Denise Martinez
Products and Technology

Do Data Science Better: Try Posit for Snowflake’s Powerful New Tools

2025-08-12

Posit’s goal is to make it seamless for data scientists to turn data into insights, wherever that data lives. It provides the premier environment for analyzing data and building applications with R and Python. Snowflake excels at securely storing data and making data and AI models available. Thus, bringing Posit’s best-in-class data science tools directly into the Snowflake ecosystem unlocks tremendous value. 

Today, we are happy to announce the latest capabilities from this powerful partnership.

 

Accelerate time-to-value with Positron 

 

Data scientists work differently. So does Positron, Posit’s code editor made for data science and analysis with Python and R. Rapidly develop data apps by previewing Shiny, Streamlit, Dash, and other data apps directly in your IDE, right next to your code. And do it all within Snowflake: easily access the next-generation Positron IDE directly within the Posit Workbench Native App for Snowflake!

 

The Posit Workbench Native App runs directly within your Snowflake account using Snowpark Container Services. This means that the tools data scientists love – RStudio, VS Code, Jupyter, and now Positron – run entirely within your Snowflake account, right next to your data, inheriting Snowflake’s security, roles, and governance. This powerful partnership allows teams to boost their productivity and reduce operational burden.

But that’s not all: Positron also includes native AI capabilities for agentic code completion and exploratory data analysis. Ask Positron Assistant to help with your code, all with the right data-specific context. Additionally, Cortex model support for Positron Assistant is not available yet, but coming soon.

Want to learn more about Positron? Register for the What’s New webinar.

 

Enabling easy, secure development in Snowflake

 

Posit has developed open-source packages specifically designed to enhance your Snowflake workflows. Packages like dbplyr allow you to write familiar dplyr code in R that gets translated into SQL for execution directly within Snowflake. More recently, orbital lets you translate tidymodels workflows into native SQL, allowing predictions to run blazing fast inside Snowflake. The Python version of orbital does the same thing for Scikit-Learn models.

A dual-pane screenshot displays two separate code editors. The left editor, in a dark theme, shows Python code with data manipulation operations, including variable assignment and data loading. Below the code is a data preview table. The right editor, in a light theme, displays a section titled "Step 7: Massive Scale Inference" with Python code primarily focused on data loading and processing using libraries like pandas. Both editors show line numbers and scrollbars, indicating longer code files.

Is it possible to have a seamless and secure AI assistant for R-based data science? One that knows that you are trying to analyze data (not develop software), and that tailors its suggestions accordingly? Yes, it is!

When used from within Snowflake (in the Posit Workbench Native App), Posit’s open-source AI tools like Gander integrate with Snowflake Cortex to bring secure, context-aware AI assistance directly into your IDE. Sensitive AI data remains securely within Snowflake and will never be shared with the wrong users. querychat provides a simple framework for building Snowflake-powered AI chat interfaces directly into R and Python dashboards.

 

Always connected to the right data

 

As discussed above, Posit Workbench integrates with Snowflake’s Snowpark Container Services to offer developers familiar environments like RStudio and VS Code – all with seamless access to Snowflake data. 

Additionally, Posit Connect enhances data sharing with Snowflake’s security and governance features to enable granular, viewer-level permissions. Regardless of where you host Posit Workbench or Posit Connect, you can securely access your Snowflake data from R or Python, using Posit’s professional drivers, ODBC Connections, or the Snowflake Python Connector. This makes it easy to query and use data stored in Snowflake from your favorite IDE or data visualization framework. Flexibly connect your existing, self-hosted Posit tools to data in Snowflake.

 

Customer Story: How a Leading Automobile Services Company Accelerated Model Development to Optimize Marketing Spend

 

A leading automobile services company faced many challenges with its existing data science environment, primarily centered around performance bottlenecks and platform limitations:

  • Slow, Manual Model Execution: Running propensity models in their legacy environment took an exorbitant amount of time (20 to 24 hours per model)
  • Processing Bottlenecks: ETL and data aggregation jobs for models competed for resources with other critical processes, leading to collisions and administrative overhead.
  • Limited Iteration: The lengthy model execution time restricted data scientists from running model updates frequently (annually or semi-annually), limiting their ability to quickly incorporate new data 

To solve these issues, they partnered with Posit and Snowflake to leverage the Posit Workbench Native App running on Snowpark Container Services:

  • Integrated Environment: By deploying their data science environment using the Posit Workbench Native App on Snowflake, the team seamlessly combined Posit’s powerful data science tooling with Snowflake’s scalable compute and storage capabilities.
  • In-Database Processing (Orbital): The team used the orbital package to convert model outputs (coefficients) into SQL code, pushing the scoring process down into Snowflake’s highly performant engine.

The results highlighted in the box on the right showcase the dramatic improvements this team has experienced.

 

Join us and learn more

 

We can’t wait to show you how these new features can power your workflows and help you win in the new age of data science.

Tags: Fall 2025 Product Announcement Posit Workbench positron snowflake