SQL Subqueries Explained: Filtering, Comparing, and Calculating Data

Day 30 of mastering SQL 📘Views in SQL 🔍 What is a View? A View in SQL is a virtual table created from a query. It does not store data itself — it shows data from one or more tables. 👉 Think of it like a saved query that you can reuse anytime. 🧠 Why Use Views? ✔ Simplifies complex queries ✔ Enhances security (hide sensitive columns) ✔ Reusability (no need to write same query again) ✔ Cleaner and organized code 🧾 Syntax CREATE VIEW view_name AS SELECT column1, column2 FROM table_name WHERE condition; 💡 Example CREATE VIEW high_salary_employees AS SELECT name, salary FROM employees WHERE salary > 50000; 👉 Now you can use: SELECT * FROM high_salary_employees; 🔄 Update View CREATE OR REPLACE VIEW view_name AS SELECT ... ❌ Drop View DROP VIEW view_name; ⚠️ Important Points View does not store data Always shows latest data from table Some views are not updatable (depends on query) #SQL #Database #techskills

🚀 SQL Journey – Day 28 Today, I explored a deeper concept in SQL: Correlated vs Non-Correlated Subqueries 🔍 Understanding how subqueries behave based on dependency really changed how I look at query performance and design. 💡 What I learned: 🔹 Non-Correlated Subqueries (Independent) 👉 Execute only once 👉 Do not depend on the outer query 👉 Faster and useful for overall comparisons & aggregations 🔹 Correlated Subqueries (Dependent) 👉 Execute once for each row of the outer query 👉 Depend on values from the outer query 👉 Useful for row-by-row comparisons but can be slower 📌 Key Takeaways: ✔ Choosing the right type of subquery impacts performance ✔ Correlated subqueries are powerful but should be used carefully ✔ Non-correlated subqueries are efficient for global conditions 📊 This session helped me understand how SQL works behind the scenes and how to apply subqueries in real-world scenarios. 📈 Step by step, improving my SQL skills every day! #SQL #SQLLearning #Subqueries #CorrelatedSubqueries #NonCorrelatedSubqueries #DataAnalytics #DataAnalyst #DatabaseManagement #LearningJourney

🚀 Day 32/100 — SQL Subqueries: Thinking Inside Queries 🧠💻 Today I learned Subqueries, a powerful concept in SQL used to solve complex problems step by step. 📊 What is a Subquery? 👉 A query inside another query ➡️ Used to break down complex problems into simpler parts 📌 What I explored today: 🔹 Subqueries in SELECT 🔹 Subqueries in WHERE 🔹 Subqueries in FROM 🔹 Nested queries for filtering 💻 Example Scenario: 👉 Find customers who made orders above the average order value 📌 Example Query: SELECT customer_id, order_amount FROM orders WHERE order_amount > ( SELECT AVG(order_amount) FROM orders ); 📊 How it works: 👉 Inner query → calculates average 👉 Outer query → filters higher-than-average orders 🔥 Key Learnings: 💡 Subqueries help solve complex business questions 💡 Makes SQL more flexible and powerful 💡 Commonly asked in interviews 🚀 Real-world use cases: ✔ Filtering based on averages ✔ Comparing values within datasets ✔ Dynamic data selection 🔥 Pro Tip: 👉 Use subqueries when: You need step-by-step filtering OR when JOINs become complex 📊 Tools Used: SQL | MySQL ✅ Day 32 complete. 👉 Quick question: Do you prefer solving problems using JOINs or Subqueries? 🤔 #Day32 #100DaysOfData #SQL #Subqueries #DataAnalytics #LearningInPublic #CareerGrowth #JobReady #InterviewPrep

🚀 SQL Journey – Day 27 Today’s focus: Subqueries in SQL 🔍 Subqueries are powerful tools that let you write queries inside another query to solve complex problems step by step. 💡 What I learned today: 🔹 Scalar Subquery Returns a single value 👉 Useful for comparisons like finding max/min values 🔹 Row Subquery Returns a single row with multiple columns 👉 Helps compare multiple column values at once 🔹 Table Subquery Returns multiple rows/columns 👉 Commonly used with IN, EXISTS, and joins 📌 Key Takeaway: Subqueries make SQL more dynamic and flexible, allowing you to break down complex queries into smaller, manageable parts. 📈 Every day, I’m getting closer to mastering SQL step by step! #SQL #LearningJourney #DataAnalytics #Subqueries #SQLPractice #FutureDataAnalyst #Day27

🧠 Master SQL, one query at a time I explored a guide with 100 essential SQL queries—from basics like "SELECT" & "WHERE" to advanced concepts like joins, subqueries, and window functions. 💡 Strong SQL isn’t about memorizing queries—it’s about understanding how data connects. What’s your most-used SQL query? 👇 #SQL #DataAnalytics #InterviewPrep #DataScience

🚀 Day 20/30 – SQL Challenge: Correlated Subqueries Definition: A correlated subquery is a subquery that depends on the outer query and executes once for each row. Helps in row-by-row comparison and dynamic filtering. 🔍 Unlike normal subqueries, it re-evaluates for every record. 💡 Example Use Case: Compare each employee’s salary with the average salary of their department 📌 When to Use: Complex filtering where JOINs are not enough When comparing individual rows with grouped data Finding top or bottom values within categories 📈 Mastering correlated subqueries takes your SQL skills to the next level of precision and control. #SQL #DataAnalytics #SQLChallenge #30DaysOfSQL #CorrelatedSubquery #LearnSQL #DataDriven #CareerGrowth

