What is a DAG in data engineering? graph basics

A Directed Acyclic Graph (DAG) in data engineering is a data structure used to define tasks and their dependencies in a way that ensures tasks execute sequentially without circular dependencies, similar to the order of steps in a recipe. DAGs are essential for managing dependencies, enabling parallelism, and isolating failures in data pipelines, with Apache Airflow being a prominent tool that structures workflows as DAGs. Nodes represent tasks like data extractions and transformations, while edges define the execution order, ensuring tasks like extracting from databases and APIs can run in parallel before subsequent transformations and loads occur. DAGs can be automatically constructed in tools like dbt, and other orchestration platforms such as Prefect and Dagster offer DAG-based workflows with varying user experiences. Cycles are not allowed in DAGs as they create infinite loops, and orchestration tools prevent such cyclic definitions, necessitating iterative behavior to be handled within individual task nodes. While a DAG is a structural concept outlining task order, it is distinct from data lineage, which records the actual data flow through the tasks, positioning the DAG as a blueprint for execution.

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