Building Applications on Snowflake: Streamlit and PostgreSQL

Traditionally the front end application lives in a different place and platform that the warehouse service. Snowflake provides and to end service where UI, transaction database like PostgreSQL and warehouse can be hosted in one place.

Streamlit in Snowflake

Streamlit is an open-source Python framework that turns a script into an interactive web app, with charts, filters, forms, and tables, in a fraction of the code a conventional web stack needs. Snowflake acquired Streamlit and hosts it natively: an app is created in the account, runs on Snowflake compute, and is shared with colleagues through the same roles that control table access. There is no separate server, no deployment pipeline, and no login system to maintain.

Typical uses are internal tools that would otherwise never get built: a data quality monitor, a pricing simulator, a what-if planner, an approval queue, or a front end for a Cortex-powered assistant. Because the app runs where the data is, it also works well as the delivery layer for machine learning and AI features.

Snowpark Container Services and Native Apps

When a Streamlit page is not enough, Snowpark Container Services runs full containerised applications, APIs, and background workers inside the Snowflake account, on CPU or GPU. The Native App Framework goes one step further and packages data and application logic together so it can be installed in another company's Snowflake account through the Marketplace. That makes it a distribution channel for vendors who want to ship analytics or AI to customers without receiving the customers' data.

PostgreSQL and transactional workloads

Analytical warehouses were never designed for the small, frequent reads and writes an application makes when a user clicks "save". Snowflake addresses this in two ways.

Snowflake Postgres is a managed PostgreSQL database inside the Snowflake platform, built on the Crunchy Data technology that Snowflake acquired. It is real PostgreSQL, so existing applications, ORMs, drivers, and extensions work unchanged, while the database is operated, secured, and billed within the Snowflake account. The data sits next to the analytical tables, which shortens the path from an operational record to a report or an AI feature.

Hybrid Tables take the other route: a Snowflake table type with row-level storage, primary key enforcement, and fast single-row lookups, queried with the same SQL as the rest of the warehouse. They suit application state, lookup tables, and serving layers that need low-latency reads and writes but do not require a separate database engine.

Why this matters for a business

• Fewer moving parts. Hosting, authentication, networking, and backups are part of the platform, so a small team can own an application end to end.
• One security model. The roles and policies that govern the data also govern who can open the app and what it can show them.
• Operational and analytical data in one place. With Snowflake Postgres and Hybrid Tables, an application's own records are immediately available for analytics and AI without an extraction pipeline.
• Fast iteration. A Streamlit prototype can be in front of users within days and hardened into a container-based service later without changing where it runs.