🚀 SQL Basics – Day 5: Subqueries Made Simple Now let’s move one step ahead and understand how to use a query inside another query 🤯 Sounds complex? Don’t worry—it’s actually simple! 👇 🔍 What is a Subquery? 👉 A query written inside another query 🧠 “Query ke andar query” --- 📌 Subquery in WHERE 👉 Used to filter data based on another query 💡 "SELECT name FROM employees WHERE salary > (SELECT AVG(salary) FROM employees);" 🧠 Employees earning more than average salary --- 📊 Subquery in FROM 👉 Used as a temporary table 💡 "SELECT dept, AVG(salary) FROM (SELECT * FROM employees) AS temp GROUP BY dept;" --- 🎯 Subquery in SELECT 👉 Used to show extra calculated data 💡 "SELECT name, (SELECT AVG(salary) FROM employees) AS avg_salary FROM employees;" --- 😄 Easy way to remember: Subquery = Query inside query WHERE = Filter with subquery FROM = Use as table SELECT = Add extra info --- ✨ Conclusion: Subqueries help you solve complex problems step by step 💪 Once you understand this, your SQL level becomes advanced 🚀 📌 Practice different types of subqueries to gain confidence! #SQL #DataAnalytics #SQLBasics #Subquery #LearningSQL #Day5 #DataAnalystii

📅 SQL Date & Time Functions (Simple Explanation) Working with dates and time is very common in SQL. These functions help you get, format, and calculate date values easily. 👉 1. GETDATE() Returns the current date and time Example: SELECT GETDATE() 👉 2. CURRENT_TIMESTAMP Also gives current date and time (same as GETDATE) 👉 3. GETUTCDATE() Returns current UTC date and time (global time) 👉 4. DATEADD() Adds or subtracts time from a date Example: Add 5 days → DATEADD(DAY, 5, GETDATE()) 👉 5. DATEDIFF() Finds difference between two dates Example: DATEDIFF(DAY, '2024-01-01', '2024-01-10') → 9 days 👉 6. DATENAME() Returns name of date part (like month or day) Example: DATENAME(MONTH, GETDATE()) → April 👉 7. DATEPART() Returns numeric value of date part Example: DATEPART(YEAR, GETDATE()) → 2026 👉 8. FORMAT() Formats date in different styles Example: FORMAT(GETDATE(), 'dd-MM-yyyy') 👉 9. ISDATE() Checks if value is a valid date Example: ISDATE('2026-04-27') → 1 (Valid) --- 💡 Why these are important? Used in reports 📊 Helps filter data by date 📅 Useful in real-time applications ⏱️ --- #SQL #DataAnalytics #SQLServer #Learning #TechBasics #Database #ITSkills

Window functions are one of the most useful SQL skills you can learn! I just uploaded a full breakdown of SQL Window Functions using healthcare data examples, and the goal was simple: make this topic feel clear, practical, and actually useful. Window functions are one of the most valuable SQL skills you can learn because they help you do deeper analysis **without losing row level detail**. That means you can rank records, compare values within groups, build running totals, and analyze trends in a much smarter way. In this video, here’s what to expect: * A simple explanation of what window functions are * A clear breakdown of `OVER()`, `PARTITION BY`, and `ORDER BY` * Easy examples of `ROW_NUMBER()`, `RANK()`, and `DENSE_RANK()` * How to calculate averages and running totals without collapsing your data * How `LAG()` and `LEAD()` help you compare rows * How functions like `NTILE()`, `FIRST_VALUE()`, `LAST_VALUE()`, and `PERCENT_RANK()` work * Real healthcare style examples to make the concepts easier to understand Why are window functions worth learning? * They come up in real analytics work all the time * They make your SQL much stronger and more advanced * They are great for interviews * They help you think like a real analyst, not just someone writing basic queries * Once you understand them, a lot of advanced SQL starts to feel much easier If you're learning SQL and want to get more confident with intermediate concepts, this is definitely a video worth watching. #sql #mysql #sqltutorial #windowfunctions #dataanalytics #learnsql #healthcareanalytics #healthinformatics #healthtech #analytics #dataskills https://lnkd.in/g4NwuTKg

This weekend, I’ll be revisiting one SQL topic that can be a little confusing at first but is very important to understand: JOINS In simple terms, “joins” help us combine data from different tables so we can get more meaningful insights. The common types I’m revising are: INNER JOIN – returns only the matching records from both tables LEFT JOIN – returns all records from the left table and the matching ones from the right RIGHT JOIN – returns all records from the right table and the matching ones from the left FULL JOIN – returns all matching and non-matching records from both tables CROSS JOIN – returns every possible combination of rows from both tables One thing I’m learning is that understanding joins is not just about memorising definitions. It’s about knowing when to use each one and what kind of result you want from your data. So this weekend is for more revision, more practice, and more clarity - one query at a time🤗 Which SQL concept are you currently revising or trying to understand better? #SQL #DataAnalytics #DataAnalysis #Omolabakethedataanalyst