Metadata-Version: 2.1
Name: typhoon-orchestrator
Version: 0.0.55
Summary: Create asynchronous data pipelines and deploy to cloud or airflow
Home-page: https://github.com/typhoon-data-org/typhoon-orchestrator
Author: Typhoon Data
Author-email: info.typhoon.data@gmail.com
License: Apache 2 License
Description: 
        <p align="center">
        <br>
         <a href="https://discord.gg/XxV5MAF8Xt">Discord :sunglasses:</a> |
         <a href="https://typhoon.talkyard.net/">Forum :wave:</a> |
         <a href="https://typhoon-data-org.github.io/typhoon-orchestrator/getting-started/installation.html#with-pip">Installation :floppy_disk:</a> |
         <a href="https://typhoon-data-org.github.io/typhoon-orchestrator/index.html">Documentation :notebook: </a>
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        <img src="https://raw.githubusercontent.com/typhoon-data-org/typhoon-orchestrator/feature/docs_gitpages/docs/img/typhoon_logo_large_tagline.png" >
        </p>
        <br>
        <p align="center"><b>Create tasks in modern Python</br>Elegant YAML DAGS for Data Pipelines</br>Deploy to AWS Lambda or to your existing Airflow.</b></p>
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        <p align="center">
        <img style="margin: 10px" src="https://img.shields.io/github/license/typhoon-data-org/typhoon-orchestrator.svg" alt="Linux" height="20" />
        <img style="margin: 10px" src="https://github.com/typhoon-data-org/typhoon-orchestrator/actions/workflows/docker-image.yml/badge.svg" />
        
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        <br> 
        <a href="https://github.com/typhoon-data-org/typhoon-orchestrator/">Why Typhoon?</a> |  
         <a href="https://github.com/typhoon-data-org/typhoon-orchestrator/">Key Features</a> |
         <a href="https://github.com/typhoon-data-org/typhoon-orchestrator/">Example YAML</a> |
         <a href="https://github.com/typhoon-data-org/typhoon-orchestrator/">Installation</a>
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        # Why Typhoon?
        
        Our vision is a new generation of cloud native, asynchronous orchestrators that can handle highly dynamic workflows with ease. We crafted Typhoon from the ground up to work towards this vision. It's designed to feel familiar while still making very different design decisions where it matters. 
        
        # Why Typhoon + AWS Lambda?
        
        A Serverless orchestrator has the potential to be infinitely scalable and extremely cost efficient at the same time. We think AWS Lambda is ideal for this:
        
        - CloudWatch Events can trigger a Lambda on a schedule, so we get scheduling for free! A scheduler is the most complex piece of an orchestrator. We can do away with it completely and still be sure that our DAGs will always run on time.
        - Lambda is cheap. You get 1 million invocations for free every month.
        - Workflows can be paralellized by running tasks in parallel on different instances of the Lambda. Typhoon DAGs use batching to take full advantage of this.
        
        # Why Typhoon + Airflow?
        
        Airflow is great!
        
        ***Typhoon lets you write Airflow DAGS faster*** :rocket::
          
            **Workflow**: Typhoon YAML DAG --> Typhoon build --> Airflow DAG 
        
        Simplicity and re-usability; a toolkit designed to be loved by Data Engineers :heart:
        
        # Key features
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        <tr>
        <td width="50%">
        
        **Elegant** -  YAML; low-code and easy to learn.
        
        **Code-completion** - Fast to compose. (VS Code recommended).
        
        **Data sharing** -  data flows between tasks making it super intuitive.
        
        **Composability** -  Functions and connections combine like Lego.
        
        
        </td>
        <td><img src="https://raw.githubusercontent.com/typhoon-data-org/typhoon-orchestrator/feature/docs_gitpages/docs/img/auto-complete.gif" alt="UI Component" width="400px" align="right" style="max-width: 100%;">
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        <td width="50%">
        
        **Components** - reduce complex tasks to 1 re-usable tasks
        
        Packaged examples:
        - Glob & Compress  
        - FileSystem to DB
        - DB to FileSystem
        - DB to Snowlfake
        
        **UI**: Share pre-built components (data pipelines) with your team :raised_hands:
        
        </td>
        <td><img src="https://raw.githubusercontent.com/typhoon-data-org/typhoon-orchestrator/feature/docs_gitpages/docs/img/component_ui.gif" alt="UI Component" width="400px" align="right" style="max-width: 100%;">
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        <td width="50%">
        
