Snowflake extends support for Python, gives developers greater flexibility

At its Snowday event, Snowflake announced that data scientists, data engineers and application developers can now use Python natively within Snowflake as part of Snowpark, the company's developer framework.

With Snowpark for Python, developers will be able to collaborate on data in their preferred language, the company states.

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.

According to the company, 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 leverage the scale and performance of Snowflake's engine, as well as native governance and security controls built-in to Snowflake's platform.

In addition to Java and Scala, Snowpark now supports Python which enables users to have different languages and different users 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 access Python open source libraries, without the need for manual installs and package dependency management, the company states.

According to Snowflake, 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.

Snowflake has highlighted key benefits that data teams can realise with Snowpark for Python. This includes the ability to:

• Accelerate their pace of innovation using Python's familiar syntax and 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.

Snowflake SVP of product Christian Kleinerman says, "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."

Kleinerman says, "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."

He concludes, "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.

Snowpark for Python is currently in private preview.

Snowflake's Data Cloud is designed to enable customers to unite siloed data, discover and securely share data, and execute diverse analytic workloads.