How workflow orchestration goes beyond scheduling, what a DAG is, tools like Apache Airflow, and practices for retries, dependencies, and alerting.
What makes a data pipeline scalable: parallel and distributed processing, less data movement, managing compute resources, and designing for change.
How DevOps applies to data engineering: version control for pipelines, CI and automated tests, infrastructure as code, deployment, and incident response.
What the modern data stack is, how cloud platforms changed data engineering, the managed services they offer, and how to design data systems for agility.
