SQL Series for Beginners and Experts

📘 Ultimate SQL Cheat Sheet — My Complete SQL Revision Guide 🚀 After completing my MySQL learning journey, I created/found this powerful SQL Cheat Sheet that covers all essential SQL concepts in one place: ✅ Basic Queries ✅ Filtering & Conditions ✅ Joins (INNER, LEFT, RIGHT, FULL) ✅ Aggregate Functions ✅ GROUP BY & HAVING ✅ Subqueries ✅ Table Operations ✅ Data Manipulation (INSERT, UPDATE, DELETE) ✅ Constraints (Primary Key, Foreign Key, Unique, Not Null) ✅ Advanced SQL Concepts (Window Functions, CASE WHEN, Views) This cheat sheet is a quick revision companion for: 🔹 Students learning SQL 🔹 Developers preparing for interviews 🔹 Backend Engineers working with databases 🔹 Anyone mastering MySQL / SQL fundamentals SQL is the backbone of data-driven applications — mastering it opens doors to backend development, data analysis, and database engineering. 💡 Save this post for quick revision and future reference! #SQL #MySQL #DatabaseManagement #SQLCheatSheet #LearningSQL #BackendDevelopment #DataEngineering #Programming #TechLearning #SoftwareDevelopment #DeveloperJourney

Just earned the SQL 50 badge on LeetCode 🎯 Honestly, if you’re starting with SQL - this is all you need. These 50 problems cover almost every important concept, from basics to advanced queries. Big takeaway? 👉 Window Functions are a game changer. Once you get them, a lot of “complex” problems become straightforward. If you’re preparing for interviews or strengthening your backend/data skills, I highly recommend going through this set. 📌 Want MySQL notes covering everything from basic to advanced? Check out my repository: https://lnkd.in/g8CTyMKC Consistency > Everything. #SQL #LeetCode #DataStructures #CodingJourney #BackendDevelopment #Learning #Tech

🚀 Master SQL from Basics to Advanced — In One Playlist! Struggling to connect SQL concepts together? This curated playlist by CodeQueryHub brings everything into one structured journey — from fundamentals to real-world problem solving. 🎯 What’s covered (21 power-packed videos): ✔️ DDL & DML (core foundation) ✔️ Functions & Operators ✔️ CASE Statements (real logic building) ✔️ Subqueries (step-by-step clarity) ✔️ Joins & Union (data combining mastery) ✔️ Window Functions (advanced analytics made simple) 💡 Designed for: • Beginners starting SQL • Students preparing for interviews • Professionals refreshing concepts No unnecessary theory. Just clear explanations + practical use cases. 📌 Start learning here: https://lnkd.in/gGkwr8Ta Consistency > Complexity. One playlist. Real understanding. --- #SQL #LearnSQL #SQLTutorial #DataAnalytics #DataScience #Database #SQLForBeginners #SQLQuery #Coding #Programming #TechLearning #DataEngineer #BusinessAnalytics #Analytics #LearnToCode #CodingJourney #DataSkills #InterviewPreparation #TechCareer #SoftwareDevelopment #Developers #ITSkills #QueryLanguage #DataLearning #CareerGrowth #Upskill #OnlineLearning #Education #SQLServer #MySQL #PostgreSQL #WindowFunctions #Subqueries #Joins #DDL #DML

Swipe through the slides first 👉 then read below 👇 🚀 Day 22 of 30 — Learning PySpark from Scratch Your data doesn't live in CSV files at work. It lives in databases. Here's how PySpark connects to them. 🔌 Here's what I learned on Day 22 👇 ⚡ JDBC — the universal database connector PySpark uses JDBC to connect to any SQL database. PostgreSQL, MySQL, SQLite — same syntax for all of them. jdbc_url = "jdbc:postgresql://localhost:5432/mydb" props = {  "user": "your_user",  "password": "your_pass",  "driver": "org.postgresql.Driver" } df = spark.read.jdbc(url=jdbc_url, table="employees", properties=props) df.show(5) ⚠️ The most important thing I learned Never read a full table and filter in Spark. Push the filter to the database instead. # BAD — pulls ALL rows then filters df.filter(df.salary > 70000) # GOOD — filters at the DB level query = "(SELECT * FROM employees WHERE salary > 70000) AS emp" df = spark.read.jdbc(url=jdbc_url, table=query, properties=props) 💻 Parallel reads for big tables df = spark.read.jdbc(  url=jdbc_url, table="employees",  column="id",      # partition column  lowerBound=1,  upperBound=100000,  numPartitions=10,   # 10 parallel reads!  properties=props ) ✅ 3 things I didn't know before today → PySpark needs the JDBC driver JAR for your specific database → Pushing filters as SQL subquery = massive performance gain → numPartitions splits the table read across multiple workers 💡 My Day 22 takeaway PySpark isn't just for files. It's a full data platform that connects to your entire data ecosystem. ❓ What database does your team use most at work? Drop it in the comments 👇 Follow me for Day 23 tomorrow → Delta Lake explained simply 🔔 #PySpark #DataEngineering #BigData #Python #LearnInPublic #30DaysOfPySpark

