Snowflake is renowned for its vast capabilities, and Snowpark is no exception.
Snowpark stands out as a game-changer for data engineers. It empowers them to tap into the familiar terrain of languages like Scala, Java, and Python, but with the unique advantage of not having to move data out of Snowflake.
Yet, the innovation doesn’t stop there. When you pair Snowpark with Ascend, the landscape changes entirely. Together, they redefine data processing by offering enhanced automation, scalability, and a drastically simplified workflow.
If you’re new to Snowpark or wish to dive deeper into its strategic benefits, we strongly recommend our thorough piece: Snowflake Snowpark: Overview, Benefits, and How to Harness Its Power.
In this guide, we aim to highlight how the combination of Ascend and Snowpark not only streamlines tasks but also significantly reduces the steps involved, offering a platform-managed experience like no other.
Overview of the Differences: How to Use Snowpark With and Without Ascend
If you go through the “Getting Started with Snowpark in Snowflake Python Worksheets” tutorial offered by Snowflake, you’ll appreciate the richness of the content. However, one thing you might notice is that it requires several stages to reach the final outcome.
The process includes seven steps, as represented in the graphic below.
This step-by-step breakdown underscores the notion that while Snowpark is immensely powerful, navigating it without Ascend requires maneuvering through a detailed and multi-faceted process:
- Data Loading: To effectively use Snowpark, users must become acquainted with the Snowpark Python library. This entails loading data from particular tables into Snowpark DataFrames.
- Data Transformations: Data manipulation in Snowpark involves diving into the snowflake.snowpark.functions library, necessitating familiarity with a slew of functions.
- Examining & Storing Data: Snowpark provides the option to inspect query execution plans using the explain() function, introducing another layer of complexity. After these operations, users save the transformed data into Snowflake tables, with an option to return the data as Snowpark DataFrame.
- Advanced Deployment: Snowpark offers the ability to deploy the entire worksheet as a Python Stored Procedure. This introduces benefits like scheduling tasks via Snowflake. However, it also adds another layer of intricacy to the process.
On the other hand, when you opt for Ascend to handle Snowpark, the entire experience takes a dramatic turn. Here’s what it boils down to:
- Ingesting Data: No longer do you have to grapple with multiple data ingestion steps. It’s direct and straightforward.
- Writing Business Logic: Once your data is ingested, all you need to focus on is your core business logic.
The rest? It’s platform-managed:
- Schema/table creation
- Code deployment
- Job scheduling
- Warehouse selection
- Data loading
- and more!
No manual interventions, no complexities. Just a smoother, more efficient process.
A Practical Demonstration
Let’s get our hands dirty and see how to use Snowpark in two steps in action.
Conclusion
In the rapidly evolving world of data processing and management, selecting the right tools and methodologies can determine the success or setback of any enterprise. Snowpark, as a powerful feature of Snowflake, offers remarkable functionalities when used in its native environment. However, integrating it with Ascend propels its capabilities to unprecedented heights.
The streamlined processes, reduced manual interventions, real-time data interactions, and platform-managed experiences offer users a competitive edge. Snowflake’s Snowpark and Ascend together pave the way for a more efficient, intuitive, and responsive data management process.
