Begin by exporting the data from AppFollow. This can usually be done by utilizing AppFollow"s export features or APIs. If using an API, write a script (in Python, for example) to call the relevant endpoints, and collect the data in a CSV or JSON format. Ensure you have the necessary API access and authentication set up before proceeding.

Once the data is extracted, it's crucial to clean and format it to match the schema of your Redshift database. This involves handling any missing values, correcting data types, and structuring the data into tables that align with your Redshift schema. Use data manipulation tools like pandas in Python for this purpose.

Create an Amazon S3 bucket to temporarily store your prepared data. This is necessary because Amazon Redshift does not support direct imports from local storage. Navigate to the S3 service in your AWS Management Console, and create a new bucket with a unique name. Keep track of your access credentials and permissions.

With your data prepared and an S3 bucket ready, upload the data files (CSV or JSON) to the bucket. Use the AWS Command Line Interface (CLI) or the AWS Management Console to perform the upload. Ensure that the files are organized and stored in a way that mirrors the intended table structure in Redshift.

If you haven't already, set up an Amazon Redshift cluster. Ensure the cluster has the necessary permissions to access your S3 bucket by modifying the IAM roles and policies associated with Redshift. This involves creating an IAM role that grants Redshift permission to access S3, and associating it with your Redshift cluster.

Use the COPY command in Amazon Redshift to load the data from S3 into your Redshift tables. Connect to your Redshift instance using a SQL client tool like SQL Workbench/J, and execute the COPY command specifying the S3 path, file format, and IAM role. For example:
```
COPY my_table
FROM 's3://my-bucket/my-data-file.csv'
IAM_ROLE 'arn:aws:iam::123456789012:role/MyRedshiftRole'
CSV;
```
Adjust the command based on your specific data format and structure.

After loading the data, perform a series of checks to ensure the data has been transferred accurately. Run queries to validate row counts and sample data against your original dataset from AppFollow. Check for any discrepancies or errors, and troubleshoot as needed to resolve any issues.

By following these steps, you can effectively and securely move data from AppFollow to Amazon Redshift without relying on third-party connectors or integrations.