans, but require different lineage tracking and monitoring than batch ELT. The shift toward governance and AI tags suggests organizations are finally treating data pipelines as first-class products. My recommendation: audit your current movement patterns honestly. Most teams I work with have accidental hybrids—some CDC flows, some batch, minimal documentation. Standardizing around patterns, documenting them explicitly in dbt and your orchestration tool, and measuring quality metrics per pattern gives you the operational clarity needed to scale.