Ensure your CSV file is clean and formatted correctly. Verify that it does not contain any corrupt data or special characters that could cause import issues. Place the CSV file in a location that Databricks can access, like a cloud storage bucket that your Databricks environment is configured to access (e.g., AWS S3, Azure Blob Storage, or Google Cloud Storage).

Log into your Databricks workspace. You should have the necessary permissions to create notebooks and access data storage options. Navigate to the workspace area where you can create new notebooks or scripts.

Before importing the CSV, ensure that your Databricks environment has the necessary permissions to read from your cloud storage. This usually involves setting up access credentials. For example, if using AWS S3, ensure that the AWS credentials are configured within Databricks to access the bucket containing your CSV file.

In your Databricks workspace, create a new notebook. This will allow you to write and execute code to read the CSV file and move the data into the Databricks Lakehouse. Choose your preferred language (Python, SQL, Scala, etc.) for the notebook.

Use Spark’s built-in CSV reader to load the data into a DataFrame. For example, in a Python notebook, you can use:
```python
df = spark.read.csv("path/to/your/csvfile.csv", header=True, inferSchema=True)
```
Replace `"path/to/your/csvfile.csv"` with the appropriate path to your CSV file in the cloud storage.

If necessary, perform any data transformations or cleaning operations using Spark DataFrame APIs. This can include operations like filtering rows, casting column types, or handling missing values to prepare the data for use in the Lakehouse.

Finally, write the DataFrame to the Databricks Lakehouse. You can save it as a Delta table, which is optimized for Databricks. Use the following command:
```python
df.write.format("delta").saveAsTable("your_table_name")
```
Replace `"your_table_name"` with the desired name for your table in the Lakehouse. This command writes the data in an optimized format for fast queries and updates.

By following these steps, you can efficiently move data from a CSV file to a Databricks Lakehouse without relying on third-party connectors or integrations.