Begin by logging into your SurveyMonkey account. Navigate to the survey you wish to export data from. Go to the "Analyze Results" section and select the "Export" option. Choose the desired format for your export, such as CSV or Excel, which are easy to handle and compatible with Databricks. Download the export file to your local machine.

Ensure your local environment is set up with the necessary tools to handle data files. This typically includes software like Python or a simple spreadsheet editor for basic data checks. Verify the integrity and structure of the downloaded file, ensuring there are no missing headers or malformed data entries.

Access your Databricks account and create a new workspace if necessary. Set up a suitable cluster if one is not already running. Ensure your cluster has access to the necessary libraries to handle data ingestion and transformation, such as pandas and pyspark for Python.

Navigate to the "Data" tab in your Databricks workspace. Choose "Upload File" to add the exported SurveyMonkey data file to DBFS. Select the file from your local machine and upload it. This makes the data accessible for processing within Databricks.

Create a new notebook in your Databricks workspace. Use PySpark or Pandas to read the data into a DataFrame. For example, with PySpark, use:
```python
df = spark.read.csv('/dbfs/FileStore/tables/your_surveymonkey_data.csv', header=True, inferSchema=True)
```
This command reads the CSV file from DBFS into a Spark DataFrame, including headers and inferring data types.

Use Spark DataFrame operations to clean and transform the data as required. This might involve handling missing values, changing data types, or filtering unnecessary entries. For instance:
```python
df_cleaned = df.dropna() # Example operation to remove any rows with missing data
df_transformed = df_cleaned.withColumn('new_column', df_cleaned['existing_column'] * 2) # Example transformation
```

Once the data is cleaned and transformed, write it to your Databricks Lakehouse. Choose a suitable format such as Delta, Parquet, or another supported format for efficient storage and querying. For example, save as Delta Lake:
```python
df_transformed.write.format('delta').save('/mnt/lakehouse/your_transformed_data')
```
This command saves the DataFrame to the Lakehouse, making it ready for analysis and further processing.
By following these steps, you can manually transfer and prepare your SurveyMonkey data for analysis in Databricks Lakehouse without relying on third-party connectors or integrations.