Metadata-Version: 2.1
Name: dataflows-aws
Version: 0.2.0
Summary: A utility library for working with Table Schema in Python
Home-page: https://github.com/frictionlessdata/dataflows-aws
Author: Open Knowledge Foundation
Author-email: info@okfn.org
License: MIT
Description: # dataflows-aws
        
        [![Travis](https://travis-ci.org/frictionlessdata/dataflows-aws.svg?branch=master)](https://travis-ci.org/frictionlessdata/dataflows-aws)
        [![Coveralls](http://img.shields.io/coveralls/frictionlessdata/dataflows-aws.svg?branch=master)](https://coveralls.io/r/frictionlessdata/dataflows-aws?branch=master)
        
        Dataflows's processors to work with AWS
        
        ## Features
        
        - `dump_to_s3` processor
        - `change_acl_on_s3` processor
        
        ## Contents
        
        <!--TOC-->
        
          - [Getting Started](#getting-started)
            - [Installation](#installation)
            - [Examples](#examples)
          - [Documentation](#documentation)
            - [dump_to_s3](#dump_to_s3)
            - [change_acl_on_s3](#change_acl_on_s3)
          - [Contributing](#contributing)
          - [Changelog](#changelog)
        
        <!--TOC-->
        
        ## Getting Started
        
        ### Installation
        
        The package use semantic versioning. It means that major versions  could include breaking changes. It's recommended to specify `package` version range in your `setup/requirements` file e.g. `package>=1.0,<2.0`.
        
        ```bash
        $ pip install dataflows-aws
        ```
        
        ### Examples
        
        These processors have to be used as a part of data flow. For example:
        
        ```python
        flow = Flow(
            load('data/data.csv'),
            dump_to_s3(
                bucket=bucket,
                acl='private',
                path='my/datapackage',
                endpoint_url=os.environ['S3_ENDPOINT_URL'],
            ),
        )
        flow.process()
        ```
        
        ## Documentation
        
        ### dump_to_s3
        
        Saves the DataPackage to AWS S3.
        
        #### Parameters
        
        - `bucket` - Name of the bucket where DataPackage will be stored (should already be created!)
        - `acl` - ACL to provide the uploaded files. Default is 'public-read' (see [boto3 docs](http://boto3.readthedocs.io/en/latest/reference/services/s3.html#S3.Client.put_object) for more info).
        - `path` - Path (key/prefix) to the DataPackage. May contain format string available for `datapackage.json` Eg: `my/example/path/{owner}/{name}/{version}`
        - `content_type` - content type to use when storing files in S3. Defaults to text/plain (usual S3 default is binary/octet-stream but we prefer text/plain).
        - `endpoint_url` - api endpoint to allow using S3 compatible services (e.g. 'https://ams3.digitaloceanspaces.com')
        
        ### change_acl_on_s3
        
        Changes ACL of object in given Bucket with given path aka prefix.
        
        #### Parameters
        
        - `bucket` - Name of the bucket where objects are stored
        - `acl` - Available options `'private'|'public-read'|'public-read-write'|'authenticated-read'|'aws-exec-read'|'bucket-owner-read'|'bucket-owner-full-control'`
        - `path` - Path (key/prefix) to the DataPackage.
        - `endpoint_url` - api endpoint to allow using S3 compatible services (e.g. 'https://ams3.digitaloceanspaces.com')
        
        ## Contributing
        
        The project follows the [Open Knowledge International coding standards](https://github.com/okfn/coding-standards).
        
        The recommended way to get started is to create and activate a project virtual environment.
        To install package and development dependencies into your active environment:
        
        ```
        $ make install
        ```
        
        To run tests with linting and coverage:
        
        ```bash
        $ make test
        ```
        
        For linting, `pylama` (configured in `pylama.ini`) is used. At this stage it's already
        installed into your environment and could be used separately with more fine-grained control
        as described in documentation - https://pylama.readthedocs.io/en/latest/.
        
        For example to sort results by error type:
        
        ```bash
        $ pylama --sort <path>
        ```
        
        For testing, `tox` (configured in `tox.ini`) is used.
        It's already installed into your environment and could be used separately with more fine-grained control as described in documentation - https://testrun.org/tox/latest/.
        
        For example to check subset of tests against Python 2 environment with increased verbosity.
        All positional arguments and options after `--` will be passed to `py.test`:
        
        ```bash
        tox -e py37 -- -v tests/<path>
        ```
        
        Under the hood `tox` uses `pytest` (configured in `pytest.ini`), `coverage`
        and `mock` packages. These packages are available only in tox envionments.
        
        ## Changelog
        
        Here described only breaking and the most important changes. The full changelog and documentation for all released versions can be found in the nicely formatted [commit history](https://github.com/frictionlessdata/dataflows-aws/commits/master).
        
        #### v0.x
        
        - an initial processors implementation
Keywords: frictionless data,open data,json schema,table schema,data package,tabular data package
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: develop
