Ensure you have access to both ClickHouse and Redshift. Install necessary command-line tools like `clickhouse-client` for ClickHouse and `psql` or `AWS CLI` for Redshift. Verify that you have sufficient permissions to export data from ClickHouse and import it into Redshift.

Use the `clickhouse-client` tool to export data from ClickHouse into a CSV or TSV file. Execute a query to select the data you need and use the `--format` option to specify the output format, such as CSV:
```bash
clickhouse-client --query="SELECT FROM your_table" --format=CSV > data.csv
```

Ensure that the exported CSV file is formatted correctly for Redshift. This may involve modifying the CSV to handle data types, escaping special characters, or ensuring date formats match Redshift's expected formats. Use tools like `sed` or `awk` for text processing if needed.

Use the AWS CLI to upload the CSV file to an S3 bucket. This step is crucial because Redshift can load data directly from S3:
```bash
aws s3 cp data.csv s3://your-bucket-name/path/to/data.csv
```

Before importing data, ensure that a corresponding table exists in Redshift with the appropriate schema. Use the `psql` command-line tool or the AWS Management Console to create the table:
```sql
CREATE TABLE your_table (
column1 datatype1,
column2 datatype2,
...
);
```

Use the `COPY` command in Redshift to load data from the S3 bucket into your table. This command references the S3 location where your CSV file is stored and requires appropriate IAM permissions:
```sql
COPY your_table
FROM 's3://your-bucket-name/path/to/data.csv'
IAM_ROLE 'arn:aws:iam::your-account-id:role/your-redshift-role'
CSV;
```

After loading the data, validate the migration by running queries in Redshift to ensure data integrity and completeness. Compare row counts and sample data between ClickHouse and Redshift to confirm that the transfer was successful.

By following these steps, you can manually transfer data from ClickHouse to Redshift without relying on third-party connectors or integrations.