Here are some of the limitations of using Streamlit on Snowflake:
- Not suitable for large datasets: Streamlit is not designed to handle large datasets. If you are working with a large dataset, you may experience performance issues.
- Not suitable for complex applications: Streamlit is not designed for complex applications. If you are building a complex application, you may need to use a different platform.
- Not suitable for production environments: Streamlit is not yet fully production-ready. If you are deploying your app to production, you may need to take some additional steps to ensure its reliability and security.
- Unsupported features: Some Streamlit features are not supported when using Snowflake, such as:
- Custom components
- Integrated version control or CI/CD systems
- App edits are viewable by app viewers
- AWS PrivateLink is not supported
- The Seaborn and Matlibplot libraries are not supported
Overall, Streamlit is a powerful platform for building data apps, but it is important to be aware of its limitations. If you are working with large datasets or complex applications, you may need to use a different platform.
Here are some other limitations that are not specific to Streamlit but are still worth considering when using Snowflake:
- Snowflake’s pricing model: Snowflake’s pricing model can be complex and expensive, especially for large datasets.
- Snowflake’s data security: Snowflake is a secure platform, but it is important to take steps to protect your data, such as using strong passwords and encryption.
- Snowflake’s documentation: Snowflake’s documentation can be difficult to understand, especially for beginners.
If you are considering using Streamlit on Snowflake, it is important to weigh the benefits and limitations carefully. If you are not sure whether Streamlit is the right platform for you, I recommend contacting a Snowflake or Streamlit expert for advice.