Unlocking the Power of Snowflake Hosting with Streamlit
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Chapter 1: Introduction to Snowflake Hosting
In recent weeks, I've been quite occupied with numerous events, which has slowed down my article releases. I had initially aimed to publish this piece in the second week of October, but here it is now. Our CTO has adopted a new writing style for the blog, and I am experimenting with it as well. This article is somewhat less technical than my usual contributions, as I've observed a growing interest in Streamlit hosting. If you're seeking a demonstration-focused article, you'll find it linked at the end.
The focus here will be on the exciting technical and business opportunities presented by hosting Streamlit applications directly within Snowflake. If you’re tuned in, you might have heard that Snowflake is soon introducing hosted Kubernetes to their platform, currently in Private Preview.
Snowflake is steadily evolving into a comprehensive data platform, a definition that seems to change year by year. Their goal is to keep everything close to the data, enhancing processing speeds, eliminating data copies, and leveraging their massive, scalable compute resources.
Section 1.1: Understanding Streamlit
In 2022, Snowflake Data Cloud acquired Streamlit, which enables the development of simple data applications using pure Python, primarily for visualization, though it can do much more. Streamlit's platform allows for the creation of interactive and dynamic data visualizations with ease, making it possible to develop applications in just a few minutes. Previously, hosting this code required an external solution, but that is changing.
Snowflake has been quick to capitalize on this acquisition, rapidly rolling out products and use cases. Remarkably, Streamlit remains open-source. For instance, consider the labs that Snowflake offers, such as a quick lab that builds a Streamlit web application for analyzing bank deposits in the U.S. This flexible model enables deployment of a Streamlit app using SQL or the UI tool Snowsight.
Section 1.2: Use Cases and Benefits
Low Latency Visualization:
Users can visualize their data directly within Snowflake, ensuring real-time insights and minimizing latency. For example, store managers could access near real-time sales performance data, allowing them to adjust displays or tailor specific sales offerings based on seasonal trends.
Data Science Dashboarding:
With Snowpark, users can train models within Snowflake, significantly enhancing the potential for providing business users with direct access to model insights. Imagine a scenario where several pre-developed models can be integrated into a rapidly created dashboard, enabling business users to experiment with various machine learning models to extract insights from current data.
Cost Efficiency:
One of the most significant advantages is the potential for cost control. By hosting web applications within Snowflake, there is minimal data movement outside the platform, reducing expenses and eliminating the need for new infrastructure or containers, which can be costly.
Chapter 2: Areas for Improvement
While there are many benefits, there are also areas that need enhancement. Notably, there is currently no CI/CD or Git integration, though we anticipate its arrival soon. Moreover, only Amazon Web Services is supported at this time. Additionally, when developers make edits to an application, those changes are immediately visible to end users, indicating a lack of distinct development and production workflows.
Conclusion
The ability to host Streamlit applications directly within Snowflake opens up a world of possibilities for real-time data visualization and interactive dashboarding. Business users can now access low-latency insights, data scientists can streamline their workflows, and IT departments benefit from reduced data movement and infrastructure demands.
This advancement positions Snowflake for a future filled with innovation while enhancing efficiency and facilitating data-driven decision-making for clients.
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