        **Rich CLI & Shell**: Inspired by others; instantly familiar.
        
        **Testable Tasks** - automate DAG task tests.
        
        **Testable Python** - test functions or full DAGs with PyTest.
        
        </td>
        <td><img src="https://raw.githubusercontent.com/typhoon-data-org/typhoon-orchestrator/feature/docs_gitpages/docs/img/typhoon_cli_testing.gif" alt="UI Component" width="400px" align="right" style="max-width: 100%;">
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        # Example YAML DAG
            
        ```yaml
        name: favorite_authors
        schedule_interval: rate(1 day)
        
        tasks:
          choose_favorites:
            function: typhoon.flow_control.branch
            args:
              branches:
                - J. K. Rowling
                - George R. R. Martin
                - James Clavell
        
          get_author:
            input: choose_favorites
            function: functions.open_library_api.get_author
            args:
              author: !Py $BATCH
        
          write_author_json:
            input: get_author
            function: typhoon.filesystem.write_data    
            args:
              hook: !Hook data_lake
              data:  !MultiStep
                - !Py $BATCH['docs']
                - !Py typhoon.data.json_array_to_json_records($1)
              path: !MultiStep 
                - !Py $BATCH['docs'][0]['key']
                - !Py f'/authors/{$1}.json'
              create_intermediate_dirs: True
        ```
        
        
        ![Favorite Authors](docs/img/open_library_example_dag.png)
        *Getting the works of my favorite authors from Open Library API*
        
        
        # Installation
        
        See [documentation](https://typhoon-data-org.github.io/typhoon-orchestrator/getting-started/installation.html) for detailed guidance on installation and walkthroughs. 
        
        ## with pip (typhoon standalone)
        
        Install typhoon: 
        ```bash
        pip install typhoon-orchestrator[dev]
        ```
        Optionally, install and activate virtualenv.
        
        Then: 
        ```bash 
        typhoon init hello_world
        cd hello_world
        typhoon status
        ```
        
        This will create a directory named hello_world that serves as an example project. As in git, when we cd into the directory it will detect that it's a Typhoon project and consider that directory the base directory for Typhoon (TYPHOON_HOME).
        
        #### Adding connnections
        
        You can add a default connections as follows in the cli
        
        ```bash
        typhoon connection add --conn-id data_lake --conn-env local
        # Check that it was added
        typhoon connection ls -l
        ```
        
        ## With Docker and Airflow
        
        To deploy Typhoon with Airflow you need: 
        
        - Docker / Docker Desktop (You must use WSL2 on Windows) 
        - Download the [docker-compose.yaml][1]  (or use curl below)
        - Create a directory for your TYPHOON_PROJECTS_HOME
        
        The following sets up your project directory and gets the docker-compose.yml:
        ```bash
        TYPHOON_PROJECTS_HOME="/tmp/typhoon_projects" # Or any other path you prefer
        mkdir -p $TYPHOON_PROJECTS_HOME/typhoon_airflow_test
        cd $TYPHOON_PROJECTS_HOME/typhoon_airflow_test
        mkdir src
        curl -LfO https://raw.githubusercontent.com/typhoon-data-org/typhoon-orchestrator/master/docker-compose-af.yml
        
        docker compose -f docker-compose-af.yml up -d  
        docker-compose -f docker-compose-af.yml run --rm typhoon-af airflow initdb
        docker-compose -f docker-compose-af.yml run --rm typhoon-af typhoon status
        docker-compose -f docker-compose-af.yml run --rm typhoon-af typhoon connection add --conn-id data_lake --conn-env local  # Adding our first connection!
        docker-compose -f docker-compose-af.yml run --rm typhoon-af typhoon dag build --all
        docker restart typhoon-af # Wait while docker restarts
        ```
        
        This runs a container with only 1 service, `typhoon-af`. This has both Airflow and Typhoon installed on it ready to work with.
        
        You should be able to then check `typhoon status` and also the airlfow UI at [http://localhost:8088](http://localhost:8088)
        
        ![Airflow UI](docs/img/airflow_ui_list_after_install.png)
        *Typhoon DAGS listed in airflow UI*
        
        **Development hints are [in the docs](https://typhoon-data-org.github.io/typhoon-orchestrator/getting-started/installation.html#directories).**
        
        ![Airflow Favorite Author](docs/img/airflow_favorite_author_basic_dag.PNG)
        *Favorite Authors DAG - as displayed in airflow UI*
        
        We can extend the above task to give an example with more complexity. The tutorial for this has some more advanced tips. The airflow compiled DAG handles complex DAG structures very nicely:
        
        ![Airflow Favorite Author Extended](docs/img/airflow_favorite_author_extended_dag_graph_1.PNG)
        *Favorite Authors Extended - a complex DAG example*
        
        
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: dev
Provides-Extra: test
