Recently, Scientific Financial Systems’ founder Peter Millington participated in a timely one hour webinar on leveraging the power of Snowflake’s Snowpark for Python, with an emphasis on applications in Financial Analytics.
Sharing the stage with Pete for this event were:
- Kesav Rayaprolu, Snowflake Sr, OSI Sales Engineer
- Sumit Bhatia, Kipi.bi SVP. Kipi is SFS’s key development partner
- Ilyas Mohammad, Kipi.bi Lead Engineer, currently working full-time with SFS
- Koi Stephanos, SFS Lead Engineer and manager of Quotient development
- Marc Lowenthal, SFS Chief Product Officer
Key Take-Aways from the Snowpark Webinar
1. For Data Transformation, Snowpark:
- Offers a powerful alternative to SQL for data transformation
- Provides a flexible and expressive programming model for complex data transformations
- Allows users to access the rich ecosystem of Python libraries and tools
2. For Machine Learning, Snowpark:
- Enables the deployment of machine learning pipelines on Snowflake
- Allows utilization of Python’s machine learning libraries to build and deploy models directly within Snowflake
3. SFS’ Quotient integrates the power of Snowflake and Snowpark to help investment professionals discover new alpha using data science innovations
- Quotient for Snowflake’s architecture co-locates compute and data for maximum performance
4. Snowpark allows SFS to:
- Move all Quotient app components into Snowflake environment
- Exploit automated parallelization and compute “burstability”
- Leverage the Snowflake architecture for multi-user orchestration
We hope you enjoyed this webinar and found the key takeaways informative. If you have any questions, please feel free to contact us. We would also be happy to set up a time to discuss how Snowpark for Python can be used to accelerate your financial analytics projects.
