Begin by accessing Trustpilot's API to extract the necessary data. You'll need to create an API key by setting up a developer account on Trustpilot. Once you have the API key, use the Trustpilot API documentation to understand the endpoints required to fetch the data you need, such as reviews, business information, or user details. Use tools like `curl` or build a small script in a language like Python to send HTTP requests to these endpoints and retrieve the data in JSON format.

The data obtained from Trustpilot will be in JSON format. You'll need to parse this JSON data to extract relevant fields and possibly transform it to match the schema of your ClickHouse tables. This can be done using scripting languages like Python with libraries such as `json` for parsing and `pandas` for data manipulation. Ensure that the data types are compatible with ClickHouse, converting them as necessary (e.g., strings to dates).

Set up your ClickHouse database and prepare the tables that will store the Trustpilot data. Define your table schema based on the transformed data structure. You can use the ClickHouse client or ClickHouse's web interface to execute SQL commands to create the necessary tables. Make sure to use appropriate data types and indexing strategies to optimize performance.

ClickHouse can efficiently import data in formats like CSV or TSV. Convert the transformed JSON data into one of these formats. You can write a script to iterate through the parsed JSON data and output it as CSV/TSV files. Ensure proper handling of special characters, delimiters, and newline characters to avoid data corruption.

Use the ClickHouse client or command-line interface to load the CSV/TSV files into your ClickHouse tables. The `clickhouse-client` command-line tool can be used with the `--query` option to execute an `INSERT INTO` statement that reads from the file. For example:
```
clickhouse-client --query="INSERT INTO your_table FORMAT CSV" < your_data.csv
```
Ensure that the file paths and table names are correct, and verify data alignment with the table schema.

After loading the data, perform checks to verify that the data has been imported correctly. Use SQL queries to count the number of rows and compare it with the original dataset size. Check for any discrepancies or missing data. It's also essential to validate the data types and ensure that no truncation or formatting errors occurred during the import process.

To make the data transfer process seamless and regular, automate the ETL (Extract, Transform, Load) process using a cron job or a similar task scheduler. Write a script that encompasses all the previous steps, from data extraction to loading, and set it to run at desired intervals, such as daily or weekly, depending on your data update requirements. This automation ensures that your ClickHouse warehouse remains up-to-date with the latest Trustpilot data without manual intervention.