The Major Orchestrators by Name

Concepts covered: paOrchestratorTools, paAirflowDagsterPrefect

Three orchestrators dominate modern data engineering: Airflow, Dagster, and Prefect. Each ships the four responsibilities described in the previous section, but they make different choices in the API and the philosophy. Knowing the names matters because production environments have already chosen one (or, more often, are slowly migrating from one to another). Knowing what they have in common matters more, because the choice of tool changes which buttons are pressed, not what the buttons do. Apache Airflow Airflow is the oldest and most widely deployed of the three. Maxime Beauchemin started it at Airbnb in 2014, and it became an Apache project in 2016. Pipelines are declared as Python files; tasks are operators (PythonOperator, BashOperator, SQLOperator) connected with the >> operator. The

About This Interactive Section

This section is part of the Orchestration and Dependencies: Beginner 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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DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.