How to load data from Azure Table Storage to Databricks Lakehouse

Begin by accessing your Azure Table Storage account. Use the Azure Portal to navigate to your storage account and open the Table Storage section. Identify the specific table you wish to migrate to Databricks Lakehouse.

Use Azure Storage Explorer or Azure Portal to export the data from your Azure Table Storage. You can export the table data to a CSV or JSON file format, which is a common and simple format for data transfer. Ensure you export the data to a secure location accessible to Databricks.

Once exported, upload the CSV or JSON file to Azure Blob Storage. This can be accomplished via Azure Portal, Azure CLI, or Azure Storage Explorer. Azure Blob Storage acts as an intermediary storage solution that Databricks can easily access.

In your Databricks workspace, create a new notebook and use it to mount the Azure Blob Storage container. Utilize the Databricks File System (DBFS) mount feature, and provide the necessary credentials and configurations. Use the following code snippet as a reference:
```python
dbutils.fs.mount(
source = "wasbs://@.blob.core.windows.net",
mount_point = "/mnt/",
extra_configs = {"":dbutils.secrets.get(scope = "", key = "")}
)
```

With the Azure Blob Storage mounted, read the data file into a DataFrame in Databricks using Spark. Depending on the file format, use the appropriate Spark method. For CSV files:
```python
df = spark.read.csv("/mnt//.csv", header=True, inferSchema=True)
```
For JSON files:
```python
df = spark.read.json("/mnt//.json")
```

If necessary, perform any data transformations or cleaning operations within Databricks using Spark SQL or DataFrame API. This step is crucial to shape the data according to the schema requirements of your Databricks Lakehouse.

Finally, write the DataFrame to the desired Databricks Lakehouse location. Depending on how you manage your Lakehouse, you may write to a Delta table or Parquet files. For Delta tables:
```python
df.write.format("delta").mode("overwrite").save("/mnt//")
```
For Parquet files:
```python
df.write.parquet("/mnt//", mode="overwrite")
```

By following these steps, you can effectively move data from Azure Table Storage to Databricks Lakehouse using Azure's native tools and Databricks capabilities, without relying on third-party connectors or integrations.