Since its launch earlier this year, Snowflake’s Snowpark developer framework has helped data scientists, data engineers, and application developers collaborate more easily and streamline their data architecture by bringing everyone onto the same platform. Snowpark (public preview) lets developers collaborate on data in the coding languages and construct familiar to them, while taking advantage of Snowflake’s security, governance, and performance benefits.
Today at Snowday, we were thrilled to announce that Snowpark now natively supports Python and is currently in private preview.
“Snowpark has dramatically expanded the scope of what’s possible in the Data Cloud,” Snowflake’s SVP of Product Christian Kleinerman said. “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 mobilizing the world’s data, Python broadens even further the choices for programming data in Snowflake, while streamlining data architectures.”
The power of Python lies in its rich ecosystem of open source packages. In recent years, open source packages have been a big enabler for data science. As part of the Snowpark for Python offering, we wanted to bring enterprise-grade open source innovation to the Snowflake Data Cloud while helping ensure a seamless experience for data scientists and developers to do their work. Through our recent Anaconda partnership and product integrations, this seamless experience is now a reality. Snowflake Data Cloud users who already benefit from near-instant and governed access to data will now be able to speed up their Python-based workflows by taking advantage of the seamless dependency management and comprehensive set of curated open source packages the Anaconda partnership provides. The integrated Anaconda package manager is immensely valuable as without the right tool set, resolving dependencies between different packages can land developers in “dependency hell,” which can be a huge time sink.
Further, Snowpark for Python enables data teams to operate with improved trust and security. Users can collaborate against the same data using their preferred languages, without needing to copy or move the data. Not only can this eliminate ungoverned copies of data, but all code is run in a highly secure sandbox directly inside Snowflake for further protection.
To be the first to know when you can join the public preview, sign up here.
In case adding one of the most popular programming languages natively to Snowpark wasn’t enough, we also shared details around a few other exciting developments for Snowpark at Snowday:
Additional Snowpark Enhancements
Snowpark adds Azure and Google Cloud for availability across all clouds
The Snowpark API and Java UDFs, available in AWS since June, are now available in Microsoft Azure and Google (in private preview). By supporting the three leading cloud service providers, we are greatly expanding access to Snowflake’s capabilities to developers everywhere.
“At Snowflake, we aim to provide the same, great support across all of the clouds and regions in which we operate. Cloud providers can be pretty different from each other, but that should be our problem, not yours,” Snowflake’s Senior Product Manager Isaac Kunen said.
Enhanced table functions
Snowpark has expanded its Java function support to include table functions, now in public preview across all supported cloud providers, opening up even more use cases within Snowpark. This is a giant step forward from supporting scalar functions (which operate on each row in isolation), enabling developers to execute complete functions such as returning a single result for a group of rows, or maintaining a state across multiple rows. You can learn more about this new capability here.
Unstructured file processing
With this new capability, now in private preview, developers can access and process unstructured data directly within Snowflake using Snowpark. For example, users can now read exchangeable image format (EXIF) data alongside other structured and semi-structured data sets. Paired with table functions, it’s a breeze to transform unstructured data for use cases including parsing PDFs such as invoices, or extracting metadata from industry-specific files such as DICOM files, and much more.
Java Stored Procedures and Tasks
Java Stored Procedures support for Snowpark, now in private preview, allows client-side code to work within a procedure and run inside of Snowflake. This enables developers to define, execute, and schedule complex application code, with no separate client to manage. Paired with Tasks, this can be used to schedule and coordinate Snowpark jobs.
Snowflake has released a new logging framework in private preview, callable from within Snowpark, that improves development productivity with easier monitoring and debugging among some of the use cases.
Snowpark expands the scope of what’s possible in the Data Cloud and is shaping the future of data engineering and data science. These latest innovations will make it easier for organizations to maintain business continuity across clouds and regions; help data engineers and data scientists build pipelines, ML workflows, and data applications faster; and remove the complexity of getting the right data into the hands of customers.
To learn more about Snowpark, watch the rest of the content from Snowday here.
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