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        "author": "Max Zheng",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "datarunner\n==========\n\nA simple data workflow runner that helps you write better ETL scripts using reusable code pieces.\n\nQuick Start Tutorial\n====================\n\nInstall using pip::\n\n    pip install datarunner\n\nThen write a few steps (functions, classes, etc) that can be called, pass to `datarunner.Workflow`, and call run():\n\n.. code-block:: python\n\n    from datarunner import Workflow, Step\n\n\n    def setup():\n        print('Ready to go!')\n\n    def extract():\n        return 'data'\n\n    def transform(data):\n        return data + ' using reusable code pieces, like Lego.'\n\n    class Load(Step):\n        \"\"\" Sub-class Step to customize the callable \"\"\"\n        def __init__(self, destination):\n            super().__init__()\n            self.destination = destination\n\n        def __str__(self):\n            return f'Load(\"{self.destination}\")'\n\n        def run(self, data):\n            print(f'Loading {data}')\n\n    flow = Workflow(setup,\n                    table_name1=[extract, transform, Load('example')])\n    flow.run()\n\nIt produces the following output::\n\n    setup\n    Ready to go!\n\n    table_name1\n    --------------------------------------------------------------------------------\n    extract\n    >> transform\n    >> Load(\"example\")\n    Loading data using reusable code pieces, like Lego.\n\nWe can also use `>>` operator to convey the same flow:\n\n.. code-block:: python\n\n    flow = (Workflow()\n            >> setup\n\n            << 'table_name1'\n            >> extract >> transform >> Load('example'))\n    flow.run()\n\nTo make the workflow more flexible (e.g. write to different dataset), use templates to provide some values at run time:\n\n.. code-block:: python\n\n    class Load(Step):\n        TEMPLATE_ATTRS = ['destination']\n\n        \"\"\" Sub-class Step to customize the callable \"\"\"\n        def __init__(self, destination):\n            super().__init__()\n            self.destination = destination\n\n        def __str__(self):\n            return f'Load(\"{self.destination}\")'\n\n        def run(self, data):\n            print(f'Loading {data}')\n\n    flow = Workflow() >> extract >> transform >> Load('{dataset}.table_name1')\n    flow.run(dataset='staging')\n\nIt produces the following output::\n\n   extract\n   >> transform\n   >> Load(\"staging.table_name1\")\n   Loading data using reusable code pieces, like Lego.\n\nAnd finally, to test the workflow::\n\n   def test_flow():\n      assert \"\"\"\\\n   extract\n   >> transform\n   >> Load(\"{dataset}.table_name1\")\"\"\" == str(flow)\n\nWorkflow Layout\n===============\n\nA recommended file layout for your ETL package::\n\n   my_package/steps/__init__.py            # Generic / common steps\n   my_package/steps/bigquery.py            # Group of steps for a specific service, like BigQuery.\n   my_package/datasource1.py               # ETL workflow for a single data source with steps specifc for the source\n   my_package/datasource2.py               # ETL workflow for another data source\n\nInside of `datasource*.py`, it should define `flow = Workflow(...)`, but not run. From your ETL script, it should call\n`flow.run()` to run the workflow. This ensures the workflow is properly constructed when imported and can be used for\ntesting without running it.\n\nLinks & Contact Info\n====================\n\n| PyPI Package: https://pypi.org/project/datarunner/\n| GitHub Source: https://github.com/maxzheng/datarunner\n| Report Issues/Bugs: https://github.com/maxzheng/datarunner/issues\n|\n| Creator: https://www.linkedin.com/in/maxzheng\n\n\n",
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