Begin by exporting the data from Drift. Navigate to the Drift dashboard and access the export functionality. Choose the data you want to export (e.g., conversation history, contact lists) and select the format for export, such as CSV or JSON. Download the exported files to your local machine.

Set up your Databricks environment if it's not already configured. This includes creating a new Databricks workspace, setting up a cluster, and ensuring you have the necessary permissions to create databases and tables within the Lakehouse environment.

Use the Databricks UI or CLI to upload the exported data files to the Databricks File System (DBFS). In the Databricks UI, navigate to the "Data" tab, select "DBFS," and use the "Upload" button to add your files. Alternatively, use the Databricks CLI with the command `databricks fs cp local-file-path dbfs:/path/in/dbfs` to upload files programmatically.

Once the data files are in DBFS, use SQL or PySpark in a Databricks notebook to create tables in the Lakehouse. Start by defining the schema based on the data structure in your files, then use the `CREATE TABLE` statement to set up a table. For instance:
```sql
CREATE TABLE drift_data (
id STRING,
message STRING,
timestamp TIMESTAMP
) USING delta LOCATION 'dbfs:/path/in/dbfs';
```

Load the uploaded data files into the newly created tables. If using PySpark, read the files into a DataFrame and then write the DataFrame to the table using the `write` method. Example:
```python
df = spark.read.format('csv').option('header', 'true').load('dbfs:/path/in/dbfs/your-file.csv')
df.write.format('delta').mode('append').saveAsTable('drift_data')
```

After loading the data, verify the data integrity by running queries on the tables to ensure all records are correctly imported. Use SQL queries to count the number of rows, check for null values, and perform basic data checks to confirm consistency with the original data.

Optimize the tables for performance using Databricks Lakehouse features like Delta Lake's `OPTIMIZE` command. Additionally, set up data retention policies and perform regular maintenance tasks such as vacuuming to manage storage efficiently:
```sql
OPTIMIZE drift_data;
VACUUM drift_data RETAIN 168 HOURS;
```

Following these steps will allow you to move data from Drift to the Databricks Lakehouse seamlessly without relying on third-party connectors or integrations.