Snowflake Data Cloud and Domino

Snowflake Data Cloud and Domino

Evolve from data-driven decisions to model-driven decisions through closer collaboration between data scientists building predictive models, business experts using models to make decisions, and IT teams governing underlying data and analytics infrastructure.

Enable data scientists to leverage data, elasticity, and processing power of Snowflake’s Data Cloud directly within Domino’s Enterprise MLOps Platform.

Jupyter Notebook in Domino calling Snowflake data.

Enable a modern analytics approach for data scientists

Modern approach to analytics enables data scientists to leverage data and processing power in Snowflake’s Data Cloud from within Domino. Break down data silos by unifying internal and 3rd party data in Snowflake and quickly developing models in Domino using Jupyter, R Studio, VS Code, or other IDEs.

Live query data in Snowflake with Snowpark Java/Scala UDFs without data transfers diminishing performance or productivity, or security. Leverage the elastic compute and processing of Snowflake’s Data Cloud from within Domino for building, executing, and productionalizing models.

“By leveraging Domino and Snowflake’s Data Cloud Together, Braze has the flexibility to build machine learning models across our databases and share data seamlessly across our organization in real-time.”

― Jon Hyman, Co-Founder & CTO, Braze

Snowflake search within Domino.

Reduce silos and create a research flywheel

Data science teams can find and build on past work and freely collaborate with their peers to not only unlock new ideas and breakthrough insights, but share them across business stakeholders through Snowflake, ensuring simultaneous access with the elasticity of Snowflake’s Data Cloud.

Data scientists can write model-derived data back into Snowflake’s Platform for end users to access via reporting and SQL business query tools.

Enterprise-scale MLOps workflows through writing Domino results back to Snowflake.

Create enterprise-scale MLOps workflows

Manage the data science lifecycle from ideation through model deployment and model monitoring. Domino’s powerful scheduling function makes it possible to orchestrate a full workflow, sourcing data from Snowflake, running model code, and writing results such as predictive scores back into Snowflake for end user consumption - keeping all results in one place alongside existing data.

Snowflake credentials as environmental variables.

Ensure IT governance and security

Authenticate with Snowflake at the data science project level, allowing each project to have it’s own independent connection and Snowflake resources or even use multiple Snowflake accounts.

Ensure security and flexibility by leveraging Snowflake authentication for Domino data science projects by storing user Snowflake credentials as environment variables, or utilizing popular tokenization and identify management solutions.

“Snowflake provides the robust scalability, elasticity, and security we need to hold enterprise volumes of health data, while the integration with Domino ensures data scientists can securely and effectively share model output with business stakeholders, clients, and partners.”

― Luca Foschini, Co-founder and Chief Data Scientist at Evidation


Domino Data Lab Deepens Integration with Snowflake to Help Mutual Customers Accelerate Returns on Data Science

Domino Data Lab Deepens Integration with Snowflake to Help Mutual Customers Accelerate Returns on Data Science

Simplify data access and publish model results in Snowflake using Domino Data Lab

Simplify data access and publish model results in Snowflake using Domino Data Lab

Connecting to Snowflake from Domino

Connecting to Snowflake from Domino

Domino’s growing partner ecosystem helps our customers accelerate the development and delivery of models with key capabilities of infrastructure automation, seamless collaboration, and automated reproducibility. This greatly increases the productivity of data scientists and removes bottlenecks in the data science lifecycle.

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