The Snowflake Data Cloud’s utility expanded further with the introduction of its Snowpark API in June of 2021. Snowflake has staked its claim as a significant player in cloud data storage and accessibility, enabling workloads including data engineering, data science, data sharing, and everything in between.
Snowflake provides a unique single engine with instant elasticity that is interoperable across different clouds and regions so users can focus on getting value out of their data, rather than trying to manage it. In today’s data-driven world, businesses must be able to quickly analyze, process, and derive insights from large volumes of data. This is where Snowpark comes in.
Snowpark expands Snowflake’s functionality, enabling users to leverage the full power of programming languages and libraries within the Snowflake environment. The Snowpark API provides a new framework for developers to bring DataFrame-style programming to common programming languages like Python, Java, and Scala. By integrating Snowpark into Snowflake, users can perform advanced data transformations, build complex data pipelines, and execute machine learning algorithms seamlessly.
The interoperability empowers organizations to extract greater value from their data, accelerating their speed of innovation.
What is Snowpark?
Snowpark’s API enables data scientists, data engineers, and software developers to perform complex data processing tasks efficiently and seamlessly. It has eliminated the need for data transfer through its high-level programming interface that allows users to write and execute code in their preferred programming language, all within the Snowflake platform. Snowpark comprises a client-side library and a server-side sandbox that enables users to work with their preferred tools and languages while leveraging the benefits of Snowflake virtual warehouses.
When developing applications, users can leverage the capabilities of Snowpark’s DataFrame API to process and analyze complex data structures and support various data processing operations such as filtering, aggregations, and sorting. In addition, users can create User Defined Functions (UDFs) whose code is uploaded to an internal stage in the Snowpark library that, when called on, is executed on the server side.
This enables the creation of custom functions to process and transform data according to their specific needs, along with greater flexibility and customization in data processing and analysis. These DataFrames are executed lazily, meaning they only run when an action to retrieve, store, or view the data they represent is run. Users write code within the client-side API in Snowpark, which is executed in Snowflake, so no data leaves unless the app asks.
Moreover, users can build queries within the DataFrame API, providing an easy way to work with data within the Structured Query Language (SQL) framework while integrating common languages like Python, Java, and Scala. Those queries are then converted to SQL within Snowpark before they distribute computation through Snowflake’s Elastic Performance Engine which enables collaboration across multiple clouds and regions.
From its support of the DataFrame API, UDFs, and seamless integration with data in Snowflake, Snowpark is an ideal tool for data scientists, data engineers, and software developers who need to work with big data in a fast and efficient manner.
Snowpark for Python
With the growth in data science and machine learning (ML) in past years, Python is closing the gap on SQL as a popular choice for data processing. Both are powerful in their own right, but they’re most valuable when they’re able to work together. Knowing this, Snowflake built Snowpark for Python “to help modern analytics, data engineering, data developers, and data science teams generate insights without complex infrastructure management for separate languages” (Snowflake, 2022). Snowpark for Python enables users to build scalable data pipelines and machine-learning workflows while utilizing the performance, elasticity, and security benefits of Snowflake.
Furthermore, with Snowflake virtual warehouses optimized for Snowpark, machine learning training is now possible due to its ability to process larger data sets by providing resources such as CPU, memory, and temporary storage. This enables Snowpark functions, including the execution of SQL statements that require compute sources (e.g., retrieving rows from tables) and performing Data Manipulation Language (DML) operations such as updating rows in tables, loading data into tables, and unloading data from tables.
With the compute infrastructure to execute memory-intensive operations, data scientists and teams can further streamline ML pipelines at scale with the interoperability of Snowpark and Snowflake.
Snowpark and Apache Spark
If you’re familiar with the world of big data, you may know a thing or two about Apache Spark. In short, Spark is a distributed system used for big data processing and analysis.
While Apache Spark and Snowpark share similar utilities, there are some distinct differences and advantages to leveraging Snowpark over Apache Spark. Within Snowpark, users can manage all data within Snowflake as opposed to the need to transfer data to Spark. This not only streamlines workflows but also eliminates the potential adverse effects of sensitive data being taken out of the databases you’re working within and into a new ecosystem.
Additionally, the ability to remain in the Snowflake ecosystem simplifies processing by reducing the complexity of setup and management. While Spark requires significant hands-on time due to its more complicated setup, the ease of data transfer that is present between Snowflake and Snowpark requires no setup. You simply choose a warehouse and are ready to run commands within the database of your choosing.
Another major advantage Snowpark offers against its more complex counterpart is the simplified security measures. Leveraging the same security architecture that is in place within Snowflake eliminates the need to build out a specific complex security protocol like what is necessary within Spark.
The interoperability of Snowpark within the Snowflake ecosystem provides an assortment of advantages when compared with Apache Spark. Being a stand-alone processing engine, Spark comes with a significant amount of complexity from setup, ongoing management, transference of data, and creating specific security protocols. By choosing Snowpark, you opt out of the unnecessary complexity and into a streamlined functional process that can improve the efficiency and accuracy of any actions surrounding the big data you are handling – two things that are front of mind for any business in any industry whose decisions are derived from their ability to process and analyze complex data.
Why It Matters
Regardless of the industry, there is a growing need to process big data and understand how to leverage it for maximum value. When looking specifically at Snowpark’s API, leveraging a simplified programming interface with support for UDFs simplifies processing large data volumes in the users programming languages of choice. In uniting the simplified process with all the benefits of the Snowflake Data Cloud platform, there is a unique opportunity for businesses to take advantage of.
As a proud strategic Snowflake consulting partner, 2nd Watch recognizes the unique value that Snowflake provides. We have a team of certified SnowPros to help businesses implement and utilize their powerful cloud-based data warehouse and all the possibilities that their Snowpark API has to offer.
In a data-rich world, the ability to democratize data across your organization and make data-driven decisions can accelerate your continued growth. To learn more about implementing the power of Snowflake with the help of the 2nd Watch team, contact us and start extracting all the value your data has to offer.
