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            "Environment :: Web Environment",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Topic :: Internet :: WWW/HTTP :: Dynamic Content",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# AWS Extensions for datapackage-pipelines\n\n[![Build Status](https://travis-ci.org/frictionlessdata/datapackage-pipelines-aws.svg?branch=master)](https://travis-ci.org/frictionlessdata/datapackage-pipelines-aws)\n\n## Install\n\n```\n# clone the repo and install it wit pip\n\ngit clone https://github.com/frictionlessdata/datapackage-pipelines-aws.git\npip install -e .\n```\n\n## Usage\n\nYou can use datapackage-pipelines-aws as a plugin for [dpp](https://github.com/frictionlessdata/datapackage-pipelines#datapackage-pipelines). In pipeline-spec.yaml it will look like this\n\n```yaml\n  ...\n  - run: aws.dump.to_s3\n```\n\nYou will need AWS credentials to be set up. See [the guide to set up the credentials](http://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/credentials.html)\n\n### dump.to_s3\n\nSaves the DataPackage to AWS S3.\n\n_Parameters:_\n\n* `bucket` - Name of the bucket where DataPackage will be stored (should already be created!)\n* `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).\n* `path` - Path (key/prefix) to the DataPackage. May contain format string available for `datapackage.json` Eg: `my/example/path/{owner}/{name}/{version}`\n* `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).\n* `endpoint_url` - api endpoint to allow using S3 compatible services (e.g. 'https://ams3.digitaloceanspaces.com')\n\n_Example:_\n\n```yaml\ndatahub:\n  title: datahub-to-s3\n  pipeline:\n    -\n      run: load_metadata\n      parameters:\n        url: http://example.com/my-datapackage/datapackage.json\n    -\n      run: load_resource\n      parameters:\n        url: http://example.com/my-datapackage/datapackage.json\n        resource: my-resource\n    -\n      run: aws.dump.to_s3\n      parameters:\n        bucket: my.bucket.name\n        path: path/{owner}/{name}/{version}\n    -\n      run: aws.dump.to_s3\n      parameters:\n        bucket: my.another.bucket\n        path: another/path/{version}\n        acl: private\n```\n\nExecuting pipeline above will save DataPackage in the following directories on S3:\n* my.bucket.name/path/my-name/py-package-name/latest/...\n* my.bucket.name/another/path/latest/...\n\n\n### change_acl\n\nChanges ACL of object in given Bucket with given path aka prefix.\n\n_Parameters:_\n\n* `bucket` - Name of the bucket where objects are stored\n* `acl` - Available options `'private'|'public-read'|'public-read-write'|'authenticated-read'|'aws-exec-read'|'bucket-owner-read'|'bucket-owner-full-control'`\n* `path` - Path (key/prefix) to the DataPackage.\n\n_Example:_\n\n```yaml\ndatahub:\n  title: datahub-to-s3\n  pipeline:\n    -\n      run: load_metadata\n      parameters:\n        url: http://example.com/my-datapackage/datapackage.json\n    -\n      run: load_resource\n      parameters:\n        url: http://example.com/my-datapackage/datapackage.json\n        resource: my-resource\n    -\n      run: aws.dump.to_s3\n      parameters:\n        bucket: my.bucket.name\n        path: path/{owner}/{name}/{version}\n    -\n      run: aws.change_acl\n      parameters:\n        bucket: my.another.bucket\n        path: path/\n        acl: private\n```\n\nExecuting pipeline above will save DataPackage on S3 and change ACL to private to all keys prefixed `path`:\n\n**Note:** If path parameter is not set this will change ACL for all object in given bucket",
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