---
title: "Snowflake"
description: "2 posts about Snowflake, summarised, each linking to the original."
---

# Snowflake
> 2 posts about Snowflake, summarised, each linking to the original.

## Articles

### [Apache Arrow Cut Snowflake Fetch Memory Growth by Up to 79%](https://yomu.fyi/post/apache-arrow-cut-snowflake-fetch-memory-growth-by-up-to-79.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Dwight Temple
- Published: Jul 22, 2026

An ML training run was exhausting worker memory before training could start because a cold Snowflake fetch peaked at 88.8 GiB RSS, versus 17.6 GiB when loading local Parquet. The shared Snowflake-to-pandas path created Python objects and SQLAlchemy Row proxies before pandas built the DataFrame, with temporary representations requiring roughly three times the final DataFrame size at peak. An Apache Arrow path for supported reads reduced median peak process RSS growth by 60–79% across three query shapes, while peak Python allocations fell by 69–87%. The narrow and wide tests also ran faster, but aggregate timing was mixed, so elapsed time was not the headline claim. Existing jobs adopted the default without query rewrites, with normalization preserving the pandas contract and SQLAlchemy retained for unsupported reads; in one workflow, lower fetch peaks doubled the training-data window on the same cluster size.


### [Cost Efficient Snowflake CI](https://yomu.fyi/post/cost-efficient-snowflake-ci.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Kevin Chao
- Published: Nov 28, 2022

Ramp’s data team used Snowflake CI checks that created a full duplicate production database for each pull request, causing rising warehouse costs and up to 30 minutes of developer idle time as pull-request volume and model count grew. They changed the process by storing dbt manifest.json artifacts in S3, using dbt’s state selector to build modified models, and generating an upstream-model list for cloning. A custom dbt macro uses Snowflake’s information schema to create zero-copy clones of required production tables in the pull-request database, preserving a single namespace for debugging instead of relying on defer across databases. The resulting CI command sequence builds and tests modified models plus direct downstream dependencies, and the post reports a dramatic, mostly sustained dip in the rolling seven-day average warehouse cost despite higher pull-request velocity.