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Practical queries that turn chaotic tables into reliable, analysis-ready data
My Favorite SQL Tricks for Cleaning Messy Real-World Data
7 Patterns to handle NULLs, duplicates, and messy strings before you ever open Python
We have all been there. You get a request for a quick data pull → You write a simple SELECT * query → export the CSV → and load it into a Jupyter Notebook or Pandas.
Then the “quick” task turns into a nightmare.
You spend the next three hours writing .dropna(), .strip(), and complex lambda functions just to get the dates to look like dates and the numbers to act like numbers. You then realize that loading a 10GB dataset into your local RAM just to fix capitalization errors isn't data engineering—it’s a bottleneck.
The truth is, we often forget that database engines were built specifically to filter, clean, and aggregate data. They are almost always faster and more efficient at cleaning data than your local machine because you avoid dragging massive files over the network.
If you clean your data in a local Python script, you are often the only one who benefits. But if you clean it in SQL (by creating a View or a new table), that data becomes clean for…
