May 28, 2024

Snowflake Cortex vs. Snowpark – What’s the difference?

By Dominick Rocco

The Snowflake Data Cloud continues to push the boundaries with innovative tools designed to enhance data processing and analysis. Two of its standout offerings, Snowflake Cortex and Snowpark, provide powerful capabilities for different use cases. 

In this post, we will explore the unique features of each tool, helping you understand their strengths and guiding you on how to choose the right solution for your data needs.

What is Snowflake Cortex?

Snowflake Cortex is a suite of pre-built machine learning models and AI capabilities designed to accelerate the deployment of AI-driven solutions within the Snowflake ecosystem. Cortex offers a collection of ready-to-use models for common use cases, with capabilities broken into two categories: 

The combination of these capabilities allows organizations to quickly implement advanced analytics without the need for extensive data science expertise.

What Are the Advantages of Snowflake Cortex?

The primary advantage of Snowflake Cortex is its ability to democratize AI by making powerful machine learning tools accessible to a broader range of users. With Cortex, business analysts, data engineers, and developers can easily incorporate Predictive and Generative AI into their workflows using simple SQL commands and intuitive interfaces. This reduces the time and complexity typically associated with developing and deploying machine learning models from scratch.

Additionally, Snowflake Cortex integrates seamlessly with Snowflake’s core platform, ensuring that all AI and machine learning processes benefit from Snowflake’s scalability, security, and data governance features. By leveraging Cortex, organizations can enhance their data analytics capabilities, drive more informed decision-making, and unlock new insights from their data with minimal overhead and maximum efficiency.

What is Snowpark?

Snowpark is a developer framework introduced by Snowflake that allows data engineers, data scientists, and developers to write code in their preferred programming languages, such as Java, Scala, and Python, directly within the Snowflake environment. This enables users to build complex data transformations and workflows using familiar tools and languages while leveraging the powerful capabilities of Snowflake’s data platform.  

As an example, check out our blog post on implementing a data engineering pipeline for Sentiment Analysis with dbt.

What Are the Advantages of Snowpark?

One of the key benefits of Snowpark is its ability to bring custom code execution closer to the data, minimizing data movement and improving performance. By executing code natively within Snowflake, users can take advantage of the platform’s scalability, security, and governance features, ensuring efficient and secure data processing. This integration simplifies the development process and reduces the need for external processing systems.

Moreover, Snowpark supports the development of advanced analytics and machine learning models directly within Snowflake. With the ability to write custom functions and procedures, developers can create sophisticated data pipelines and analytical workflows, all within the same unified environment. 

At phData, we’ve seen tremendous performance benefits of running ML workloads in Snowpark. This not only streamlines operations but also ensures consistency and reliability across the entire data processing lifecycle.

How to Pick Between Cortex and Snowpark?

Choosing between Snowflake Cortex and Snowpark depends on your specific needs, skill sets, and the complexity of the tasks you aim to accomplish. If your primary goal is to quickly implement machine learning models and AI-driven insights with minimal coding, Snowflake Cortex is likely the best choice. Cortex’s pre-built models and user-friendly interfaces make it accessible for business analysts and developers who may not have deep expertise in data science, allowing for rapid deployment and immediate value.

On the other hand, if your organization requires more customized data processing and advanced analytics capabilities, Snowpark offers greater flexibility. Snowpark is ideal for data engineers and data scientists who prefer to write custom code in languages like Java, Scala, or Python and need to build complex data transformations and workflows. It enables you to develop bespoke machine learning models and data applications directly within the Snowflake environment, leveraging its powerful processing capabilities without moving data.

Ultimately, the decision comes down to the specific use cases and resources of your team. 

If you need ready-to-use solutions for standard AI tasks and want to minimize development time, Cortex is the way to go. However, if you need to create tailored solutions and have the technical expertise to write and manage custom code, Snowpark provides the flexibility and control required for more sophisticated projects. 

Both tools are designed to maximize the potential of Snowflake’s platform, so you can’t go wrong with either choice based on your needs.

What Are You Going to Build?

The great thing about Snowflake offering both Snowpark and Cortex is that any Snowflake users of any background can start building AI solutions.  At phData, we have a long history of incredible AI applications into production.  If you have any questions about AI on Snowflake, reach out to us today!

If your business is interested in wielding the power of AI with the Snowflake platform, phData can help drive success!

For a limited time, we’re offering a free generative AI workshop to help explore the potential of AI. In a 90-minute session, we will:

  • Explore use cases for generative AI with your business
  • Evaluate enterprise readiness to execute on your top use cases
  • Recommended next steps to advance AI for your organization

Data Coach is our premium analytics training program with one-on-one coaching from renowned experts.

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