Dataiku + Snowflake: Where Everyday AI Meets the Data Cloud

Dataiku and Snowflake deliver cloud-optimized, enterprise-ready AI solutions that enable customers to easily and quickly build, deploy and monitor all types of data science projects including machine learning and deep learning. Companies can now acquire crucial insights to make better business decisions and drive new business opportunities at scale. 

Dataiku and Snowflake are complementary solutions that combine Snowflake’s highly scalable computational power and processing flexibility with Dataiku’s machine learning and model management capabilities.

“With Snowflake, a data scientist can run a query for 2 minutes and pay for only those 2 minutes of compute time. There is no reason to run a smaller cluster for 4 minutes anymore – spin big and spin back down when you are done!”

– Anthony Schneider, Technical Architect (Snowflake Alliances)

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Enterprise AI meets The Data Cloud

• Performance: Dataiku and Snowflake easily scale to tackle big data, providing elastic compute performance that matches growing data and machine learning requirements. 
  • Data teams can push data processing tasks from Dataiku to Snowflake, which allows users to fully leverage the computational power and elasticity of Snowflake.
  • Dataiku provides data teams with in-database charting capabilities to visualize entire datasets that are stored in Snowflake, ensuring optimized performance.
  • Dataiku enables machine learning models built with Snowflake data to be quickly and easily deployed into production, with better model and lifecycle management.
• Simplicity: Dataiku provides an intuitive graphical interface for accessing and analyzing all data in Snowflake, which makes it easier and faster to operationalize machine learning models.
  • Design complete data science and machine learning workflows by assembling visual and code recipes to design data transformations (group, join, sort, filter, pivot, etc.) in Dataiku and fully execute in Snowflake.
  • Dataiku’s graphical interface gives users an intuitive environment to seamlessly load data from AWS and Azure to Snowflake.
• Operationalization: Dataiku and Snowflake support access to the same data by multiple collaborating groups at the same time without diminishing performance.
  • More users can leverage the power and scalability of Snowflake through Dataiku to build more data products and deploy more projects into production.
  • Cross-functional teams (data, business, IT teams, etc.) can collaborate simultaneously on the same projects and data in Snowflake without sacrificing performance or data integrity.
  • Data and IT teams can collaborate seamlessly and easily with the same data.

Integration Highlights

• Use the native Dataiku connector to seamlessly read or write Snowflake tables.
• Scale data preparation and feature engineering processes by running entire Dataiku flows in-database, including visual recipes.
• Full support for SQL coding to create advanced data processing logic and leverage native Snowflake SQL features and functions.
• Leverage all Dataiku features to perform interactive analytics on Snowflake data, including charts, notebooks, metrics, statistical data profiling, and cataloguing.
• Train and deploy machine learning models on Snowflake data at scale, thanks to the Snowflake Spark connector.
• Automate all data access, transformation, or modeling pipelines on Snowflake using the Dataiku deployment and automation infrastructure.
• Works both with AWS and Microsoft Azure cloud platforms.

Take a look at the Snowflake and Dataiku Machine Learning Showcase to explore how Dataiku pushes down storage and compute operations directly into Snowflake.

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