Ollayor Sabirov’s Post

If you have ever been confused about SQL JOINS, this visual breakdown will clear everything up. INNER JOIN Only records with matching values in both tables. Think of it as the intersection. LEFT JOIN All records from the left table + matching records from the right table. Left table is complete, right table is partial. LEFT JOIN with NULL Check Only records from the left table that have NO match in the right table. Great for finding orphaned data. RIGHT JOIN All records from the right table + matching records from the left table. Mirror image of LEFT JOIN. RIGHT JOIN with NULL Check Only records from the right table that have NO match in the left table. FULL OUTER JOIN Everything from both tables. Records match when possible, NULL when no match exists. FULL OUTER JOIN with NULL Check Only records that do NOT have a match in either table. Find disconnected data. Pro tip: Most real-world queries use INNER JOIN and LEFT JOIN. The others are less common but powerful when you need them. The mistake I made: I used to write complex WHERE clauses to filter data when a simple JOIN type would do the job. Understanding JOIN types saves you from writing unnecessary logic. Which JOIN type confused you the most when learning SQL? Drop it below! #SQL #Database #BackendDevelopment #Programming #DataEngineering #SoftwareDevelopment

🚀 Understanding SQL Query Execution Order Many developers write SQL queries in the order they appear — but databases don’t execute them that way. Here’s the actual execution flow behind the scenes: 🔹 FROM – Identify the source tables 🔹 JOIN – Combine tables based on conditions 🔹 ON – Apply join conditions 🔹 WHERE – Filter rows before grouping 🔹 GROUP BY – Group rows for aggregation 🔹 HAVING – Filter grouped data 🔹 SELECT – Choose the required columns 🔹 ORDER BY – Sort the result 🔹 LIMIT – Restrict the number of rows returned 💡 Key Insight: Even though we write "SELECT" first, it is executed much later in the process! Understanding this order helps in: ✔ Writing optimized queries ✔ Debugging complex SQL ✔ Improving performance 📌 Master the execution flow, and SQL will start making much more sense. #SQL #Database #BackendDevelopment #DataAnalytics #Programming #Learning

Most SQL beginners write queries like this: SELECT * FROM (  SELECT user_id, SUM(revenue) AS total  FROM orders  GROUP BY 1 ) t WHERE t.total > 1000; It works. But it's hard to read. Here's the same query using a CTE: WITH user_revenue AS (  SELECT user_id, SUM(revenue) AS total  FROM orders  GROUP BY 1 ) SELECT * FROM user_revenue WHERE total > 1000; Same result. Way easier to read. So what IS a CTE? Think of it like giving your subquery a name and moving it to the top. That's it. Why? → Your query reads top to bottom like a story → Each step has a clear, meaningful name → You can chain multiple CTEs together → Debugging becomes much easier Bonus: Recursive CTEs let you walk through hierarchical data — like org charts or folder trees — in pure SQL. If nested subqueries are giving you headaches, try CTEs. You won't go back. #SQL #DataAnalytics #DataEngineering #LearningInPublic

📘 My SQL Learning Journey – Subqueries (IN, ANY, ALL) Today I learned how to work with multi-row subqueries in SQL 👇 🔹 What is a Multi-row Subquery? A subquery that returns more than one value. 👉 In such cases, we cannot use = 👉 We must use: IN, ANY, ALL 🔹 1. IN Operator 👉 Used to check if a value matches any value in a list. 🔹 2. ANY Operator 👉 Used to compare with at least one value. 🔹 3. ALL Operator 👉 Used to compare with all values. 🔹 Key Difference IN → match any value ANY → compare with at least one value ALL → compare with all values 🔹 New Important Insight 💡 When working with multiple tables, we must identify related (connecting) columns, not just similar names.  Example: employees.department_id departments.id Even though names are different, they represent the same relationship. ✔️ Always connect: Foreign key → Primary key Matching data types Logical relationship between tables 🔹 Big Takeaway 💡 SQL is not just syntax it’s about thinking in steps and relationships: 1️⃣ Get the required values 2️⃣ Use correct connecting columns 3️⃣ Apply the condition #SQL #LearningJourney #Subqueries #DataAnalytics

