Begin by accessing the Oura API. You'll need to authenticate using an Oura API Personal Access Token, which you can generate from your Oura account dashboard. This token will allow you to make authorized requests to the API.

Using the Oura API, make HTTP GET requests to fetch the data you need. You can use Python's `requests` library to make these calls, specifying the endpoints for the particular data you're interested in, such as sleep, readiness, or activity data.

Once the data is fetched, parse the JSON response to extract the relevant fields. This involves transforming the raw JSON data into a structured format compatible with DynamoDB, usually as dictionaries or lists containing the necessary attributes and values.

Install and configure the AWS SDK for Python, known as Boto3, to interact with DynamoDB. Set up your AWS credentials using the AWS CLI or environment variables to ensure that your script can authenticate with AWS services.

Ensure you have a DynamoDB table ready to receive data. If you don't have one, use Boto3 to create a new table, specifying the primary key schema and any secondary indexes required. Adjust the table's read and write capacity settings according to your use case.

Transform the structured data into a format suitable for DynamoDB. Use Boto3's `put_item` or `batch_write_item` methods to insert the data into your DynamoDB table. Make sure to handle any potential exceptions, such as capacity exceeded errors, to ensure robust data insertion.

Automate the data fetching and insertion process by scheduling your script to run at regular intervals. You can achieve this using cron jobs on Unix-based systems or Task Scheduler on Windows. This ensures that your DynamoDB table is consistently updated with the latest data from Oura.

By following these steps, you can effectively move data from Oura to DynamoDB without relying on third-party connectors or integrations.