SQL Challenge: Top 3 Customers by Total Spent

SQL Execution Order (not how we write it, but how it actually runs) Most of us write queries like this: SELECT → FROM → WHERE → GROUP BY → ORDER BY But internally, SQL processes it very differently. SQL executes in this order: FROM JOIN WHERE GROUP BY HAVING SELECT DISTINCT ORDER BY LIMIT Here’s a simpler way to think about it FILTER → SHOW → SORT → LIMIT What this actually means • FILTER → FROM, JOIN, WHERE, GROUP BY, HAVING (Define data + reduce it step by step) • SHOW → SELECT, DISTINCT (Choose what you want to display) • SORT → ORDER BY (Organize the result) • LIMIT → LIMIT / TOP (Control how much data you return) Once we start thinking in execution order, we stop “trial and error” and start writing SQL with confidence. If you’re working with SQL daily, this mental model makes a huge difference. #SQL #DataAnalytics #LearnSQL #SQLTips #DataEngineering #Analytics

Most beginners learn SQL… But still struggle to use it in real analysis. I was doing the same. Then I realized — SQL is not about syntax, it’s about thinking. Here’s what actually matters: • Understanding how tables are connected • Knowing what question you want to answer • Using joins to combine meaningful data For example: If you have: Customers table + Orders table You don’t just query them separately. You JOIN them to understand: → Who bought what → How often they purchase → Which customers bring more revenue That’s when SQL becomes powerful. Not just queries… But insights. This shift changed how I approach data analysis. Are you focusing more on syntax or understanding? #SQL #DataAnalytics #LearningInPublic #BusinessAnalytics

🚀 SQL Problem-Solving in Action! #Average_Selling_Price Just solved an interesting query challenge that tested both logic and precision. The task: Calculate the average selling price per product, ensuring correct handling of date ranges, units sold, and cases where no units exist. Approach: Joined the Prices table with UnitsSold using product_id and matching purchase dates within the price validity range. Used SUM(price * units) / SUM(units) to compute the weighted average price. Applied ROUND(..., 2) for two-decimal precision. Wrapped with IFNULL(..., 0) to handle products with no sales gracefully. Grouped results by product_id for clarity. 👉 What I love about this solution is how it combines aggregation, conditional handling, and date logic into one clean query. It’s a great reminder that SQL isn’t just about pulling data—it’s about telling the right story with the data. #Sql #Leetcode #DataAnalysis

🚀 #30DaysOfSQL – Day 20 Continuing my 30 Days of SQL Challenge to strengthen my SQL and data analysis skills. 📌 Topic for Day 20: INDEX – Improving Query Performance Today I learned how indexes help speed up data retrieval in SQL. Indexes work like a table of contents, allowing the database to find data faster instead of scanning the entire table. 💡 Practice Example: Create an index on the salary column to improve search performance. 🧠 SQL Query: CREATE INDEX idx_salary ON employees(salary); 📊 Key Learnings: • INDEX improves query performance • Especially useful for large datasets • Too many indexes can slow down INSERT/UPDATE operations • Best used on columns frequently used in WHERE, JOIN, ORDER BY Understanding indexes is important for writing efficient and optimized SQL queries. Excited to continue this journey with Day 21! #SQL #30DaysOfSQL #DataScience #LearningInPublic #SQLPractice #DataAnalytics

🧠 SQL Challenge 4/100 (IN 🆚 EXISTS) 🚀 🚀 Find customers who never placed any order 👉 Return: customer_id, name ⚠️ Catch There is a NULL in Orders.customer_id A very common approach will return 0 rows 😶 🔥 Caption This looks like a basic question… but one NULL breaks most solutions. If your query uses NOT IN, double check it 👀 Do you know the correct way? Drop your answer 👇 #SQL #DataEngineering #LearnSQL #Analytics #TechCareers

Day 28 SQL Learning Journey I explored the INSERT INTO SELECT statement in SQL and it’s a powerful way to move data between tables efficiently. In simple terms, it allows you to copy data from one existing table and insert it into another existing table, all within a single query. How it works: You select data from a source table Then insert it directly into a target table Key things to remember: The column structure must match (same number of columns and compatible data types) It adds new rows to the target table without affecting the existing data Why this is useful: 1. Backing up specific records 2. Migrating data between tables 3. Saving time when working with large datasets #SQL #Data Analyics #NightStudy #LearningJourney #DataSkills #TechGrowth

💡 SQL Quick Tip! Need to find the TOP performing records per category? Here's a classic pattern using ROW_NUMBER(): 📊 PARTITION BY product_category 📊 ORDER BY sales DESC This gives you the TOP 3 products in EACH category. Which SQL technique would you like to see next? #SQL #DataAnalytics #QueryTips #Analytics #Learning #TechSkills

🚀 7-Day SQL Challenge – Day 3 (Leveling Up 📊) Today’s focus was on making data more meaningful using sorting, aggregation, and grouping. 🔹 Sorting Results (ORDER BY) Learned how to arrange data in ascending and descending order to quickly identify top or bottom values. 🔹 Aggregate Functions Worked with powerful functions like: ✔️ COUNT() – total records ✔️ SUM() – total value ✔️ AVG() – average ✔️ MAX() / MIN() – highest & lowest These functions help summarize large datasets into simple insights. 🔹 GROUP BY Clause Used to group data based on a column (like department-wise analysis). Makes it easier to analyze patterns across categories. 🔹 HAVING Clause Filters grouped data (unlike WHERE which filters rows). Very useful when working with aggregated results. 💡 Key Learning: Understanding how SQL processes queries step-by-step makes writing efficient queries much easier. 📌 SQL Execution Order: FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY Day 3 done ✅ Feeling more confident with data analysis using SQL! #SQL #DataAnalytics #LearningJourney #7DayChallenge #DataAnalyst #SQLBasics

📊 Day 49/90 — SQL Learning: Grouping Data (GROUP BY) Today I learned how to summarize data using: 👉 GROUP BY clause This is where SQL starts becoming really powerful 🔥 Here’s what I practiced: ✅ Grouping data based on a column ✅ Using "COUNT()" to count records ✅ Using "SUM()" to calculate totals ✅ Using "AVG()" for averages ✅ Combining "GROUP BY" with "WHERE" Example: 👉 Total sales by region 👉 Number of customers per city 💡 Big lesson: Raw data gives information. Grouped data gives insights. Because: Individual rows → Hard to understand 😵💫 Grouped data → Clear patterns 📊 From today, I’ll focus on extracting insights using GROUP BY. 💬 What do you use most: COUNT, SUM, or AVG? #SQL #DataAnalytics #LearningInPublic #DataAnalystJourney #90DaysChallenge

✅ Solved a SQL problem on StrataScratch — Day 54 of my SQL Journey 💪 User activity looks simple… until you try to measure it correctly ⏱️ Today’s problem was about calculating average session time — But sessions weren’t explicitly given. They had to be built from events. The approach: • Identified session boundaries using page_load and page_exit • Used MIN() and MAX() with CASE WHEN to capture valid timestamps • Calculated session duration using TIMESTAMPDIFF() • Filtered out invalid sessions (where load happens after exit) • Averaged session time per user What I practised: • Event-based session reconstruction • Conditional aggregation using CASE WHEN • Time difference calculations in SQL • Data cleaning before aggregation What stood out — Metrics don’t exist in raw data. You have to build them. A “session” isn’t stored anywhere… It’s something you define from behaviour. That’s where analysis actually begins. Consistent learning, one query at a time 🚀 #SQL #StrataScratch #DataAnalytics #LearningInPublic #SQLPractice