Author name: Abhay

Yesterday you learned how Snowflake stores data in immutable micro-partitions and how clustering keeps those partitions organized for fast pruning. Today we’re zooming out to a bigger question: what kind of table should you create in the first place? Snowflake has six distinct table types — Permanent, Transient, Temporary, External, Dynamic, and Iceberg — and […]

Yesterday you saw how Snowflake uses caching to avoid redundant work. Today we’re going one layer deeper — into how Snowflake physically stores your data. There are no indexes in Snowflake. None. Zero. So how does it find a single row in a table with billions of records? The answer is micro-partitions — and once

Yesterday you mastered multi-cluster warehouses — scaling OUT with clusters to handle concurrency, choosing between Standard and Economy policies. But here’s the real question the exam is building toward: in a production Snowflake account, how many warehouses should you actually have, and how should each one be configured? The answer is never “one big warehouse

Yesterday you learned how to pick the right warehouse size — XS through 6XL, doubling credits each step. But here’s a scenario the exam loves: 50 analysts hit your dashboard at 9 AM Monday. Your Large warehouse handles one complex query fine, but now there are 50 in the queue. Making it XL won’t fix

Week 2 starts with the component that controls your Snowflake bill more than anything else: the virtual warehouse. You already know from Day 2 that warehouses live in the compute layer and are completely independent of storage. Today you’ll learn exactly how they’re sized, how credits scale (spoiler: it doubles every step), and two C03-specific