Data Engineering Data Pipeline

This one's for when you're building or refactoring data pipelines and need architectural guardrails. It covers the full stack: batch and streaming ingestion, orchestration with Airflow or Prefect, transformations in dbt or Spark, and storage patterns like Delta Lake and Iceberg. The strength here is the opinionated guidance on data quality frameworks, monitoring setups, and cost optimization tactics like partition sizing and lifecycle policies. It's practical about failure modes and gives you concrete implementation patterns rather than just theory. Best for mid to senior engineers who know the tools but want systematic approaches to common pipeline problems like incremental loading, schema evolution, and observability.