1. Log in to your Databricks workspace.

2. Create a new cluster or start an existing one that you wish to use for the data transfer process.

3. Once the cluster is running, create a new notebook in the workspace.

In your Databricks notebook, you may need to install additional libraries to work with SFTP protocols, such as `paramiko` for Python. Use the following command to install the required library:

```python

%pip install paramiko

```

In the notebook, write a script to establish a connection to your SFTP server. Here's an example in Python using the `paramiko` library:

```python

import paramiko

sftp_hostname = 'your_sftp_server.com'

sftp_port = 22  # or the port your SFTP server uses

sftp_username = 'your_username'

sftp_password = 'your_password'  # or use key-based authentication

# Initialize the SSH client

ssh_client = paramiko.SSHClient()

ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())

# Connect to the SFTP server

ssh_client.connect(sftp_hostname, port=sftp_port, username=sftp_username, password=sftp_password)

# Create an SFTP session

sftp_client = ssh_client.open_sftp()

```

Identify the files you want to transfer and download them to the Databricks file system (DBFS).

```python

remote_file_path = '/path/to/remote/file.csv'

local_file_path = '/dbfs/tmp/my_data.csv'  # Temporary storage in DBFS

# Download the file from SFTP to local DBFS

sftp_client.get(remote_file_path, local_file_path)

# Close the SFTP client

sftp_client.close()

ssh_client.close()

```

Load the downloaded data into a DataFrame for further processing or direct storage into the Databricks Lakehouse.

```python

# Using PySpark to read the data into a DataFrame

df = spark.read.csv(local_file_path, header=True, inferSchema=True)

# Perform any necessary data transformations here

```

Now, write the DataFrame to the Databricks Lakehouse, which is backed by a Delta Lake on top of your data storage.

```python

# Define the path to the Delta Lake

delta_lake_path = '/mnt/delta_lakehouse/my_data'

# Write the DataFrame to the Delta Lake

df.write.format("delta").mode("overwrite").save(delta_lake_path)

```

If you need to transfer data regularly, you can schedule the notebook as a job in Databricks.

1. Go to the 'Jobs' tab in your Databricks workspace.

2. Create a new job, and select the notebook you've created as the task.

3. Configure the schedule to run as often as needed.

Step 8: Clean Up (Optional)

After the data transfer is complete, you may want to delete the temporary files from DBFS to free up space.

```python

dbutils.fs.rm(local_file_path, recurse=True)

```