Traditionally, when working with Spark workloads, you would have to run separate processing clusters for different languages. It is important to know How to Get Value from Snowpark. There are four times that you need to be aware about. Capacity management and resource sizing are also a hassle. Snowflake addressed these problems by providing native support for different languages. With consistent security, governance policies, and simplified capacity management, Snowflake pulls ahead as a great alternative to Spark.
Snowpark provides a set of libraries and runtimes in Snowflake to securely deploy and process non-SQL code, including Python, Java, and Scala. On the client side, Snowpark consists of libraries including the DataFrame API and native Snowpark machine learning (ML) APIs in preview for model development and deployment. Specifically, the Snowpark ML Modeling API (public preview) scales out feature engineering and simplifies model training while the Snowpark ML Operations API includes the Snowpark Model Registry (private preview) to effortlessly deploy registered models for inference.
On the server side, to get to get Value from Snowpark, runtimes include Python, Java, and Scala in the warehouse model or Snowpark Container Services (private preview). In the warehouse model, developers can leverage user-defined functions (UDFs) and stored procedures (sprocs) to bring in and run custom logic. Snowpark Container Services are available for workloads that require the use of GPUs, custom runtimes/libraries, or the hosting of long-running full-stack applications.
Snowpark DataFrame
How to Get Value from Snowpark- Four Tips
Snowpark brings deeply integrated, DataFrame-style programming to the languages that data engineers prefer to use. Data engineers can build queries in Snowpark using DataFrame-style programming in Python, using their IDE or development tool of choice.
Behind the scenes, all DataFrame operations are transparently converted into SQL queries that are pushed down to the Snowflake scalable processing engine. Because DataFrames use first-class language constructs, to get Value from Snowpark, engineers also benefit from support for type checking, IntelliSense, and error reporting in their development environment.
Snowpark User Defined Functions (UDFs)
Custom logic written in Python runs directly in Snowflake using UDFs. Functions can stand alone or be called as part of a DataFrame operation to process the data. Snowpark takes care of serializing the custom code into Python byte code and pushes down all of the logic to Snowflake, so it runs next to the data. To host the code, Snowpark has a secure, sandboxed Python runtime built right into the Snowflake engine. Python UDFs scale-out processing is associated with the underlying Python code, which occurs in parallel across all threads and nodes, and comprises the virtual warehouse on which the function.
Who Should use and get Value from Snowpark?
Snowpark is really great, but it’s not for everyone. One of the greatest things about Snowflake is that it lets users do really big things with SQL, especially when paired with tools like dbt. But some workloads are particularly well-suited for Snowflake. We think those workloads fall into three broad categories:
- Data Science and Machine Learning – Data Scientists love Python, which makes Snowpark Python an ideal framework for machine learning development and deployment. Data scientists can use Snowpark’s Dataframe API to interact with data in Snowflake, and Snowpark UDFs are ideal for running batch training and inference on Snowflake compute. Working with data right inside Snowflake is significantly more efficient than exporting to external environments. For one of our clients, we migrated a 20-hour batch job to run in 30 minutes on Snowpark.
- Data-Intensive Applications – Some teams develop dynamic applications that run on data. Snowpark lets those applications run directly on Snowflake compute. Snowpark can be combined with Snowflake’s Native App and Secure Data Sharing capabilities to allow companies to process their customer’s data in a secure and well-governed manner. We’ve worked with one of our clients to do exactly that!
- Complex Data Transformations – Some data cleansing and ELT workloads are complex, and SQL can inflate that complexity. A functional programming paradigm lets developers factor code for readability and reuse, while also providing a better framework for unit tests. On top of that, developers can also bring in external libraries from internal developers, third parties, or open source. Snowpark Python makes it so all of that well-engineered code can run on Snowflake compute without depending on shipping data to an external environment.
- For more analysis regarding snowflake for data analytics, talk to our experts.
