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Snowflake Shapes the Future of Data Science with Python Support

Snowflake (NYSE: SNOW), the Data Cloud company, today announced at its Snowday event that data scientists, data engineers, and application developers can now use Python – the fastest growing programming language[1]– natively within Snowflake as part of Snowpark, Snowflake’s developer framework. With Snowpark for Python, developers will be able to easily collaborate on data in their preferred language. At the same time, they can leverage the security, governance, and elastic performance of Snowflake’s platform to build scalable, optimised pipelines, applications, and machine learning workflows. Snowpark for Python is currently in private preview.

Developers want flexibility when working with data, simpler environments that require less administrative work and maintenance, and immediate access to the data they need. Snowpark brings the programming languages of choice for data to Snowflake. With Snowpark, developers can unlock the scale and performance of Snowflake’s engine, and leverage native governance and security controls built-in to Snowflake’s easy-to-use platform. In addition to Java and Scala, Snowpark now supports Python, allowing users to have different languages and different users all working together against the same data with one processing engine, without needing to copy or move the data.

As a result of the recently announced partnership with Anaconda, Snowflake users can now seamlessly access one of the most popular ecosystems of Python open source libraries, without the need for manual installs and package dependency management. The integration can fuel a productivity boost for Python developers. Snowflake’s recently launched Snowpark Accelerated Program also supports customers with access to numerous pre-built partner capabilities and integrations, from directly within their Snowflake account.

With Snowpark for Python, data teams can:

  • Accelerate their pace of innovation using Python’s familiar syntax and thriving ecosystem of open-source libraries to explore and process data where it lives.
  • Optimise development time by removing time spent dealing with broken Python environments with an integrated Python package dependency manager.
  • Operate with improved trust and security by eliminating ungoverned copies of data with all code running in a highly secure sandbox directly inside Snowflake.

Novartis, the multi-national healthcare company that provides solutions to address the evolving needs of patients worldwide, needed a way to empower their global team of analysts and data scientists with a powerful data platform that would reduce data preparation time and provide self-service capabilities for building models and running analytics.

“Novartis’ mission is to reimagine medicine to improve and extend people’s lives, and to do so successfully today we need to leverage digital technologies that continue to put data and data science at the center of our transformation,” said Loic Giraud, Global Head of Digital Platform & Product Delivery at Novartis. “As a progressive, data-driven life-science organisation, the flexibility and scale of Snowflake’s Data Cloud allows us to accelerate our pace of knowledge through data interpretation and insight generation, bringing more focus and speed to our business. Bringing together all available data ultimately unlocks more value for our employees, patients, and health care providers, and data science innovations help us realise this goal.”

“Snowflake has long provided the building blocks for pipeline development and machine learning workflows, and the introduction of Snowpark has dramatically expanded the scope of what’s possible in the Data Cloud,” said Christian Kleinerman, SVP of Product at Snowflake. “As with Snowpark for Java and Scala, Snowpark for Python is natively integrated into Snowflake’s engine so users can enjoy the same security, governance, and manageability benefits they’ve come to expect when working with Snowflake. As we continue to focus on mobilising the world’s data, Python broadens even further the choices for programming data in Snowflake, while streamlining data architectures.”

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