Begin by logging into your Clockify account. Navigate to the 'Reports' section where you can generate reports of the data you need (e.g., time entries, project summaries). Use the export functionality provided by Clockify to download the data in a common format like CSV or Excel. Save these files locally on your computer.

Open the exported file to review its contents. Ensure that the data is complete and correct. If necessary, clean the data by removing any unwanted columns or rows. Also, format the data to match the schema you plan to use in ClickHouse. This might involve renaming columns or changing data types.

Ensure that you have ClickHouse installed and running on your server. You can download it from the official ClickHouse website and follow installation instructions for your operating system. Once installed, use the ClickHouse client or a GUI tool to create a new database (e.g., `create database clockify_data;`) and the necessary tables to store your data.

Based on the data format from Clockify, create a table in ClickHouse with an appropriate schema. Use the `CREATE TABLE` command to define columns that match the data types and structure of your Clockify data. For example:
```sql
CREATE TABLE clockify_entries (
id String,
project String,
task String,
start_time DateTime,
end_time DateTime,
duration UInt32
) ENGINE = MergeTree() ORDER BY id;
```

If your data needs transformation (e.g., converting timestamps to a different format or calculating additional fields), perform these operations using a scripting language like Python or a spreadsheet tool. Ensure that the transformed data is saved in a format compatible with ClickHouse, such as CSV.

Use the `clickhouse-client` command to import the CSV data into ClickHouse. The command will look something like this:
```sh
clickhouse-client --query="INSERT INTO clockify_entries FORMAT CSV" < path_to_your_data.csv
```
Ensure that the CSV file follows the same order of columns as your ClickHouse table schema.

After importing the data, verify its integrity by running queries on the ClickHouse database. Check for discrepancies, such as missing entries or incorrect data types. Use queries like:
```sql
SELECT FROM clockify_entries LIMIT 10;
```
This step ensures that the data transfer was successful and that the data in ClickHouse matches the original data from Clockify. Adjust and re-import any data as necessary.

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