1. Determine the Data to Export: Identify the tables and columns you want to transfer from PostgreSQL to ClickHouse.

2. Choose an Export Format: Decide on a file format that is compatible with both PostgreSQL and ClickHouse for the export. CSV is a common choice due to its simplicity and wide support.

3. Export the Data: Use the `COPY` command in PostgreSQL to export the data to a CSV file. For example:

   ```sql

   COPY (SELECT * FROM your_table) TO '/path/to/your_file.csv' WITH CSV HEADER;

   ```

   Replace `your_table` with the name of your table and `/path/to/your_file.csv` with the desired file path.

1. Review Data Types: Ensure that the data types in the CSV file are compatible with ClickHouse's data types. You may need to convert data types that don't have a direct equivalent in ClickHouse.

2. Modify the CSV if Necessary: If there are any discrepancies in the data, such as date formats or string encodings, adjust the CSV file accordingly. You can use scripting languages like Python or tools like `sed` and `awk` for this purpose.

1. Design the Schema: Define the schema of the table in ClickHouse, ensuring that it matches the structure and data types of the data you exported from PostgreSQL.

2. Create the Table: Use the ClickHouse client or UI to execute the `CREATE TABLE` statement. For example:

   ```sql

   CREATE TABLE clickhouse_db.your_table (

       column1 DataType1,

       column2 DataType2,

       ...

   ) ENGINE = MergeTree()

   ORDER BY (column1);

   ```

   Replace `clickhouse_db.your_table` with the desired database and table name, and define the columns and data types according to your data.

1. Transfer the CSV File: Move the CSV file to a location that is accessible by the ClickHouse server. This could be done via `scp`, `rsync`, or by placing the file on a shared network drive.

2. Import the Data: Use the ClickHouse client to import the data from the CSV file into the table you created. You can use the `clickhouse-client` command-line tool with the `--query` parameter:

   ```sh

   clickhouse-client --query="INSERT INTO clickhouse_db.your_table FORMAT CSV" < /path/to/your_file.csv

   ```

   This command reads the CSV file and inserts the data into the ClickHouse table.

1. Check the Row Count: Compare the row count in the PostgreSQL table with the row count in the ClickHouse table to ensure all rows have been transferred.

   ```sql

   -- PostgreSQL

   SELECT COUNT(*) FROM your_table;

   -- ClickHouse

   SELECT COUNT(*) FROM clickhouse_db.your_table;

   ```

2. Sample Data Check: Run a few sample queries on both databases to compare the results and verify the data integrity.

1. Data Discrepancies: If there are discrepancies, check the export and import logs for errors and warnings. You may need to adjust the CSV file or the table schema in ClickHouse.

2. Performance Tuning: If the import process is slow, consider tuning ClickHouse settings or breaking the CSV into smaller chunks to import in parallel.

Additional Notes:

- Ensure that the PostgreSQL server allows exporting data to a file, and the necessary permissions are in place.

- For large datasets, it's recommended to export and import data in chunks to avoid memory issues and to allow for parallel processing.

- Always back up your databases before performing such operations to prevent data loss.

- Make sure that the ClickHouse server has enough disk space to accommodate the imported data.

By following these steps, you should be able to move data from PostgreSQL to ClickHouse without using third-party connectors or integrations. Remember to test the process with a small subset of data before attempting to transfer large volumes of data.