Data Engineering in Production
CI/CD for pipelines, environment strategy, secrets, cost control in cloud warehouses, on-call, and how the whole stack composes in a real company
The practitioner-level layer that separates a data engineer who has built pipelines in a notebook from one who has run them in production: testing dbt models and DAGs before merge with slim CI, dev/staging/prod environment strategy and promotion, secrets management for pipeline credentials, cost optimization in cloud warehouses (clustering, auto-suspend, per-query attribution, the runaway-warehouse story), on-call and runbooks for pipeline failures, how sources, orchestration, storage, transformation, and BI actually compose end to end with real ownership boundaries, and the documentation practices that keep a pipeline alive after its author leaves.
Practice questions (5)
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Diagnosing and Preventing a Runaway Warehouse Cost Spike
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Prod Credentials Leaked Into a Dev Environment
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Designing Slim CI for a Slow, Expensive dbt Project
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A 3am Pipeline Failure With No Usable Runbook
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A Metric Changed Meaning and Nobody Downstream Was Told
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