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id=\"project-overview\">Project Overview\u003C\u002Fh2>\u003Cp>Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. Workflows (DAGs) are defined as Python code, which makes them more maintainable, versionable, testable, and collaborative; the scheduler executes your tasks on an array of workers following the specified dependencies, while rich command line utilities and a rich user interface make it easy to visualize production pipelines, monitor progress, and troubleshoot issues.\u003C\u002Fp>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Value\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>Platform\u003C\u002Ftd>\n\u003Ctd>Python environment, Docker, and more\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Language\u003C\u002Ftd>\n\u003Ctd>Python\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>License\u003C\u002Ftd>\n\u003Ctd>Apache-2.0\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Core concepts\u003C\u002Ftd>\n\u003Ctd>DAGs, task dependencies, scheduling and monitoring\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Cblockquote>\n\u003Cp>Workflows as code: define once, with scheduling, monitoring, and collaboration built in.\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Ch2 id=\"key-features\">Key Features\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Workflows as code\u003C\u002Fstrong>: DAGs are written in Python, so version control and code review come naturally\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Task scheduling\u003C\u002Fstrong>: the scheduler runs tasks on multiple workers according to dependencies and timing\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Visual monitoring\u003C\u002Fstrong>: the web UI shows pipeline status, progress, and failure details\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Command line tools\u003C\u002Fstrong>: rich CLI utilities perform complex operations on DAGs\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Broad scenarios\u003C\u002Fstrong>: orchestrates ETL, ELT, machine learning, and MLOps workflows\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Container deployment\u003C\u002Fstrong>: an official Docker image eases running on various infrastructures\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"target-audience\">Target Audience\u003C\u002Fh2>\u003Cul>\n\u003Cli>Data engineers\u003C\u002Fli>\n\u003Cli>Data platform teams\u003C\u002Fli>\n\u003Cli>MLOps engineers\u003C\u002Fli>\n\u003Cli>Anyone scheduling batch jobs\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"quick-start\">Quick Start\u003C\u002Fh2>\u003Col>\n\u003Cli>Install from PyPI:\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-bash\">pip install apache-airflow\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"2\">\n\u003Cli>Start a local development environment in one command:\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-bash\">airflow standalone\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"3\">\n\u003Cli>Open the web UI and explore the bundled example DAGs\u003C\u002Fli>\n\u003Cli>Follow the official tutorial to write your first DAG\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Ch2 id=\"core-advantages\">Core Advantages\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Collaborative by design\u003C\u002Fstrong>: workflows live as code, ready for review, rollback, and testing\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Intuitive UI\u003C\u002Fstrong>: pipeline health is visible at a glance, lowering debugging cost\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Mature ecosystem\u003C\u002Fstrong>: an Apache Software Foundation project with a large community and many integrations\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"system-requirements\">System Requirements\u003C\u002Fh2>\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Requirement\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>Runtime\u003C\u002Ftd>\n\u003Ctd>Python or Docker\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Deployment\u003C\u002Ftd>\n\u003Ctd>PyPI install or the official Docker image\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"notes\">Notes\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Learning curve\u003C\u002Fstrong>: the DAG model and scheduling semantics take some study; finish the official tutorial before going live\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Production deployment\u003C\u002Fstrong>: \u003Ccode>airflow standalone\u003C\u002Fcode> is for local experience only; plan the metadata database, workers, and other components yourself in production\u003C\u002Fli>\n\u003C\u002Ful>\n",1793228992029]