Building a scalable data platform requires choosing an orchestrator early and designing for composability. Reactive, tool-by-tool growth leads to fragmentation, alert fatigue, and brittle pipelines. Key principles include centralizing orchestration as a control plane, using loosely coupled components with abstraction layers, and externalizing configuration. Dagster's multi-code-location architecture lets teams (e.g., one using Databricks/Delta Lake, another using Snowflake/dbt) work independently while sharing unified lineage, cataloging, and observability. Case studies from smava (zero downtime, 15-minute onboarding), Big Cartel (solo engineer running enterprise-grade monitoring), and Otto (eliminating YAML hell across six teams) illustrate the payoff. Architecture patterns covered include ETL, ELT, and Lakehouse, with most production platforms combining all three under consistent orchestration.
