# Step 1: Adding documents to Amazon S3
Before you run an Amazon Comprehend entities analysis job on your dataset, you create an Amazon S3 bucket to host the data, metadata, and the Amazon Comprehend entities analysis output.
**Topics**
+ [Downloading the sample dataset](#tutorial-search-metadata-add-documents-download-extract)
+ [Creating an Amazon S3 bucket](#tutorial-search-metadata-add-documents-create-bucket)
+ [Creating data and metadata folders in your S3 bucket](#tutorial-search-metadata-add-documents-data-metadata)
+ [Uploading the input data](#tutorial-search-metadata-add-documents-upload-data)
## Downloading the sample dataset
Before Amazon Comprehend can run an entities analysis job on your data, you must download and extract the dataset and upload it to an S3 bucket.
### To download and extract the dataset (Console)
1. Download the [tutorial-dataset.zip](https://docs.aws.amazon.com/kendra/latest/dg/samples/tutorial-dataset.zip) folder on your device.
1. Extract the `tutorial-dataset` folder to access the `data` folder.
### To download and extract the dataset (Terminal)
1. To download the `tutorial-dataset`, run the following command on a terminal window:
------
#### [ Linux ]
```
curl -o path/tutorial-dataset.zip https://docs.aws.amazon.com/kendra/latest/dg/samples/tutorial-dataset.zip
```
Where:
+ *path/* is the local filepath to the location you want to save the zip folder in.
------
#### [ macOS ]
```
curl -o path/tutorial-dataset.zip https://docs.aws.amazon.com/kendra/latest/dg/samples/tutorial-dataset.zip
```
Where:
+ *path/* is the local filepath to the location you want to save the zip folder in.
------
#### [ Windows ]
```
curl -o path/tutorial-dataset.zip https://docs.aws.amazon.com/kendra/latest/dg/samples/tutorial-dataset.zip
```
Where:
+ *path/* is the local filepath to the location you want to save the zip folder in.
------
1. To extract the data from the zip folder, run the following command on the terminal window:
------
#### [ Linux ]
```
unzip path/tutorial-dataset.zip -d path/
```
Where:
+ *path/* is the local filepath to your saved zip folder.
------
#### [ macOS ]
```
unzip path/tutorial-dataset.zip -d path/
```
Where:
+ *path/* is the local filepath to your saved zip folder.
------
#### [ Windows ]
```
tar -xf path/tutorial-dataset.zip -C path/
```
Where:
+ *path/* is the local filepath to your saved zip folder.
------
At the end of this step, you should have the extracted files in a decompressed folder called `tutorial-dataset`. This folder contains a `README` file with an Apache 2.0 open source attribution and a folder called `data` containing the dataset for this tutorial. The dataset consists of 100 files with `.story` extensions.
## Creating an Amazon S3 bucket
After downloading and extracting the sample data folder, you store it in an Amazon S3 bucket.
**Important**
The name of an Amazon S3 bucket must be unique across all of AWS.
### To create an S3 bucket (Console)
1. Sign in to the AWS Management Console and open the Amazon S3 console at [https://console.aws.amazon.com/s3/](https://console.aws.amazon.com/s3/).
1. In **Buckets**, choose **Create bucket**.
1. For **Bucket name**, enter a unique name.
1. For **Region**, choose the AWS region where you want to create the bucket.
**Note**
You must choose a region that supports both Amazon Comprehend and Amazon Kendra. You cannot change the region of a bucket after you have created it.
1. Keep the default settings for **Block Public Access settings for this bucket**, **Bucket Versioning**, and **Tags**.
1. For **Default encryption**, choose **Disable**.
1. Keep the default settings for the **Advanced settings**.
1. Review your bucket configuration and then choose **Create bucket**.
### To create an S3 bucket (AWS CLI)
1. To create an S3 bucket, use the [create-bucket](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/s3control/create-bucket.html) command in the AWS CLI:
------
#### [ Linux ]
```
aws s3api create-bucket \
--bucket amzn-s3-demo-bucket \
--region aws-region \
--create-bucket-configuration LocationConstraint=aws-region
```
Where:
+ amzn-s3-demo-bucket is your bucket name,
+ *aws-region* is the region you want to create your bucket in.
------
#### [ macOS ]
```
aws s3api create-bucket \
--bucket amzn-s3-demo-bucket \
--region aws-region \
--create-bucket-configuration LocationConstraint=aws-region
```
Where:
+ amzn-s3-demo-bucket is your bucket name,
+ *aws-region* is the region you want to create your bucket in.
------
#### [ Windows ]
```
aws s3api create-bucket ^
--bucket amzn-s3-demo-bucket ^
--region aws-region ^
--create-bucket-configuration LocationConstraint=aws-region
```
Where:
+ amzn-s3-demo-bucket is your bucket name,
+ *aws-region* is the region you want to create your bucket in.
------
**Note**
You must choose a region that supports both Amazon Comprehend and Amazon Kendra. You cannot change the region of a bucket after you have created it.
1. To ensure that your bucket was created successfully, use the [list](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/s3/ls.html) command:
------
#### [ Linux ]
```
aws s3 ls
```
------
#### [ macOS ]
```
aws s3 ls
```
------
#### [ Windows ]
```
aws s3 ls
```
------
## Creating data and metadata folders in your S3 bucket
After creating your S3 bucket, you create data and metadata folders inside it.
