SQL-Engineering-Handbook
🗃️ Datasets
Part of the Resources Library — SQL Engineering Handbook
For practice beyond this repo’s own datasets/ folder (employee_management, ecommerce, sales, finance, healthcare, nagpurlens). Once the Handbook’s own schema feels comfortable, importing one of these into a local database is the natural next step — real data is messier than curated exercise data on purpose.
| Dataset Source | What It Is | Best For | Free / Paid |
|---|---|---|---|
| Kaggle Datasets | The largest general-purpose public dataset hub, spanning every domain | Broad selection with community notebooks showing how others approached the same data | Free |
| data.gov | The U.S. government’s open data portal | Large, realistic government/civic datasets — good for practicing messy real-world joins | Free |
| UCI Machine Learning Repository | Long-standing academic dataset repository, originally built for ML research | Clean, well-documented classic datasets with clear schemas | Free |
| NYC Open Data | New York City’s open government data portal | City-scale operational data (transit, permits, 311 calls) — excellent for GROUP BY and date-function practice |
Free |
| data.gov.in | India’s open government data portal | Practicing SQL against Indian civic/government datasets, directly relevant if projects like nagpurlens are on your mind |
Free |
[!TIP] Pick a dataset with at least three related tables before importing it — single flat CSVs don’t exercise joins, which is most of what real SQL work actually is.
Next: playgrounds.md. Back to Resources Library.