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        "description": "sebflow\n=======\n\n``Airflow`` is awesome\n\nBut it's confusing.\n\n- So many logs\n- How do I run anything?\n- Did I just run something?\n- I think I just ran something...\n- Really, too many logs\n\nAnd bulky.\n\n- You have to launch a webserver and a scheduler to run a dag.\n\nAnd it doesn't work on Windows...\n\n``Sebflow`` is a stripped-down Airflow implementation that runs locally on both Windows and Linux, only uses PythonOperators, and allows you to use your computer's scheduling programs to run a dag.\n\nSebFlow is ripped almost entirely from the Incubator-Airflow project: a team of incredible programmers. I have learned so much from their code.\n\n.. code-block:: pycon\n\n import time\n\n from sebflow.models import DAG\n from sebflow.operators.python_operator import PythonOperator\n from sebflow.exceptions import SebOnPurposeError\n\n\n  def sleep1():\n      time.sleep(1)\n\n\n def sleep5(*args, **kwargs):\n     time.sleep(5)\n\n\n def fail_on_purpose(arg1, arg2):\n     raise SebOnPurposeError()\n\n\n dag = DAG(\n     'test',\n     schedule_interval='@daily',\n\n )\n\n t1 = PythonOperator(\n     task_id='task1',\n     dag=dag,\n     python_callable=sleep1)\n\n t2 = PythonOperator(\n     task_id='task12',\n     dag=dag,\n     python_callable=fail_on_purpose,\n     op_args=['asdf', 'asdfasdf']\n )\n\n t3 = PythonOperator(\n     task_id='task13',\n     dag=dag,\n     python_callable=sleep5,\n     op_args=['asdf', 'asdfsdf'],\n     op_kwargs={'kwarg1': 'asdfasdf', 'kwarg2': 'asdfasdf'}\n )\n\n\n t2.set_upstream(t1)\n t3.set_upstream(t1)\n\n if __name__ == '__main__':\n     dag.run()\n",
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        "summary": "programatically author and monitor data pipelines",
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