### To create folders in your S3 bucket (Console)
1. Open the Amazon S3 console at [https://console.aws.amazon.com/s3/](https://console.aws.amazon.com/s3/).
1. In **Buckets**, click on the name of your bucket from the list of buckets.
1. From the **Objects** tab, choose **Create folder**.
1. For the new folder name, enter **data**.
1. For the encryption settings, choose **Disable**.
1. Choose **Create folder**.
1. Repeat steps 3 to 6 to create another folder for storing the Amazon Kendra metadata and name the folder created in step 4 **metadata**.
### To create folders in your S3 bucket (AWS CLI)
1. To create the `data` folder in your S3 bucket, use the [put-object](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/s3api/put-object.html) command in the AWS CLI:
------
#### [ Linux ]
```
aws s3api put-object \
--bucket amzn-s3-demo-bucket \
--key data/
```
Where:
+ amzn-s3-demo-bucket is your bucket name.
------
#### [ macOS ]
```
aws s3api put-object \
--bucket amzn-s3-demo-bucket \
--key data/
```
Where:
+ amzn-s3-demo-bucket is your bucket name.
------
#### [ Windows ]
```
aws s3api put-object ^
--bucket amzn-s3-demo-bucket ^
--key data/
```
Where:
+ amzn-s3-demo-bucket is your bucket name.
------
1. To create the `metadata` folder in your S3 bucket, use the [put-object](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/s3api/put-object.html) command in the AWS CLI:
------
#### [ Linux ]
```
aws s3api put-object \
--bucket amzn-s3-demo-bucket \
--key metadata/
```
Where:
+ amzn-s3-demo-bucket is your bucket name.
------
#### [ macOS ]
```
aws s3api put-object \
--bucket amzn-s3-demo-bucket \
--key metadata/
```
Where:
+ amzn-s3-demo-bucket is your bucket name.
------
#### [ Windows ]
```
aws s3api put-object ^
--bucket amzn-s3-demo-bucket ^
--key metadata/
```
Where:
+ amzn-s3-demo-bucket is your bucket name.
------
1. To ensure that your folders were created successfully, check the contents of your bucket using the [list](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/s3/ls.html) command:
------
#### [ Linux ]
```
aws s3 ls s3://amzn-s3-demo-bucket/
```
Where:
+ amzn-s3-demo-bucket is your bucket name.
------
#### [ macOS ]
```
aws s3 ls s3://amzn-s3-demo-bucket/
```
Where:
+ amzn-s3-demo-bucket is your bucket name.
------
#### [ Windows ]
```
aws s3 ls s3://amzn-s3-demo-bucket/
```
Where:
+ amzn-s3-demo-bucket is your bucket name.
------
## Uploading the input data
After creating your data and metadata folders, you upload the sample dataset into the `data` folder.
### To upload the sample dataset into the data folder (Console)
1. Open the Amazon S3 console at [https://console.aws.amazon.com/s3/](https://console.aws.amazon.com/s3/).
1. In **Buckets**, click on the name of your bucket from the list of buckets and then click on `data`.
1. Choose **Upload** and then choose **Add files**.
1. In the dialog box, navigate to the `data` folder inside the `tutorial-dataset` folder in your local device, select all the files, and then choose **Open**.
1. Keep the default settings for **Destination**, **Permissions**, and **Properties**.
1. Choose **Upload**.
### To upload the sample dataset into the data folder (AWS CLI)
1. To upload the sample data into the `data` folder, use the [copy](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/s3/cp.html) command in the AWS CLI:
------
#### [ Linux ]
```
aws s3 cp path/tutorial-dataset/data s3://amzn-s3-demo-bucket/data/ --recursive
```
Where:
+ *path/* is the filepath to the `tutorial-dataset` folder on your device,
+ amzn-s3-demo-bucket is your bucket name.
------
#### [ macOS ]
```
aws s3 cp path/tutorial-dataset/data s3://amzn-s3-demo-bucket/data/ --recursive
```
Where:
+ *path/* is the filepath to the `tutorial-dataset` folder on your device,
+ amzn-s3-demo-bucket is your bucket name.
------
#### [ Windows ]
```
aws s3 cp path/tutorial-dataset/data s3://amzn-s3-demo-bucket/data/ --recursive
```
Where:
+ *path/* is the filepath to the `tutorial-dataset` folder on your device,
+ amzn-s3-demo-bucket is your bucket name.
------
1. To ensure that your dataset files were uploaded successfully to your `data` folder, use the [list](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/s3/ls.html) command in the AWS CLI:
------
#### [ Linux ]
```
aws s3 ls s3://amzn-s3-demo-bucket/data/
```
Where:
+ amzn-s3-demo-bucket is the name of your S3 bucket.
------
#### [ macOS ]
```
aws s3 ls s3://amzn-s3-demo-bucket/data/
```
Where:
+ amzn-s3-demo-bucket is the name of your S3 bucket.
------
#### [ Windows ]
```
aws s3 ls s3://amzn-s3-demo-bucket/data/
```
Where:
+ amzn-s3-demo-bucket is the name of your S3 bucket.
------
At the end of this step, you have an S3 bucket with your dataset stored inside the `data` folder, and an empty `metadata` folder, which will store your Amazon Kendra metadata.