To read an Excel file using Databricks, you can use the Databricks Runtime's built-in support for reading various file formats, including Excel. Here are the steps to do it:

1. **Upload the Excel File**: First, upload your Excel file to a location that Databricks can access, such as DBFS (Databricks File System) or an external storage system like Azure Blob Storage or AWS S3.

2. **Create a Cluster**: If you don't already have a Databricks cluster, create one.

3. **Create a Notebook**: Create a Databricks notebook where you will write your code.

4. **Load the Excel File**: Use the appropriate library and function to load the Excel file. Databricks supports multiple libraries for this purpose, but one common choice is using the `pandas` library in Python. Here's an example using `pandas`:

```python

# Import the necessary libraries

import pandas as pd

# Specify the path to your Excel file

excel_file_path = "/dbfs/path/to/your/excel/file.xlsx" # Replace with your file path

# Use pandas to read the Excel file

df = pd.read_excel(excel_file_path)

# Show the first few rows of the DataFrame to verify the data

df.head()

```

5. **Execute the Code**: Run the code in your Databricks notebook. It will read the Excel file and load it into a DataFrame (in this case, using `pandas`).

6. **Manipulate and Analyze Data**: You can now use the `df` DataFrame to perform data manipulations, analysis, or any other operations you need within your Databricks notebook.

7. **Save Results**: If you need to save any results or processed data, you can do so using Databricks' capabilities, whether it's saving to a new Excel file, a database, or another storage location.

Make sure to configure your Databricks environment and notebook with the necessary dependencies if you're using libraries other than `pandas` for reading Excel files. Also, adjust the file path to match the location of your Excel file within your Databricks environment.