Begin by exporting the data you wish to transfer from LaunchDarkly. You can achieve this by using LaunchDarkly�s API to retrieve the necessary data. Use the REST API endpoint to fetch feature flag data, user data, or event data. Make sure to authenticate your API requests using your API key. Store the exported data in a JSON or CSV format, as these formats are easier to handle for subsequent steps.

Store the exported data locally on your machine or server. Ensure that you organize the data files in a structured manner, such as by using directories that reflect the data categories (e.g., feature flags, users, events). This organization will help you manage and process the data efficiently in the following steps.

Access your Databricks environment and set up a new cluster if necessary. Ensure that your cluster is configured with the appropriate compute resources and permissions to access the Databricks File System (DBFS). You can do this by logging into your Databricks workspace, navigating to the "Clusters" section, and creating a new cluster with the desired specifications.

Use the Databricks web interface or CLI to upload the locally stored data files to DBFS. You can use the Databricks CLI command `dbfs cp` to copy files from your local system to DBFS. For example:
```
dbfs cp local_path dbfs:/path_in_dbfs --recursive
```
Ensure that you upload all necessary files and maintain the directory structure.

Create a new notebook in Databricks to transform and clean the data. Use Apache Spark's DataFrame API to read the JSON or CSV files from DBFS. Perform any necessary data transformations, cleaning, or enrichment within the notebook. For instance, you might need to handle missing values, convert data types, or filter irrelevant records.

Once the data is transformed and cleaned, write it to Delta Lake tables within your Databricks Lakehouse. Delta Lake provides ACID transactions and scalability, making it suitable for large datasets. Use the following Spark DataFrame operation to write data:
```python
df.write.format("delta").mode("overwrite").save("/path_in_dbfs/delta_table")
```
Replace `df` with your DataFrame variable and specify the appropriate path.

After writing the data, validate the tables to ensure data integrity and completeness. Run queries to check for expected record counts and data accuracy. Additionally, optimize the Delta Lake tables by running the `OPTIMIZE` command to improve query performance:
```sql
OPTIMIZE delta.`/path_in_dbfs/delta_table`
```
This step will compact small files and improve data retrieval efficiency.
By following these steps, you can effectively move data from LaunchDarkly to Databricks Lakehouse without relying on third-party connectors or integrations.