Snowflake
The data cloud that powers analytics and AI
Snowflake is a cloud data warehouse — which is enterprise-speak for "the place where companies put all their data to analyze it." It's become a central player in the AI space because, it turns out, you need a lot of well-organized data to build AI features. Who knew.
snowflake.comUse Cases
Analytics & reporting
Running complex queries across massive datasets. The thing your BI team uses every day.
ML feature stores
Organizing the data that feeds machine learning models in a consistent, reliable way.
Data sharing
Sharing datasets between companies or departments without copying data around.
AI/ML workloads
Running ML models directly where your data lives, instead of moving data to where your models are.
Key Features
- →Scales compute and storage independently
- →Cortex AI — run LLMs and ML models directly in Snowflake
- →Snowpark — write data pipelines in Python, Java, or Scala
- →Near-zero maintenance — fully managed
- →Data Marketplace — access third-party datasets
Why This Matters for PMs
If you're a PM at a company with a data team, you're probably already using Snowflake (or BigQuery, or Databricks). Understanding what your data warehouse can do helps you scope AI features realistically. The key insight: your AI features are only as good as the data in your warehouse. If your data is messy, incomplete, or siloed, no amount of fancy AI will save you.