Start by ensuring your MySQL server is up and running. Check that you have the necessary user privileges to export data from the MySQL database. You will need access to the database tables you wish to transfer to Databricks.

Use the MySQL command-line client or a MySQL GUI tool to export the desired tables as CSV files. You can do this by executing a query such as:
```
SELECT * INTO OUTFILE '/path/to/export/file.csv'
FIELDS TERMINATED BY ',' ENCLOSED BY '"'
LINES TERMINATED BY '\n'
FROM your_table;
```
Make sure to replace `/path/to/export/file.csv` with the actual path where you want to save the CSV file, and `your_table` with the name of your table.

Log in to your Databricks account and create a new workspace or use an existing one. Ensure you have the necessary permissions to create clusters and import data.

Use the Databricks web interface to upload the CSV files to the Databricks File System. Navigate to the "Data" tab, select "Add Data," and then choose "Upload File." Follow the prompts to upload your CSV files.

Go to the "Clusters" section in Databricks and set up a new cluster if you don't have one running. Choose the appropriate cluster configuration based on your data size and processing needs. Start the cluster once it is configured.

Use a Databricks notebook to read the CSV data into a Spark DataFrame. In a new notebook cell, write the following Spark code to load the data:
```python
df = spark.read.format('csv').option('header', 'true').load('/mnt/path/to/your/uploaded/file.csv')
```
Replace `/mnt/path/to/your/uploaded/file.csv` with the actual path to your CSV file in DBFS.

Finally, save the DataFrame into Databricks Lakehouse. You can choose to save it in Delta format for optimized performance and features:
```python
df.write.format('delta').save('/mnt/lakehouse/your_table_path')
```
Replace `/mnt/lakehouse/your_table_path` with the desired path in your Lakehouse storage where you want to store the table.
By following these steps, you can effectively move your data from MySQL to Databricks Lakehouse without relying on any third-party connectors or integrations.