What is Snowflake?
In the course, Peter Olson calls Snowflake users “builders” and the tool itself the “Data Cloud.” That’s actually a great way to think about it. Whether you’re a data analyst, data scientist, data engineer, or BI analyst, Snowflake provides practically everything you need—and more—for your work.
Even PPC specialists can benefit from using Snowflake, especially thanks to its support for LLMs that work with table formats. Imagine you have a merchant feed with a description attribute. You can easily upload your tables and write a prompt like this:
SELECT
id,
description,
COMPLETE(
'openai-gpt-5',
CONCAT(
'Generate an SEO-optimized title (max 60 characters, catchy, keyword-rich) for the following description: ',
description
)
) AS seo_title
FROM product_descriptions;
Need to store data? You can create scalable databases in Snowflake.
Don’t want to overpay? Snowflake lets you choose warehouses of different sizes for your processes, so you can balance cost and performance (a smaller warehouse is cheaper, though slower).
Need to process data? You can use Snowflake Notebooks with SQL, Python, JavaScript, and many other languages.
Want to apply machine learning? Snowflake has built-in ML models that can handle datasets with millions of rows.
Finally, need to deploy an app? Even that is possible directly in Snowflake.
I highly recommend this course on Coursera for beginners in the world of data analysis. If you struggle to understand the differences between data analysts, data scientists, data engineers, BI analysts, and related roles, this course gives you a clear overview of the processes you’ll encounter in each.