Metadata-Version: 2.1
Name: dataflow-cookiecutter
Version: 1.0.0a3
Summary: Command-line utility for creating Dataflow projects
Home-page: UNKNOWN
Author: Lester James V. Miranda
Author-email: ljvmiranda@gmail.com
License: UNKNOWN
Description: # dataflow-cookiecutter [![Build Status](https://dev.azure.com/ljvmiranda/ljvmiranda/_apis/build/status/ljvmiranda921.dataflow-cookiecutter?branchName=master)](https://dev.azure.com/ljvmiranda/ljvmiranda/_build/latest?definitionId=5&branchName=master) ![PyPI](https://img.shields.io/pypi/v/dataflow-cookiecutter?color=light-green&label=pypi&logo=python&logoColor=white)
        
        **Tired of copy-pasting your ad-hoc Dataflow modules?** Then you can use this
        [cookiecutter](https://github.com/cookiecutter/cookiecutter) command-line
        tool to easily generate standardized Dataflow templates! :zap:
        
        ![dataflow-cookiecutter demo](assets/demo.gif)
        
        ## Installation
        
        You can install `dataflow-cookiecutter` from PyPI:
        
        ```sh
        pip install dataflow-cookiecutter
        ```
        
        In addition, you can also clone this repository and install locally:
        
        ```sh
        git clone https://github.com/ljvmiranda921/dataflow-cookiecutter.git
        cd dataflow-cookiecutter
        python3 setup.py install
        ```
        
        ## Usage
        
        You can create a Dataflow project by executing the command:
        
        ```sh
        $ dataflow-cookiecutter new
        ```
        
        **Choose from a variety of our premade templates.** See all available templates
        by running `dataflow-cookiecutter ls`. For example, you can create a Google
        Cloud Storage (GCS) to BigQuery (BQ) pipeline via:
        
        ```sh
        $ dataflow-cookiecuter new -t GCSToBQ
        ```
        
        Lastly, our templates are **highly-compatible to your trusty, old
        `cookiecutter`** command-line tool (be sure to use `cookiecutter`>=1.7.1!):
        
        ```sh
        $ cookiecutter https://github.com/ljvmiranda921/dataflow-cookiecutter \
           --directory <directory-to-desired-template> 
        ```
        
        
        ## FAQ
        
        - **Why are you still wrapping cookiecutter?**  This started as my learning
            project to see how cookiecutter's internals work. While building the alpha
            version, I realized that I can add more functionality to this CLI more than
            templating, so wrapping Cookiecutter seems to be a good approach.
        - **I already have cookiecutter, can I use it with your templates?** Yes of
            course! Look at the Usage section above! However, ensure that your
            cookecutter version is `>=1.7.1` so that you can use the `--directory`
            flag!
        - **Why are you using Python 3 for Dataflow templates?** It's 2020, we
            shouldn't be supporting [legacy Python](https://pythonclock.org/?1)
            anymore. Besides, Dataflow now has [streaming support in Python
            3](https://cloud.google.com/blog/products/data-analytics/introducing-python-3-python-streaming-support-from-cloud-dataflow).
            See more developments for Beam support in Python 3  in their
            [issue tracker](https://issues.apache.org/jira/browse/BEAM-1251).
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