Day 13 of learning SQL 🚀 Today I learned how to use Temporary Tables in MySQL to store and reuse data during a session. This concept helped me understand how to break down complex queries and work with intermediate results more efficiently. Topics I covered: ✔ Creating temporary tables manually ✔ Inserting data into temp tables ✔ Creating temp tables directly from SELECT queries ✔ Using temp tables for filtering and analysis Example I practiced: CREATE TEMPORARY TABLE salary_over_50k AS SELECT * FROM employee_salary WHERE salary >= 50000; Key learning today 💡 Temporary tables exist only during the session They help simplify complex queries Useful when the same filtered data is needed multiple times Make SQL workflows more structured and efficient Step by step, moving towards writing cleaner and more practical SQL queries. Goal: Become job-ready in SQL & Data Analysis 💪 #SQL #DataAnalytics #LearningInPublic #100DaysOfCode #Consistency

Day 25 of My SQL Study Today I learned about the SQL ANY operator, and it’s actually quite interesting. The ANY operator is used with a subquery and returns TRUE if any one of the values from the subquery meets the condition. In simple terms: If at least one value matches, the condition is TRUE. Basic Syntax SELECT column_name FROM table_name WHERE column_name operator ANY (subquery); Example: Imagine you have: A Products table An Orders table You want to find products that are priced higher than at least one ordered product. Query: SELECT product_name FROM Products WHERE price > ANY ( SELECT price FROM Orders ); This will return products whose price is greater than any one of the prices in the Orders table. A simple way to understand it ANY = at least one match is enough #SQL #SQLLearning #DataAnalytics #LearningInPublic #TechJourney #ContinuousLearning

Day 42-What I Learned In a Day (SQL) Today I focused on understanding and practicing core table operations (DDL commands) in SQL. I worked on: • Creating tables -defining structure with columns, data types, and constraints • Renaming columns - improving clarity and readability of table design • Altering tables - adding new columns to existing tables • Modifying columns - updating data types and applying constraints • Dropping columns - removing unnecessary data fields • Creating table copies -duplicating structure and data for backup/testing • Working with views - creating virtual tables for simplified and secure data access These concepts are essential because they help in designing, managing, and maintaining database structures efficiently. Consistent practice is helping me build strong fundamentals in SQL. Practiced 👇 #SQL #Database #Learning #TechSkills #100DaysOfCode

A small SQL tip that can make your queries much more powerful: ORDER BY with CASE. Most people use `ORDER BY` only for simple sorting: * ascending * descending But `CASE` inside `ORDER BY` lets you define custom sorting logic which is extremely useful in real-world scenarios. For example, suppose you want: • Active users first • Then Pending • Then Disabled Instead of relying on alphabetical order, you can control it: ``` SELECT * FROM users ORDER BY CASE status WHEN 'ACTIVE' THEN 1 WHEN 'PENDING' THEN 2 WHEN 'DISABLED' THEN 3 ELSE 4 END; ``` Why this is useful: ✅ Business-based sorting ✅ Prioritizing important records ✅ Cleaner UI ordering ✅ Better reporting queries You can even combine it with multiple conditions: ``` ORDER BY CASE WHEN priority = 'HIGH' THEN 1 ELSE 2 END, created_date DESC ``` This means: 1. High priority first 2. Then latest records within each group Small trick. But incredibly useful in dashboards, reports, and production queries. Sometimes the simplest SQL features… solve the most complex problems. #SQL #PostgreSQL #SoftwareEngineering #BackendDevelopment #Database #TechTips 🚀

SQL Cheat Sheet👇 SQL isn’t just about writing queries it’s about understanding how they execute. Every query follows a flow: FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT This means data is first picked, then filtered, grouped, and only at the end selected and sorted. Once you understand this sequence along with basics like JOINs and aggregations, your queries become more accurate and efficient. #SQL #DataAnalytics #DataEngineering #Learning #TechSkills

As I continue my SQL learning journey, I moved beyond just creating tables to understanding how to modify and manage them effectively. This is done using DDL (Data Definition Language) commands. At first, it feels like just structural changes, but these commands play a huge role in maintaining and evolving a database. Here’s what I explored: • Using ALTER to add, modify, or delete columns • Using DROP to completely remove tables or databases • Using TRUNCATE to quickly delete all records while keeping the structure • Using RENAME to update table or column names And the most important part: If you execute a DDL command → It is auto-committed There’s no rollback → Changes are permanent Structure + Changes = A well-maintained database. Small step, but a very important one in understanding how databases evolve in real-world scenarios. Read here →https://lnkd.in/dkG_UWnG #DataAnalytics #SQL #DatabaseManagement