Asset vs Task Orchestration

Concepts covered: paAssetBasedOrchestration, paTaskBasedOrchestration

Two philosophies compete in modern orchestration. Task-based orchestration, exemplified by Airflow, treats the work as the primary object: define tasks, declare dependencies between them, and trust that the data will follow. Asset-based orchestration, exemplified by Dagster, treats the data as the primary object: declare the assets the pipeline produces, declare the dependencies between assets, and let the orchestrator infer the work. The two models produce equivalent pipelines on simple cases. They diverge sharply on lineage, observability, partial refreshes, and the operability of large deployments. Task-Based: Work Is the Object Airflow's model places tasks at the center. A task has a definition, a retry policy, an upstream dependency, and a downstream dependency. Tasks happen to read a

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This section is part of the Orchestration and Dependencies: Advanced lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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