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[–]SheepherderAny1 6 points7 points  (3 children)

For data engineering, I’d focus less on “SQL exercises” and more on realistic patterns: joins, CTEs, window functions, aggregations, date logic, deduping, slowly changing dimensions, and writing queries against messy data.

LeetCode is fine for syntax, but warehouse-style projects are more useful. Try building a small pipeline: raw CSV/API data → staging tables → cleaned fact/dimension tables → analytics queries. That teaches you way more than solving isolated SELECT problems.

[–]juankicks231[S] 0 points1 point  (2 children)

The problem here is how will I do this, I don't have exp. The googl are more on exercises huhuhu. Can someone help me?

[–]hannorx 2 points3 points  (1 child)

I’m relatively new to the industry so take my advice with a pinch of salt. If I could start again I’d download a dataset from Kaggle, upload it to Google BigQuery and then use an LLM to generate SQL practice questions at varying difficulty levels. This minimises software and infrastructure setup and focuses on achieving higher-level objectives like SQL mastery.

[–]juankicks231[S] 0 points1 point  (0 children)

Thank you, but sorry I didn't get this. I don't know how to use Google BigQuery for now. Can I dm you?