After dedicating past days using Claude 3.7 Sonnet for modern data engineering tasks, I've found it to be the most effective model for general programming and data engineering workflows. 

- While Claude 3.7 Sonnet doesn't inherently follow all the latest data engineering documentation, it demonstrates impressive learning capability. Providing hints about new APIs or functions results in the model making concerted efforts to deliver optimal solutions.
- I was particularly impressed by its ability to convert Python DataFrames to SQL code using DuckDB syntax. The success rate exceeded 85% without requiring extensive prompting, outperforming o3-mini-high.

- The model demonstrates solid understanding of modern data engineering orchestrators such as Kestra. It successfully converted my Dagster scripts to Kestra format, though this required more detailed input. 03-mini-high (high) was unable to achieve comparable results.

- Claude 3.7 Sonnet excels with S3 APIs, providing accurate syntax for AWS environment configuration alongside valuable recommendations. It even suggested effective temporary workarounds for common issues.

The primary drawback is Claude 3.7 Sonnet's API limits. I reached these constraints relatively quickly, which proved frustrating and frequently necessitated switching to o3-mini-high as an alternative.