Before you begin, ensure you have the necessary access to both your Amazon S3 bucket and your ClickHouse database. You'll need AWS credentials with permissions to read from S3 and appropriate access rights to your ClickHouse database.

Use the AWS CLI or a programmatic approach with Python's `boto3` to download the data from your S3 bucket to a local or temporary storage location on your ClickHouse server. For example, using the AWS CLI:
```bash
aws s3 cp s3://your-bucket-name/path/to/data.csv /local/path/data.csv
```

Ensure your data is in a format compatible with ClickHouse, such as CSV, TSV, or JSON. You may need to clean or transform your data to ensure it matches the structure of the target ClickHouse table. This could involve using tools like `awk`, `sed`, or Python scripts for processing.

Define and create a table in ClickHouse that matches the structure of your data. Use the `CREATE TABLE` statement to specify the schema, including column names and data types. For example:
```sql
CREATE TABLE my_table (
column1 String,
column2 Int32,
column3 Date
) ENGINE = MergeTree() ORDER BY column1;
```

Use the ClickHouse client or the HTTP interface to load your data file into the newly created table. For a CSV file, use the `INSERT INTO` statement with the `FORMAT` option:
```bash
clickhouse-client --query="INSERT INTO my_table FORMAT CSV" < /local/path/data.csv
```

Once the data is imported, verify the import by running some basic queries to check the data integrity and count. For example:
```sql
SELECT COUNT(*) FROM my_table;
SELECT * FROM my_table LIMIT 10;
```
This helps ensure that the data is loaded as expected and that there are no discrepancies.

After verifying the data import, remove any temporary files or data downloads from your local or temporary storage. This is a good practice to save space and avoid clutter:
```bash
rm /local/path/data.csv
```
By following these steps, you can effectively transfer data from S3 to ClickHouse without relying on third-party connectors or integrations.