How To Export Data From Databricks To Excel

To export data from Databricks to Excel, you can follow several methods, each with its own set of steps and tools. Here are the most common approaches:

Method 1: Export as CSV and Convert to Excel

1. Save DataFrame as CSV: Within Databricks, save the DataFrame as a CSV file.
   python

   # Assuming 'df' is your DataFrame
df.write.csv('/path/to/save/file.csv', header=True)
2. Convert CSV to Excel: Use external tools or libraries like Pandas in Python to convert the CSV file to Excel format.
   python

   import pandas as pd

   # Load CSV into a Pandas DataFrame
   df = pd.read_csv(‘/path/to/save/file.csv’)

   # Save as Excel file
   df.to_excel(‘/path/to/save/file.xlsx’, index=False)

   This method requires you to first export the data as a CSV file and then convert it to Excel using a script or a tool outside of Databricks.

Method 2: Direct Export Using Libraries

1. Use Python Libraries: You can use Python libraries like pandas and openpyxl to directly export a DataFrame to an Excel file within Databricks.
   python

   import pandas as pd

   # Assuming ‘df’ is your DataFrame
   df.to_excel(‘/path/to/save/file.xlsx’, index=False)

   This approach is straightforward if you are familiar with Python and Pandas.

Method 3: Use Databricks SQL

1. Export SQL Results: If you are using Databricks SQL, you can download the query results directly as an Excel file.
   • Ensure that the “LIMIT 1000” option is unchecked to download the full dataset.
   • Click the download button to retrieve your data in Excel format.

Method 4: Connect Excel Directly to Databricks

1. ODBC Connection: Use the Databricks ODBC driver to connect Excel directly to Databricks.
   • Install the ODBC driver and configure a Data Source Name (DSN).
   • In Excel, go to Data > Get Data > From Other Sources > From ODBC and select your DSN.
   • Authenticate using OAuth 2.0 or a personal access token to import data directly into Excel.

 

Each method has its advantages depending on your setup and requirements. For example, using ODBC allows for real-time data access, while exporting as CSV and converting to Excel might be simpler for smaller datasets or one-time exports.

 

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