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How to Build a Internal Data App with Google Sheets Backend Using Streamlit
- This article discusses how to build an internal data app using Streamlit with Google Sheets as the backend.
- The article provides a step-by-step guide for configuring Google Sheets API and setting up Streamlit for the app.
- Key steps include enabling the Google Sheets API, creating a service account, granting access to the spreadsheet, and installing Streamlit.
- The article showcases code snippets for authentication, retrieving data from Google Sheets, and displaying it in a Streamlit app.
- Various methods for adding authentication for internal use in Streamlit apps are also discussed in the article.
- Options include adding authentication individually, using Snowflake in Streamlit, or leveraging Morph platform for centralized app management.
- Streamlit is highlighted as a useful tool for building data applications powered by machine learning or AI, especially for internal tools like workflow automation, demand forecasting, and internal chatbots.
- The possibilities of applying Streamlit are vast, making it a valuable tool for data scientists and analysts.
- Streamlit is recommended for those comfortable with Python and interested in developing data applications efficiently.
- Overall, the article emphasizes the versatility and potential of Streamlit for a wide range of internal data app development scenarios.
- To explore the full potential of Streamlit, the article encourages readers to give it a try and experiment with different use cases.
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