SQL DELETE Statement: Removing Data Safely

📊 Day 56/90 — SQL Learning: INNER JOIN (Deep Dive) Today I explored the most commonly used JOIN: 👉 INNER JOIN This is where SQL starts feeling like real-world problem solving 🔥 Here’s what I learned: ✅ INNER JOIN returns only matching records ✅ It connects tables based on a common column (key) ✅ Non-matching data is excluded ✅ Most used JOIN in real-world queries Example: 👉 Get customers who placed orders 👉 Combine customer + order details 💡 Big lesson: INNER JOIN focuses on common data between tables Because: No match → Ignored ❌ Match → Included ✅ Example Query: SELECT c.name, o.amount FROM customers c INNER JOIN orders o ON c.id = o.customer_id; From today, I’m understanding how real datasets are connected 🔗 💬 Where do you use INNER JOIN in real projects? #SQL #DataAnalytics #LearningInPublic #DataAnalystJourney #90DaysChallenge

📊 Day 51/90 — SQL Learning: Filtering Groups (HAVING Clause) After learning GROUP BY, today I explored: 👉 HAVING clause At first, I was confused between "WHERE" and "HAVING" 🤔 But now it makes sense. Here’s what I learned: ✅ "WHERE" → filters rows before grouping ✅ "HAVING" → filters after grouping ✅ Using "HAVING" with "COUNT()", "SUM()", "AVG()" ✅ Combining "GROUP BY + HAVING" Example: 👉 Find regions where total sales > 1000 👉 Find cities with more than 5 customers 💡 Big lesson: WHERE filters data. HAVING filters insights. Because: Rows → Data Groups → Insights 📊 From today, I’m focusing on writing smarter queries using HAVING. 💬 What confused you more: WHERE or HAVING? #SQL #DataAnalytics #LearningInPublic #DataAnalystJourney #90DaysChallenge

Day 4 of learning SQL 🚀 Today I focused on making my queries more powerful and readable: ✔ LIMIT – controlling how many rows to return ✔ ORDER BY + LIMIT – finding top/bottom results ✔ OFFSET – skipping rows (useful for ranking like 2nd highest) ✔ Aliasing (AS) – making column names cleaner and more readable Example I practiced: SELECT department, AVG(salary) AS avg_salary FROM employees GROUP BY department ORDER BY avg_salary DESC LIMIT 1; Key learning today 💡 LIMIT is essential for real-world analysis (Top N queries) OFFSET helps in ranking (like finding 2nd or 3rd highest values) Aliasing makes queries more readable and professional Mistakes I made: Forgot GROUP BY while using AVG() ❌ Used wrong LIMIT with OFFSET ❌ Fixing these helped me understand query flow better. Goal: Become job-ready in SQL & Data Analysis 💪 #SQL #DataAnalytics #LearningInPublic #100DaysOfCode #BeginnerToPro #Alextheanalyst

🔄 SQL is not just a skill — it’s a system. Most beginners learn SQL in parts… But professionals understand how everything connects. Think of SQL like a circle: 🔹 WHERE — Filter the right data 🔹 JOINS — Connect multiple tables 🔹 FUNCTIONS — Analyze with logic (SUM, AVG, COUNT) 🔹 GROUP BY — Summarize insights 🔹 ORDER BY — Present results clearly 🔹 ALIAS — Make queries clean and readable 💡 Writing queries is easy. Understanding the flow is what makes you powerful. Because in real-world projects, you don’t use these commands separately — you use them together. 🎯 Learn the syntax. 🎯 Understand the flow. 🎯 Master the system. That’s how you go from writing queries… to solving real data problems. #SQL #DataAnalytics #DataScience #Database #Learning #TechSkills #BusinessIntelligence #Analytics #CareerGrowth

📊 Day 59/90 — SQL Learning: RIGHT JOIN Today I learned the opposite of LEFT JOIN: 👉 RIGHT JOIN It works similarly… but from the other side 👇 Here’s what I learned: ✅ Returns all records from right table ✅ Includes matching records from left table ✅ Non-matching data from left table → NULL ✅ Useful when right table is more important Example: 👉 Get all orders + customer details 👉 Find orders without customer info 💡 Big lesson: RIGHT JOIN focuses on right table completeness Because: Left match missing → NULL ❌ Right data always included ✅ Example Query: SELECT c.name, o.amount FROM customers c RIGHT JOIN orders o ON c.id = o.customer_id; From today, I understand how to analyze data from both sides of tables 🔄 💬 Do you use LEFT JOIN more or RIGHT JOIN? #SQL #DataAnalytics #LearningInPublic #DataAnalystJourney #90DaysChallenge

