Start by accessing the Clockify API to extract the data you want to transfer. First, sign in to your Clockify account and navigate to the API documentation. Identify the endpoints you need and use them to fetch data, such as time entries or project details. Use HTTP requests (GET) to retrieve the data in JSON format. You can use command-line tools like `curl` or write a script in Python, using libraries like `requests`, to automate this extraction.

Once you have retrieved the data in JSON format, transform it into a format suitable for loading into Redshift. This might involve cleaning the data, normalizing it, or converting it into a tabular format like CSV. You can use Python pandas or similar tools to process the JSON data and write it to a CSV file. Ensure that the data types and structures align with the schema you plan to use in Redshift.

Before loading data, ensure that the target table in Redshift is ready. Connect to your Redshift cluster using a SQL client or command line tool and create the necessary table(s) with the appropriate schema to accommodate the Clockify data. Define column types that match the transformed data, and remember to specify primary keys or indexes if needed to optimize query performance.

After transforming and saving the data in CSV format, prepare it for upload to Amazon S3. This involves verifying the integrity of the data, ensuring there are no missing or malformed records. Larger data files may need to be split into smaller chunks to comply with AWS S3 upload limitations.

Upload the prepared CSV file(s) to an Amazon S3 bucket. You can use AWS CLI commands such as `aws s3 cp` to copy files from your local system to S3. Ensure that your AWS CLI is configured with the appropriate credentials and that you have write permissions to the S3 bucket. Verify the successful upload by checking the S3 bucket through the AWS Management Console.

With the data in S3, use the Redshift `COPY` command to load the data into your Redshift table. Connect to your Redshift cluster and execute the `COPY` command, specifying the S3 path, the format (CSV), and any necessary credentials. For example:
```sql
COPY my_table FROM 's3://mybucket/myfile.csv'
CREDENTIALS 'aws_access_key_id=YOUR_ACCESS_KEY;aws_secret_access_key=YOUR_SECRET_KEY'
CSV;
```
Monitor the process for any errors or issues during the load.

After loading the data into Redshift, perform checks to ensure data integrity and completeness. Run SQL queries to compare the record counts and sample data from Redshift with your original Clockify data. Validate key columns and formats to confirm the accuracy of the data migration. Make adjustments or corrections as necessary and document any discrepancies for future reference.

By following these steps, you can effectively move data from Clockify to a Redshift destination without relying on third-party connectors or integrations.