06.02 · Walkthrough
Snowflake for ML Data
Create tables, load data, run transformations, and manage compute warehouses for an ML training dataset in Snowflake.
Snowflake can act as the structured data layer for ML training pipelines, with durable tables for raw, cleaned, feature, and final datasets, and separate warehouses supplying compute for loads, transformations, validation, and queries. Good practice is reproducible SQL, clear dataset lineage, and warehouse settings that match workload size without leaving compute idle.
What this lesson answers
- how to prepare ML training data in Snowflake
- how Snowflake warehouses affect ML pipeline cost
- how to structure feature tables in Snowflake
Notes
Snowflake is often used as the structured data layer for machine learning because it separates storage from compute and gives teams a reliable place to prepare training datasets. In this lesson, emphasize that tables hold the durable data, while warehouses provide the compute used to load, transform, query, and validate that data. A working engineer should understand that creating a table is about defining the shape and contract of the dataset, loading data brings raw or staged records into Snowflake, and transformations turn that data into clean features, labels, and training-ready outputs.
Wh…
Common questions
- Why use Snowflake for ML training data?
- Snowflake gives ML teams a central, structured place to store and prepare training data. Tables define durable dataset contracts, while SQL transformations can clean records, join sources, create features, and produce final training outputs. Separating storage from compute also lets teams scale processing without changing where the data lives.
- How should ML datasets be organised in Snowflake?
- A practical layout separates raw ingested data, cleaned intermediate tables, feature tables, and final training tables. That structure makes the pipeline easier to reason about, rerun, and debug. When model quality changes, clear lineage helps engineers trace whether the issue came from source data, feature logic, filtering, or label generation.
- What do Snowflake warehouses do in an ML pipeline?
- Warehouses provide the compute used to load, transform, query, and validate data. Smaller warehouses fit development and lightweight checks, while heavier transformations or bulk loads may need more capacity. Auto-suspend and auto-resume keep workflows convenient while reducing the risk of paying for unused compute.
Short definition: what is Snowflake for ML Data?