Begin by accessing the Zendesk Chat API to retrieve the data you need. You will need to generate an API token from your Zendesk admin panel. Navigate to the API section under settings, and create a new API token. Use this token to authenticate your requests when accessing the API endpoints, which will allow you to extract chat-related data such as chat transcripts, visitor data, and agent information.

Write a Python script to extract data from Zendesk Chat using the API. Use Python libraries such as `requests` to execute API calls. For example, to get a list of chats, you'll need to send a GET request to the appropriate endpoint. Handle the authentication by including the API token in the headers of your requests. Parse the JSON response to extract the necessary data fields.

Once you have extracted the data, transform it into a CSV format suitable for processing in AWS. Use Python’s `csv` module to write the data into CSV files. This involves structuring the data into rows and columns, where each row represents a chat session and columns contain attributes like chat ID, timestamp, agent name, visitor name, and chat content.

Before uploading data to AWS, ensure you have the necessary permissions by setting up an IAM role or user with appropriate access rights. This includes permissions to write data to an S3 bucket and to use AWS Glue services. You can define a custom policy that grants these permissions and attach it to your IAM role or user.

Upload the CSV files to an S3 bucket using the AWS SDK for Python, `boto3`. Authenticate using your AWS credentials and specify the S3 bucket name and the file path for the upload. Use the `put_object` method to transfer your CSV files to the specified S3 location. Ensure that your S3 bucket is in the same region as your intended AWS Glue job to optimize performance.

Set up an AWS Glue Crawler to automatically detect the schema of the data stored in your S3 bucket. In the AWS Glue Console, create a new crawler and configure it to point to the location of the CSV files in your S3 bucket. Define an appropriate IAM role for the crawler to access your S3 data. Run the crawler to populate the Glue Data Catalog with the metadata of your CSV files.

Create and execute an AWS Glue ETL job to process and transform the data as needed. In the AWS Glue Console, define a new job, specifying the script or using the Glue Studio for visual ETL design. Configure the job to read from the data catalog created by the crawler and output the processed data back to S3 or another desired location. Monitor the job execution and verify the output to ensure that the data has been processed correctly.