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:

  1. Writeback to table

  2. Automatic Requery after successful writeback

  3. Python function runs against table

  4. Data is returned via a view containing the function

To re-create this in Snowflake, follow the guidance in this snowflake tutorial.

A gif of a snowpark workbook helping drive budgeting decisions.

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 🌌

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