Snowpark for Python: A Programming Gamechanger?
As data analytics consultants, we help a whole range of organizations implement and optimize modern data platforms. That means we have to be fluent in a whole range of programming languages, depending on what our clients use or what they need to use with the new platform they’re building.
We know from experience that working with different languages can create complications and inefficiencies. It’s less than optimal having to make special adaptations to a platform so we’re working in a language that a client is comfortable with or having to train a client on a new language as the platform is being built.
I’ve often thought it would be great if there was a way to overcome the “language barrier” – for everyone involved in a project to program in whatever language they want.
Thanks to a new innovation from Snowflake, we’re actually getting there.
Last year, Snowflake launched Snowpark, which gives developers a simple and efficient way to program data in their language of choice. Snowpark lets teams with different skill sets collaborate and work on the same data within Snowflake, making processing data faster and easier.
Snowpark recently got a big boost when Snowflake launched Snowpark for Python, bringing the world’s most popular programming language into Snowpark along with Java and Scala. Now programmers can use Python, Java, or Scala with familiar DataFrame and custom function support to build powerful and efficient pipelines, machine learning workflows, and data applications. Snowpark for Python will be an invaluable tool for us and our clients, helping optimize the Snowflake Data Cloud experience by enhancing performance, ease of use, governance, and security.
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What Does Snowpark for Python Mean for You (and us)
For us, Snowpark for Python is a potential gamechanger. Let’s say we’re working with a company called WidgetCo on a Snowflake-based data platform. What will Snowpark mean for the way we work and the solution we can deliver?
To start, Snowpark for Python opens a new world of possibilities for collaboration. We can collaborate with WidgetCo’s teams on the same single copy of their data, while Snowpark natively supports everyone’s programming language of choice. Snowpark for Python means we can write code in WidgetCo’s Integrated Development Environment of choice and execute data processing in Snowflake with pushdown capabilities, all while minimizing the complexity of having to manage additional environments to run non-SQL data pipelines.
Snowpark also helps us deliver higher performance with lower maintenance. We can work with WidgetCo to build scalable, optimized pipelines, apps, and ML workflows with superior price/performance and near-zero maintenance, powered by Snowflake’s elastic performance engine. Snowpark for Python enables efficient data processing and supports near-unlimited users and workloads with tailored transformation needs. It also speeds up Python-based workflows with seamless access to open-source packages and package managers via Anaconda Integration.
And with Snowpark, WidgetCo can enhance data governance and security – two priorities which are always top of mind in today’s data environment. Snowpark allows WidgetCo to easily enforce consistent governance and security policies from a single platform. They can manage libraries with full governance control while preventing unwanted network access, and eliminate data redundancy across different systems and services with Snowflake’s Secure Data Sharing.
Snowpark for Python started out in private preview in March 2022. Public preview launched at Snowflake Summit in June. We’re looking forward to taking advantage of its features to deliver enhanced solutions to our clients. Contact us to learn more about what Snowpark for Python means for your company.
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