Amazon Personalize
Developer Guide

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Importing Your Data

To import your training data into Amazon Personalize, first create an empty dataset group and then an empty dataset in that dataset group. Next, create an import job that populates the dataset with data from your Amazon S3 bucket. For more information about datasets, see Datasets and Schemas.

Import Your Data Using the AWS Python SDK

The following steps show how to create and populate a dataset. For the equivalent steps using the AWS CLI, see Step 1: Import Training Data.

  1. Create a dataset group using the CreateDatasetGroup API.

    import boto3 personalize = boto3.client('personalize') response = personalize.create_dataset_group(name = 'YourDatasetGroup') dsg_arn = response['datasetGroupArn'] description = personalize.describe_dataset_group(datasetGroupArn = dsg_arn)['datasetGroup'] print('Name: ' + description['name']) print('ARN: ' + description['datasetGroupArn']) print('Status: ' + description['status'])

    The response from the DescribeDatasetGroup API returns the datasetGroupArn and the status of the operation.


    You must wait until the status shows as ACTIVE before you can proceed to the next step.

  2. Create a dataset using the CreateDataset API. Specify the datasetGroupArn returned in the previous step. Use the schemaArn created earlier in Datasets and Schemas.

    import boto3 personalize = boto3.client('personalize') response = personalize.create_dataset( name = 'YourDataset', schemaArn = 'schema_arn', datasetGroupArn = 'dataset_group_arn', datasetType = 'Interactions') print ('Dataset Arn: ' + response['datasetArn'])
  3. To add data to your dataset, create and run a dataset import job using the CreateDatasetImportJob API. Specify the datasetGroupArn and set the dataLocation to the S3 bucket where you stored the training data.

    For the roleArn, see Creating an IAM Role. The roleArn parameter specifies the AWS Identity and Access Management (IAM) role that gives Amazon Personalize permissions to access your S3 bucket.

    import boto3 personalize = boto3.client('personalize') response = personalize.create_dataset_import_job( jobName = 'YourImportJob', datasetArn = 'dataset_arn', dataSource = {'dataLocation':'s3://bucket/file.csv'}, roleArn = 'role_arn') dsij_arn = response['datasetImportJobArn'] print ('Dataset Import Job arn: ' + dsij_arn) description = personalize.describe_dataset_import_job( datasetImportJobArn = dsij_arn)['datasetImportJob'] print('Name: ' + description['jobName']) print('ARN: ' + description['datasetImportJobArn']) print('Status: ' + description['status'])

    The response returns the datasetImportJobArn. The response from the DescribeDatasetImportJob API includes the status of the operation.


    You must wait until the status changes to ACTIVE before you can use the data to train a model.


Imports in Amazon Personalize are a full refresh of the data. You can't add incremental updates. If you need to update your data, import the complete updated file.

Amazon Personalize provides operations for managing datasets, dataset groups, and dataset import jobs. For example, you can use ListDatasets to list the datasets in a dataset group and use DeleteDataset to delete a dataset.

After you import your data into the relevant datasets in the dataset group, create a solution version by training a model. For more information, see Creating a Solution.