Snowflake’s debut in 2014, marked a major shift in the world of data warehousing and data processing. Since its introduction, Snowflake is continuously evolving with the regular addition of new robust features. Traditionally, developers mainly used SQL for data transformation in Snowflake. However, the platform’s versatility is increasing with the introduction of Snowpark, a developer framework, where users can write code in their preferred programming languages.
Snowpark: A Sneak Peek
Snowpark in-database technology provides a rich set of APIs and runtime environments within the Snowflake platform. It allows programmers to process data within the same environment using popular programming languages Java, Scala, and Python.
As data volumes grow exponentially, often reaching terabytes in size, moving such massive datasets to external environments for computation becomes increasingly challenging and inefficient. Snowpark addresses this issue by eliminating the need for separate processing environments. Instead, it allows developers to work directly on a single copy of the data within Snowflake, thereby enhancing collaboration between developers with data duplication.
Snowpack programming languages
Snowpark: The Standout Features
Developing complex logic for large-scale applications is often a challenge that developers face in production environments. Snowpark addresses this issue by allowing developers to write code in full-featured languages. There are many libraries available for such languages which make it easier to understand the logic. Some of the stand-out features of Snowpark- include:
- Multi language Support – Snowpark enables multi language support for developers to write code with one's native programming languages to transform data. This helps organizations utilize their existing resources better, without additional hiring.
- Parallel Processing – Snowpark’s parallel processing capabilities, with load distributed across clusters, enable efficient handling of massive volumes of data. This significantly improves performance, offering faster processing time and enhanced scalability.
- Machine Learning ML– Snowpark integrates with machine learning frameworks, enabling models to be developed, deployed, and executed with Snowflake. Snowpark provides a unified platform for users to seamlessly combine data processing, analytics, and machine learning leading to more streamlined and efficient data-driven operations in enterprises.
- Latency in Distributing Resources– Snowpark uses the lazy execution technique by bundling multiple operations together. This reduces the need for frequent data transfer between the client and Snowflake database leading to significant improvements in performance.
- Pricing– Snowpark comes bundled with Snowflake’s subscription and this avoids additional overheads for users. Organizations can utilize Snowpark’s advanced features without incurring additional costs.
Snowpark v/s SQ
|
Features |
SQL |
Snowpark |
|
Simplicity |
Easy to understand |
Complex to understand for beginners. Even for simple queries, users need to write the entire function. |
|
Execution Time |
Faster for small data |
Large-scale data has better performance |
|
Coding Language |
Only SQL can be used |
Java, Python, Scala |
|
Case Sensitivity |
Case Insensitive |
Case Sensitive |
|
ML |
No support |
End-to-end support for developing and executing ML models |
|
Security & Maintenance |
SQL server maintenance required. Data breach challenges are there. |
Data security without any additional infrastructure. |
|
Compliance |
Compliance challenges while moving data from server to machine. |
Better compliance as there is no data movement outside the Snowflake environment. |
A Working Example of Snowpark
We can connect to Snowpark using popular Integrated Development Environments (IDEs) such as IntelliJ and VS Code, and this enables us to work in preferred or familiar coding environments. Additionally, Snowflake's web interface, Snowsight, provides direct access to Snowpark, featuring Python worksheets for writing and executing Snowpark code. This gives us the flexibility to perform the development and testing without installing separate libraries thereby streamlining the development process.
Creating a new Python Worksheet
Steps:
- Login to Snowflake via Snowsight by providing Snowflake account credentials.
- Create a session
- Go to worksheet
- Click on Python Worksheet.
- Import Snowpark library
**Instead of Snowsight, any IDE could be used for eg: IntelliJ, VS Code etc.
Sample code to extract data from Snowflake using Snowpark:
Explanation:
- Import the necessary libraries.
- Connection details are provided to connect to the Snowflake account.
- Session is initiated providing connection properties.
- Execute SQL query
- show()method is used to display output from the table: USER_TABLE.
Conclusion
Snowpark, the powerful tool from Snowflake uses familiar programming languages that are easy for developers to code and understand thus making data processing easier and more efficient. It supports real-time data processing, end-to-end ML modeling, and integration with external libraries while keeping all the data within Snowflake thus maintaining data security and encryption. With all its advantages, Snowpark is going to be the go-to option for data processing and analytics in the future.
