Snowpark is a powerful feature from snowflake which combines the use of SQL and python, Java or Scala. This article focuses on how Snowpark can be used to enrich analytics and drive-confident decision-making.
What types of analysis can I achieve using Snowpark?
Any! As long as there is a python package and of course, data, to support your analysis. Combining Astrato writeback with Snowpark
Here are a few examples to get spark your creativity:
Forecasting
Clustering
Sentiment analysis
Machine learning
ℹ Snowpark features cannot connect to the internet and are secure - this does mean that all data and python packages need to be in snowflake.
Example workbook featuring Snowpark & writeback
👨🔬 Click the link to try out this Snowpark workbook
📺 Watch the recorded demo here on Astrato Live
Users of this workbook are planning marketing budgets for a campaign next week. They need to make sure the spend is fairly distributed - previous campaign results have been loaded and used to train a model.
Each time a model is run, scenarios are sent back to snowflake for easy retrieval using writeback. A model is run against them in real-time to understand what return on investment (ROI) may be achieved by the proposed budget allocation.
This model helps drive budgeting decisions. In this model, only a few steps need to occur to driver powerful analytics:
Writeback to table
Automatic Requery after successful writeback
Python function runs against table
Data is returned via a view containing the function
To re-create this in Snowflake, follow the guidance in this snowflake tutorial.
Learn more about Snowpark
For more in-depth snowpark tutorials, navigate to the Snowflake documentation links below:
We'd love to hear how you are using Snowpark, let us know in Astrato Galaxy 🌌