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Most window functions computed in SQL are wrong the moment a user touches your report. Here’s what I mean: When you write RANK() OVER() in SQL, that ranking is computed once β€” against the full, unfil
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Most SQL content teaches this β€œexecution order”: FROM β†’ WHERE β†’ GROUP BY β†’ HAVING β†’ SELECT β†’ ORDER BY But the truth is, it is not that simple even though it is helpful. Take this simple query filteri
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Competitive Fortnite and database optimization have more in common than you’d think. At the highest level, both are about: β€’ Making fast decisions β€’ Managing limited resources β€’ Adapting under pressu
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A lot of pandas code becomes difficult to maintain because transformations are scattered everywhere. One column here. Another column 15 lines later. Then a third column depending on both of them. .a
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Raw data usually doesn’t tell you much. Aggregation is what turns thousands of rows into something understandable. In pandas, functions like: β€’ sum() β€’ mean() β€’ count() β€’ nunique() β€’ groupby() help
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List comprehensions look confusing… until you realize they follow one simple pattern: what β†’ for β†’ in β†’ if Once that clicks, they become one of Python’s most readable features. #python #datascienc
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Pandas code can become hard to read when transformations are scattered everywhere. Method chaining + .pipe() help your code flow top-to-bottom like a pipeline. Cleaner code. Better analysis. #pytho
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Many pandas nested data problems come down to one issue: One row contains multiple values. At first, that structure looks perfectly fine. But the moment you try to: β€’ aggregate β€’ group β€’ filter β€’ jo
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Most SQL queries don’t become difficult because the logic is advanced. They become difficult because the structure collapses. Common Table Expressions (CTEs) help break SQL into logical steps that a
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One-to-many relationships are one of the easiest places to make SQL mistakes. One ACCOUNT can have many OPPORTUNITY records. So when you join them, the ACCOUNT row can repeat once for every matching
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Most data models don’t fail because they’re wrong. They fail because they don’t scale. At first, everything works. Then requirements grow… and everything gets harder. More complex queries Slower das
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Everyone says β€œfix your data.” But what does good data modeling actually look likeβ€”especially for AI? It’s not about making data more complex. It’s about making it more clear, consistent, and usable
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About @datawithchris

Chris Perry is a verified Instagram growing creator in the Science, Technology & Engineering category. With 104 followers and 12 posts, datawithchris's account shows a follower-to-following ratio of 2.4:1. View all content anonymously on Pictame - no login required.

datawithchris's engagement rate sits at 5.45%. This is right in line with the 5% average for accounts with 1K-10K followers β€” a healthy, steady performance.

Out of the last 12 posts, 0% are videos and 100% are photos. Content shows up about every 5 days. Captions lean detailed, averaging 673 characters.

5.45% engagement rate β€” right on track for 1K-10K follower accounts.
Posts about every 5 days.
2.4:1 follower-to-following ratio.

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@datawithchris

Chris Perry

PublicScience, Technology & Engineering
Posts
12
Top Hashtags
#sql#dataanalytics#python#datascience#pandas#dataengineering#businessintelligence#dataanalyst

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Senior AI Engineering Manager Lecturer at UT Dallas Creator of SQLShortReads.com Tableau Ambassador I teach: πŸ‘¨β€πŸ’» Datab...

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