SQL UPDATE Statement: Modify Existing Data

I wrote a SQL query to filter high-revenue countries… and it failed. The logic looked correct. But SQL threw an error. Here’s what I tried: 👉 Filtering total revenue using WHERE Something like: WHERE SUM(order_total) > 10000 And SQL didn’t accept it. That’s when I realized: 👉 I was filtering at the wrong stage of the query. In SQL, execution doesn’t happen the way we read the query. It actually works like this: FROM WHERE GROUP BY HAVING SELECT ORDER BY 💥 The mistake: WHERE runs before aggregation So it can’t use functions like SUM(), COUNT(), etc. ✅ The fix: Use HAVING for aggregated conditions: 👉 HAVING SUM(order_total) > 10000 💡 What I learned: WHERE filters rows HAVING filters grouped results Sounds simple… but easy to mess up in real queries. Now I think of it like this: 👉 WHERE → “filter raw data” 👉 HAVING → “filter summarized data” 📌 Lesson: If your query involves aggregation and filtering… Always ask: 👉 Am I filtering before grouping or after? This small distinction can save you from a lot of confusion. #SQL #DataEngineering #SQLTips #Analytics #LearnSQL #DataAnalytics #QueryOptimization #TechLearning #Debugging

DAY 46 — Lessons Learned Using SQL in Real Projects After working with SQL across multiple datasets, here are key lessons: 1️⃣ Always start with the business question 2️⃣ Keep queries simple and readable 3️⃣ Validate results at every step 4️⃣ Understand your joins deeply 5️⃣ Document your logic SQL is not just a technical skill. It is a thinking process. The goal is not to write complex queries. It is to produce reliable, meaningful insights. #ShinaAwopejuBusinessAnalysisJourneyWith10alytics #BusinessAnalysisWith10alytics #DataAnalysis #SQL

 Building My SQL Foundation I’ve been focusing on understanding the basics of SQL, and today’s practice helped me get more comfortable with: ✅ SELECT – retrieving specific data ✅ WHERE – filtering records based on conditions ✅ ORDER BY – sorting data for better insights 💡 What I learned: Even simple queries can turn raw data into meaningful information when used correctly. Practicing with real examples is helping me understand how databases work in real-world scenarios. Consistency is key — learning something new every day! 💪 Looking forward to exploring more advanced concepts soon 🚀 #SQL #DataAnalytics #LearningJourney #BeginnerToPro #SkillBuilding #Consistency #FutureDataAnalyst

📊 SQL Revision Day – Strengthening the Foundations Today, I focused on revising core SQL concepts by working on a dummy cellular company dataset. 🔍 Concepts I practiced: ✔️ SELECT & data filtering using WHERE (AND / OR, LIKE) ✔️ Sorting data using ORDER BY ✔️ Aggregating insights using GROUP BY ✔️ Combining multiple tables using INNER JOIN 💡 What stood out: Understanding how data connects across tables using JOINs really helps in seeing the bigger picture — especially when analyzing customers, plans, and subscriptions together. 📈 This hands-on revision is helping me move beyond theory and build practical confidence in SQL. #SQL #DataAnalytics #LearningJourney #DataSkills #BusinessAnalysis

Must-know SQL queries for Data Analysts. Revisiting the fundamentals — because strong basics make better analysts. Here’s a quick cheat sheet covering: • Filtering • Joins • Aggregations • Window functions • CTEs Simple. Practical. Useful #SQL #DataAnalytics #DataAnalyst #LearnSQL #Analytics #TechCareers #DataScience

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 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

🚀Day 87 of My 100 Days Data Analysis Journey This is what SQL looks like when everything finally connects. Not scattered commands. Not random syntax. But a clear system that controls how data is filtered, grouped, combined, and understood. At a glance, this breaks SQL into its core building blocks: WHERE, defines what matters GROUP BY & HAVING, turns raw data into meaningful segments ORDER BY, brings structure and clarity to results JOINS, connects multiple tables into one complete view FUNCTIONS, summarize data into insights ALIAS (AS), improves readability and interpretation Then comes precision: LIKE, IN, BETWEEN, EXISTS AND, OR, NOT Each one is small on its own. Together, they form a system that answers complex questions. The real shift happens here: SQL stops being something to memorize and becomes something to think with. That is where real analysis begins. #DataAnalytics #SQL #LearningInPublic #100DaysOfCode #DataSkills #TechJourney

✅ 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

Day 4/30 of SQL Challenge Today I learned: -> LIMIT Key idea: LIMIT is used to control how many rows are returned in a query result. Example: SELECT * FROM products LIMIT 5; What I understood: When working with large datasets, we don’t always need all the data. LIMIT helps us quickly preview or focus on a smaller portion. Small insight: LIMIT is often used with ORDER BY to get top or bottom results. Example: SELECT name, price FROM products ORDER BY price DESC LIMIT 3; This returns the top 3 most expensive products. Practice thought: What if I want to skip some rows and then get results? Example: SELECT * FROM products LIMIT 5 OFFSET 5; This skips the first 5 rows and returns the next 5. Note: OFFSET is used to skip some data. In another day we just learn the OFFSET. #SQL #LearningInPublic #Data #BackendDevelopment #DataEngineer #DataAnalyst