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This tutorial will give you a chance to experience working with the Snowflake database and Domino. You will follow a basic data collection, engineering, and loading workflow and then create a model in Python that uses the data in Snowflake. Domino offers various methods to connect to Snowflake:

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Overview

In this Get Started series, you’ll learn how to work with Domino Data Stores to crush big data with the following workflow:
  1. Preliminaries – Data Engineering:
    1. Find data.
    2. Understand the data.
    3. Get the data.
    4. Wrangle data into a format usable for analysis.
  2. Analysis:
    1. Look at the data – normally using a subset of the complete dataset.
    2. Clean the data – deal with missing and errant data.
    3. Identify the arguments that you believe matter for your prediction to work.
  3. Model development:
    1. Try out several algorithms to determine which one produces the best results.
    2. Save the training function.
  4. Model training:
    1. Run the model training function on the complete dataset.
    2. Collect the model.
    3. Test again.

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Assumptions

  • This tutorial is aimed at data science professionals familiar with JupyterLab, Jupyter Notebooks, and the Python language.
  • The code is for illustration purposes. It is functional, tested, and offers a very basic view into the use of Domino with data in Snowflake.
  • Domino offers multiple connectivity modes with Snowflake — primarily:
    • Domino Data Sources - meant for read-oriented exploration.
    • The Snowflake Python library - meant for full-featured database operations in Snowflake.
  • Please use Domino’s file sync functionality to store your file progress in the project’s repository throughout the tutorial.

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Pre-requisites

  • Familiarity with Domino Workspaces and Datasets.
  • Access permissions (username, password, and authorization) to a Snowflake database.
  • The name of your Snowflake warehouse, database, and schema.
  • Domino permissions to set up a Snowflake Data Source (if applicable).
  • Snowflake’s SnowSQL command line tool for the data engineering and loading sections of this tutorial.
  • Familiarity with the SQL language and Pandas library.

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Next steps

The tutorial is designed to be followed in a sequence:
  1. Understand the data.
  2. Data engineering - Prepare and load the data into Snowflake.
  3. Use Snowflake with a Domino Data Source - A simple connectivity example.
  4. Feature exploration, data wrangling, and predictive weather model creation with the Snowflake Data Source.
  5. Create a weather prediction Launcher.
  6. Use Snowflake’s Python driver in Domino: Build a data update service with a Domino Job.
  7. Domino endpoint: Share your model with your organization.
  8. Snowflake Snowpark - Create a model in Domino and set it up as a Snowflake user-defined function (Video).