Start by exporting the data from Aha! to a CSV or Excel file. Log into your Aha! account, navigate to the data section you need (such as Features, Ideas, or Releases), and use the export functionality to download the data. Ensure you have the necessary permissions to perform the export.

Once the data is exported, review and clean it to ensure data quality. Remove any unnecessary columns, correct formatting issues, and ensure that data types are consistent. This step is crucial to avoid errors during the import process into Databricks.

Log into your Databricks account and navigate to your workspace. Ensure you have the necessary permissions to create tables and upload data. Familiarize yourself with the Databricks environment if you are not already acquainted with it.

Use the Databricks interface to upload the cleaned data file to the Databricks File System (DBFS). You can do this by navigating to the "Data" tab, selecting "Add Data," and then uploading your CSV or Excel file to the DBFS.

In a new Databricks notebook, create a table to house the imported data. Use SQL or PySpark to define the table schema. Ensure that the data types and column names match those in your file. For example, if using SQL:
```sql
CREATE TABLE aha_data (
column1 STRING,
column2 INT,
...
);
```

Load the data from DBFS into the table you just created. You can use Spark to read the data and write it into the table. For example, with PySpark:
```python
df = spark.read.format("csv").option("header", "true").load("/dbfs/path/to/your/file.csv")
df.write.format("delta").mode("overwrite").saveAsTable("aha_data")
```

Finally, verify that the data has been correctly imported. Run queries to check row counts, data types, and sample data to ensure no discrepancies exist. Use SQL queries within Databricks to validate the data:
```sql
SELECT * FROM aha_data LIMIT 10;
```
This step ensures that the data transfer was successful and that the data in Databricks Lakehouse is accurate and usable.

By following these steps, you can manually transfer data from Aha! to Databricks Lakehouse without relying on third-party connectors or integrations.