LeetCode 1321. Restaurant Growth | SQL Rolling Window Average Explained #leetcodesql #LeetCode

LeetCode 1321. Restaurant Growth | SQL Rolling Window Average Explained In this video, we solve LeetCode 1321. Restaurant Growth, a SQL problem that teaches you how to compute a 7-day rolling revenue and average amount spent by customers. Using powerful subqueries and aggregation techniques, we build a complete solution to understand daily restaurant performance over time. This problem is perfect for those who want to strengthen their understanding of date filtering, aggregation, and rolling window logic in SQL. 🔍 Problem Focus: LeetCode 1321. Restaurant Growth The goal of LeetCode 1321. Restaurant Growth is to compute the total amount spent by customers over the past 7 days (including the current day) for each date, and also calculate the average amount spent over those days. The final result excludes the first 6 days to ensure there’s a full 7-day window for each entry. 🧠 SQL Concepts Covered In this video, you’ll learn: How to use subqueries to calculate dynamic date ranges Using DATE_SUB() and DATE_ADD() to work with intervals Filtering dates to include only those with a full 7-day history Calculating rolling sums and averages in a non-window-function SQL environment Applying GROUP BY to maintain date-level granularity GROUP BY visited_on; 🎓 Why This Problem Is Important LeetCode 1321. Restaurant Growth teaches you how to simulate sliding window analysis without using window functions like LAG() or OVER(). This is useful when working in SQL environments that don’t support advanced functions (like some versions of MySQL). By practicing LeetCode 1321. Restaurant Growth, you improve your ability to: Build real-world analytics queries Understand customer behavior trends Calculate moving averages for dashboards and KPIs Master SQL logic for date-based data 👤 Who Should Watch This video is ideal for: Beginners looking to deepen SQL skills Data analysts and engineers preparing for interviews Professionals working with sales, revenue, or time-series data Anyone wanting to crack LeetCode 1321. Restaurant Growth 🧪 Real-world Use Case This solution mirrors common business use cases like: Calculating weekly sales or active users Monitoring 7-day performance trends Performing rolling analysis on marketing or financial datasets With LeetCode 1321. Restaurant Growth, you gain the confidence to write similar queries in business intelligence tools like Power BI, Tableau, or even Excel (via SQL-based backends). 📌 Final Thoughts In this walkthrough of LeetCode 1321. Restaurant Growth, we tackled the problem using nested subqueries, date filtering, and grouping techniques. This elegant solution does not rely on window functions, making it a must-learn method for broader compatibility across SQL engines. 🔥 Don't forget to: ✅ Like this video if you found it helpful ✅ Subscribe to Insightvanta for more LeetCode, SQL, Power BI & Python tutorials ✅ Share this video with friends and colleagues ✅ Comment below with any questions or your custom solutions! #LeetCode1321RestaurantGrowth #RestaurantGrowth #LeetCodeSQL #SQLInterview #7DayRollingAverage #SQLDateFunctions #MySQLQuery #SQLForDataAnalysis #LeetCodeSQLSolution #SQLRevenueGrowth #DataEngineering #SQLWindowAlternative #Insightvanta #SQLTutorial #LeetCodeChallenge #SQLQueryExplanation #RollingWindowInSQL #LeetCodeProblem1321 #SQLProjectIdeas #SQLRealWorldUseCase #DataAnalyticsSQL #PowerBIReadyData #SQLPractice #InterviewPrepSQL #WeeklyRevenueTracking #SQLTipsAndTricks Follow me on Social Media Instagram - https://www.instagram.com/insightvanta/ Linked In - https://www.linkedin.com/in/yash-singh-04bb361b2/