Begin by logging into your Looker account and navigating to the dashboard or report containing the data you want to export. Use Looker�s built-in export functionality to download the data in a CSV format. This can typically be done by selecting the export option and choosing CSV as the format. Save the file to your local system.

Set up your local environment to process the CSV file. Ensure you have Python installed on your system, as it will be used for data transformation. You might need additional packages such as `pandas` for data manipulation and `boto3` for interacting with AWS services. Install these packages using pip:
```bash
pip install pandas boto3
```

Use a Python script to load the CSV file and transform it into a format suitable for DynamoDB. Read the CSV using pandas and perform any necessary data cleaning or transformation. Ensure that the data structure matches the schema of your DynamoDB table. Here is a simple example:
```python
import pandas as pd
# Load CSV
df = pd.read_csv('your_data.csv')
# Example transformation
df['new_column'] = df['existing_column'].apply(your_transformation_function)
# Convert DataFrame to a list of dictionaries
data = df.to_dict(orient='records')
```

Configure your local environment to authenticate with AWS. This is necessary for accessing DynamoDB. You can do this by setting up your AWS credentials. Create or update the `~/.aws/credentials` file with your AWS Access Key ID and Secret Access Key:
```plaintext
[default]
aws_access_key_id = YOUR_ACCESS_KEY
aws_secret_access_key = YOUR_SECRET_KEY
```
Ensure that these credentials have permission to write to the desired DynamoDB table.

Create a Python script using the `boto3` library to insert the transformed data into your DynamoDB table. Here�s a template to get started:
```python
import boto3
# Initialize a session using Amazon DynamoDB
session = boto3.Session(
region_name='us-west-2' # Specify your region
)
dynamodb = session.resource('dynamodb')
# Specify the DynamoDB table
table = dynamodb.Table('YourDynamoDBTableName')
# Insert data into the table
for item in data:
table.put_item(Item=item)
```

If you are dealing with a large dataset, use batch writing to efficiently insert data into DynamoDB. Modify the script to batch write items using `batch_writer()`:
```python
with table.batch_writer() as batch:
for item in data:
batch.put_item(Item=item)
```
This approach reduces the number of write requests, making the process more efficient and cost-effective.

After the script completes execution, verify that the data has been successfully moved to DynamoDB. You can do this by logging into the AWS Management Console, navigating to DynamoDB, and checking the items in your table. Alternatively, use a Python script to scan the table and print out a few items to confirm the data presence and integrity.
By following these steps, you can effectively move data from Looker to DynamoDB without reliance on third-party connectors or integrations.