u/SELECT_dev

We put Snowflake and Databricks head-to-head across 5 scenarios.

𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 𝘄𝗼𝗻 𝟰 𝗼𝘂𝘁 𝗼𝗳 𝟱 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀:

- Sequential queries: 34% faster, 17% cheaper (at $2/credit)

- Concurrent queries: 38% faster, 39% cheaper

- Cold start: 54% faster (Databricks startup time: ~7 sec. Snowflake: sub-second. Every. Single. Time.)

- DML (delete + insert): 59% faster, 32% cheaper thanks to elite query pruning that treated 6B rows like 6M

𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗰𝗹𝗮𝗶𝗺𝗲𝗱 𝘁𝗵𝗲 𝗼𝗻𝗲 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀:

CTAS (Create Table As Select): 58% faster, 71% cheaper when writing billions of rows across multiple table shapes

If your workload is heavy on dbt materializations, large table builds, or data pipeline writes, Databricks has a real edge here.

If your workload is analysts running queries, dashboards, and incremental refreshes, Snowflake Standard looks compelling, even vs. Databricks Enterprise pricing.

- Read the full methodology and results: https://select.dev/posts/snowflake-vs-databricks-showdown

- Take a look at the repo: https://github.com/get-select/snowflake-databricks-benchmark

You will learn:

• What Adaptive Warehouses actually are (and what they're not)
• The billing mechanics that will catch you off guard
• How to decide if converting your existing warehouses makes sense
• Live Q&A: Bring your Snowflake and Adaptive Warehouse questions

Register here: https://pages.select.dev/what-is-adaptive-warehouse?utm_source=reddit&utm_medium=organic&utm_campaign=workshop26