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Bumping version to 0.3.1
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.gitignore

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testing/*requirements*.txt
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testing/coverage/*
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/docs/coverage/

CONTRIBUTING.md

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See the [LICENSE](https://github.com/awslabs/aws-data-wrangler/blob/master/LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution.
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We may ask you to sign a [Contributor License Agreement (CLA)](http://en.wikipedia.org/wiki/Contributor_License_Agreement) for larger changes.
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## Instructions
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* AWS Data Wrangler practically only makes integrations. So we prefer to dedicate our energy / time writing integration tests instead of unit tests. We really like an end-to-end approach for all features.
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* All integration tests are between a local Docker container and a remote/real AWS service.
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* We have a Docker recipe to set up the local end (testing/Dockerfile).
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* We have a Cloudformation to set up the AWS end (testing/template.yaml).
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### Steps
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**DISCLAIMER**: Make sure to know what you are doing. This steps will charge some services on your AWS account. And requires a minimum security skills to keep your environment safe.
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* Pick up a Linux or MacOS.
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* Install Python 3.6+
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* Install Docker and configure at least 4 cores and 8 GB of memory
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* Fork the AWS Data Wrangler repository and clone that into your development environment
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* Go to the project's directory create a Python's virtual environment for the project (**python -m venv venv && source venv/bin/activate**)
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* Run **./setup-dev-env.sh**
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* Go to the *testing* directory
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* Configure the parameters.json file with your AWS environment infos (Make sure that your Redshift will not be open for the World! Configure your security group to only give access for your IP.)
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* Deploy the Cloudformation stack **./deploy-cloudformation.sh**
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* Open the docker image **./open-image.sh**
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* Inside the image you finally can run **./run-tests.sh**

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