SQL isn’t just about writing queries… it’s about understanding how data really works. From SELECT, JOINs, and WHERE → to functions, keys, and performance — everything connects like layers of a cake 🍰 The more you explore, the more powerful your queries become. Still learning, still querying 🚀 #SQL #DataAnalytics #Learning #Database

One of the biggest myths in data? "SQL is easy." Sure, writing a simple SELECT * is easy. But moving from "functional" SQL to "masterful" SQL is where the real challenge (and the fun) begins. Lately, I’ve been diving deeper into the nuances that separate a basic query from a high-performing one: 🔹 CTEs over Subqueries: For better readability and easier debugging. 🔹 Window Functions: To perform complex calculations without messy self- joints. 🔹 Query Optimization: Because a query that works isn’t always a query that’s efficient. Every time I think I’ve mastered a concept, I find a more elegant way to pull a dataset or a faster way to join tables. That’s the beauty of working with data; there is always a "level up" waiting for you. For the SQL pros in my network: What was the one function or concept that completely changed the way you approach a database? #SQL #DataAnalytics #ContinuousLearning 

🚨 𝗬𝗼𝘂𝗿 𝗦𝗤𝗟 𝗾𝘂𝗲𝗿𝘆 𝗶𝘀 𝗡𝗢𝗧 𝗲𝘅𝗲𝗰𝘂𝘁𝗲𝗱 𝘁𝗼𝗽 𝘁𝗼 𝗯𝗼𝘁𝘁𝗼𝗺. And this is exactly why many queries break. Most people write SQL like this: 𝘚𝘌𝘓𝘌𝘊𝘛 → 𝘍𝘙𝘖𝘔 → 𝘞𝘏𝘌𝘙𝘌 → 𝘎𝘙𝘖𝘜𝘗 𝘉𝘠 But that’s not how SQL runs. Here’s the actual execution order 👇 1️⃣ 𝗙𝗥𝗢𝗠 2️⃣ 𝗪𝗛𝗘𝗥𝗘 3️⃣ 𝗚𝗥𝗢𝗨𝗣 𝗕𝗬 4️⃣ 𝗛𝗔𝗩𝗜𝗡𝗚 5️⃣ 𝗦𝗘𝗟𝗘𝗖𝗧 6️⃣ 𝗢𝗥𝗗𝗘𝗥 𝗕𝗬 7️⃣ 𝗟𝗜𝗠𝗜𝗧 Now the important part: 👉 𝗦𝗘𝗟𝗘𝗖𝗧 𝗿𝘂𝗻𝘀 𝗮𝗹𝗺𝗼𝘀𝘁 𝗮𝘁 𝘁𝗵𝗲 𝗲𝗻𝗱. Which means: • You can’t use column aliases in WHERE • Aggregations don’t exist before GROUP BY • HAVING works on grouped data, not raw rows That’s why beginners get errors like: ❌ “column not found” ❌ “invalid aggregation” Here’s the truth: 𝗦𝗤𝗟 𝗶𝘀 𝘄𝗿𝗶𝘁𝘁𝗲𝗻 𝘁𝗼𝗽-𝗱𝗼𝘄𝗻. 𝗕𝘂𝘁 𝗲𝘅𝗲𝗰𝘂𝘁𝗲𝗱 𝗯𝗼𝘁𝘁𝗼𝗺-𝘂𝗽. Once you understand this, debugging SQL becomes 10x easier. How long did it take you to realize this? 😄👇 #SQL #DataEngineering #Analytics #Database #ETL

Day 5 of my SQL learning journey has been productive. Today, I focused on sorting data using ORDER BY. Here are the key points I covered: - Sorted data in ascending and descending order - Learned multi-column sorting - Improved data readability A key takeaway from today is that well-organized data leads to better insights. I am steadily progressing in my SQL skills. #SQL #LearningJourney #Day5 #DataAnalytics #TechSkills

📊 SQL Learning Journey Today, I learned a new concept in SQL: HAVING clause So far, I’ve covered: ✔️ SELECT & filtering using WHERE (AND / OR, LIKE) ✔️ ORDER BY for sorting ✔️ GROUP BY for grouping data ✔️ INNER JOIN for combining tables 🔍 Today’s focus: HAVING At first, it felt a bit confusing, but here’s the simple way I understood it: 👉 WHERE filters rows 👉 HAVING filters grouped data 💡 Example: If I group users by plan, HAVING helps me filter only those plans where users are more than a certain number. This small concept made a big difference in understanding how to analyze grouped data more effectively. Step by step, getting more comfortable with SQL. 📈 #SQL #DataAnalytics #LearningJourney #SQLPractice