Deep Dive: Snowflake Notebooks
Forget Jupyter. Native Snowflake Notebooks are here, offering mixed SQL/Python cells and integrated scheduling.
Data Scientists have always felt like second-class citizens in the data warehouse. They had to extract data to their local Jupyter notebooks to do “real work.”
Snowflake Notebooks bring the notebook experience to the data.
The Interface#
It looks familiar (cells), but with superpowers:
- SQL Cells: Write a query (
SELECT * FROM sales). - Python Cells: Reference that SQL result immediately as a DataFrame (
df = sql_cell_1.to_pandas()). - Visualization Cells: No code needed. Just point and click to graph the dataframe.
State Management#
Unlike a local notebook where you lose state if you close the tab, Snowflake Notebooks persist their variables and connections.
Scheduling#
This is the killer feature. You can “Productize” a notebook by adding a schedule (CRON). Snowflake effectively wraps the notebook running in a headless mode.
Useful for:
- Daily ML model retraining.
- Data quality reports.
- Generating email digests.
Git Integration#
Yes, they support Git! You can version control your .ipynb files in GitHub and sync them to Snowflake.
Example Workflow#
- SQL Cell: Load raw data from a staging table.
- Python Cell: Use
scikit-learn(from Anaconda channel) to train a forecasting model. - Python Cell: Save the model object to a Snowflake Stage using
joblib. - SQL Cell: Register a UDF that uses that model file for inference.
All in one document, executable linearly.
Conclusion#
Snowflake Notebooks bridge the gap between Analysts (SQL) and Scientists (Python). They accelerate the “Experiment to Production” loop by removing the need for infrastructure management.