Creating a dataset and a schema (console)Creating a dataset and a schema (AWS CLI)Creating a dataset and a schema (AWS Python SDK)

Step 2: Creating a dataset and a schema

After you have completed Step 1: Creating a dataset group, you are ready to create a dataset. Datasets are Amazon Personalize containers for data. Datasets are organized within Amazon Personalize dataset groups.

You can create three types of historical datasets: Users, Items, and Interactions. When you create a dataset, you also create a schema for the dataset. A schema tells Amazon Personalize about the structure of your data and allows Amazon Personalize to parse the data.

You can create only one of each kind of dataset in a dataset group, and you must at minimum create an Interactions dataset. You create datasets using the Amazon Personalize console, AWS Command Line Interface (AWS CLI), or AWS SDK.

For more information, including dataset and schema requirements, see Datasets and schemas.

Topics

Creating a dataset and a schema (console)
Creating a dataset and a schema (AWS CLI)
Creating a dataset and a schema (AWS Python SDK)

Creating a dataset and a schema (console)

If this is your first dataset in your dataset group, your first dataset type will be an Interactions dataset. To create your Interactions dataset in the console, specify the dataset name and then specify a JSON schema in Avro format. If it is not your first dataset in this dataset group, choose the dataset type and then specify a name and a schema.

For information on Amazon Personalize datasets and schema requirements, see Datasets and schemas.

Note

If you just completed Step 1: Creating a dataset group and you are already on the user-item interaction page, skip to step 4 in this procedure.

To create a dataset and a schema

Open the Amazon Personalize console at https://console.aws.amazon.com/personalize/home and sign in to your account.
On the Dataset groups page, choose the dataset group you created in Step 1: Creating a dataset group. This displays the dataset group Dashboard.
In the Upload datasets section, for the type of dataset that you want to import (Amazon Personalize datasets include Interactions, Users, or Items), choose Import. The Configure < dataset type > page is displayed.
In Dataset details, for Dataset name, specify a name for your dataset.
In Schema details, for Schema selection, either choose an existing schema or choose Create new schema.
If you are creating a new schema, for Schema definition, paste in the schema JSON that matches your data. Use the examples found in Datasets and schemas as a guide.
For New schema name, specify a name for the new schema.
Choose Next and follow the instructions in Step 3: Importing your data to import your data.

Creating a dataset and a schema (AWS CLI)

To create a dataset and a schema using the AWS CLI, you first define a schema in Avro format and add it to Amazon Personalize using the CreateSchema operation. Then create a dataset using the CreateDataset operation. For information on Amazon Personalize datasets and schema requirements, see Datasets and schemas.

To create a schema and dataset

Create a schema file in Avro format and save it as a JSON file. This file should be based on the type of dataset, such as Interactions, you are creating.

The schema must match the columns in your data and the schema name must match one of the three types of datasets recognized by Amazon Personalize. The following is an example of a minimal Interactions dataset schema. For more examples, see Datasets and schemas.
```
{
  "type": "record",
  "name": "Interactions",
  "namespace": "com.amazonaws.personalize.schema",
  "fields": [
      {
          "name": "USER_ID",
          "type": "string"
      },
      {
          "name": "ITEM_ID",
          "type": "string"
      },
      {
          "name": "TIMESTAMP",
          "type": "long"
      }
  ],
  "version": "1.0"
}
```
Create a schema in Amazon Personalize by running the following command. Replace schemaName with the name of the schema, and replace file://SchemaName.json with the location of the JSON file you created in the previous step. The example shows the file as belonging to the current folder. For more information about the API, see CreateSchema.
```
aws personalize create-schema \
  --name SchemaName \
  --schema file://SchemaName.json
```
The schema Amazon Resource Name (ARN) is displayed, as shown in the following example:
```
{
  "schemaArn": "arn:aws:personalize:us-west-2:acct-id:schema/SchemaName"
}
```
Create an empty dataset by running the following command. Provide the dataset group Amazon Resource Name (ARN) from Creating a dataset group (AWS CLI) and schema ARN from the previous step. The dataset-type must match the schema name from the previous step. For more information about the API, see CreateDataset.
```
aws personalize create-dataset \
  --name Dataset Name \
  --dataset-group-arn Dataset Group ARN \
  --dataset-type Dataset Type \
  --schema-arn Schema Arn
```
The dataset ARN is displayed, as shown in the following example.
```
{
  "datasetArn": "arn:aws:personalize:us-west-2:acct-id:dataset/DatasetName/INTERACTIONS"
}
```
Record the dataset ARN for later use. After you have created a dataset, you are ready to import your training data. See Step 3: Importing your data.

Creating a dataset and a schema (AWS Python SDK)

To create a dataset and a schema using the AWS Python SDK, you first define a schema in Avro format and add it to Amazon Personalize using the CreateSchema operation. Then create a dataset using the CreateDataset operation. For information on Amazon Personalize datasets and schema requirements, see Datasets and schemas.

To create a schema and a dataset

Create a schema file in Avro format and save it as a JSON file in your working directory.

The schema must match the columns in your data and the schema name must match one of the three types of datasets recognized by Amazon Personalize. The following is an example of a minimal Interactions dataset schema. For more examples, see Datasets and schemas.
```
{
  "type": "record",
  "name": "Interactions",
  "namespace": "com.amazonaws.personalize.schema",
  "fields": [
      {
          "name": "USER_ID",
          "type": "string"
      },
      {
          "name": "ITEM_ID",
          "type": "string"
      },
      {
          "name": "TIMESTAMP",
          "type": "long"
      }
  ],
  "version": "1.0"
}
```

Create the schema using the CreateSchema API. Replace Schema Name with the name of your schema.


import boto3

personalize = boto3.client('personalize')

with open('schemaFile.json') as f:
    createSchemaResponse = personalize.create_schema(
        name = 'Schema Name',
        schema = f.read()
    )

schema_arn = createSchemaResponse['schemaArn']

print('Schema ARN:' + schema_arn )

Amazon Personalize returns the ARN of the new schema. Record it because you'll need it in the next step.

Create a dataset using the CreateDataset operation. Specify the datasetGroupArn returned in Creating a dataset group (AWS Python SDK). Use the schemaArn created in the previous step. Replace dataset type with the type of dataset you are uploading. For types of datasets, see Datasets and schemas.
```
import boto3

personalize = boto3.client('personalize')

response = personalize.create_dataset(
    name = 'datase_name',
    schemaArn = 'schema_arn',
    datasetGroupArn = 'dataset_group_arn',
    datasetType = 'dataset_type'
)

print ('Dataset Arn: ' + response['datasetArn'])
```
After you have created a dataset, you are ready to import your training data. See Step 3: Importing your data.

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Document Conventions

Step 1: Creating a dataset group

Step 3: Importing your data