JOIN performance — PostgreSQL Slow JOINs are not about JOINs. They are about: 👉 too much data 👉 late filtering 👉 missing indexes Reduce data first → JOINs become fast. Indexes + early filtering = smaller datasets = better performance. JOIN TYPES In PostgreSQL, there are 3 main JOIN execution strategies: 👉 Nested Loop — inefficient for large datasets 👉 Hash Join — most common in real-world queries 👉 Merge Join — less common than Hash Join WHEN MERGE JOIN IS USED -- when both tables are sorted by the join key (or can be sorted efficiently) -- when processing large datasets with limited memory -- when queries benefit from ordered data or range conditions Merge Join would work if we had indexes on join keys: users(id) orders(user_id) payments(order_id) HASH JOIN (SIMPLIFIED IN PYTHON) # STEP 1: users → hash user_map = {user["id"]: user for user in users} # STEP 2: users + orders user_orders = [   {"user": user_map[o["user_id"]], "order": o}   for o in orders   if o["user_id"] in user_map ] # STEP 3: result → hash order_map = {row["order"]["id"]: row for row in user_orders} # STEP 4: join payments result = [   {**order_map[p["order_id"]], "payment": p}   for p in payments   if p["status"] == "success" and p["order_id"] in order_map ] #PostgreSQL #SQL #Database #Backend #Performance #QueryOptimization #Indexes #DataEngineering #SoftwareEngineering #Tech #Programming

🚀 Day 29 of My SQL Journey – Subqueries (Part 4) Today I explored how subqueries can be used based on their location in SQL queries 📊 Instead of just learning types, I focused on where exactly we can use subqueries: 🔹 Subquery in SELECT Used to display calculated values alongside each row 👉 Example: Showing each customer with overall average amount 🔹 Subquery in WHERE Used for filtering based on conditions 👉 Example: Finding customers with second highest salary or matching categories 🔹 Subquery in FROM (Derived Table) Used to create a temporary table for further querying 👉 Example: Calculating average sales per store and using it as a table 🔹 Subquery in HAVING Used to filter grouped results based on aggregate conditions 👉 Example: Finding stores with transaction count less than overall transactions 💡 Key Takeaway: Subqueries are powerful because they help break complex problems into smaller steps and allow placing logic exactly where it's needed. 📈 Slowly moving from basic queries to writing more optimized and structured SQL! #SQL #LearningJourney #Day29 #Subqueries #Database #Coding #Tech #StudentDeveloper

After a lot of consistent effort, I’ve finally completed my SQL course 🎉 This journey helped me explore so many concepts, gain strong practical knowledge, and most importantly build a solid foundation in databases. 📌 Certificates don’t matter as much as the knowledge you gain — what truly counts is how you showcase your skills through real work. A big thanks to Tanakanti Vani for suggesting this course 🙌 I’ve also documented my day-to-day learning and practice notes here: 🔗https://lnkd.in/gQsCu-RF 💡 Why PostgreSQL? PostgreSQL stood out to me because: • It supports schemas within a single database, making it easier to organize and manage data • Great for multi-tenant applications by isolating client data without separate databases • Strong on security, modularity, and structured design 💡 Why choose PostgreSQL over MySQL / MSSQL? From my experience across projects, PostgreSQL is widely used because: • It’s open-source and cost-effective • Handles complex queries and advanced use cases efficiently • Supports modern workloads like JSON, analytics, and geospatial data • Highly scalable and reliable for long-term projects This is just the beginning — excited to build more and go deeper 🚀 MOURI Tech #SQL #PostgreSQL #LearningJourney #Consistency #BackendDevelopment #DataEngineering #python #AI #GenAI #fastapi

🚀 SQL Learning Journey 🗓️Day 30 🌟 Topic:LeetCode Subqueries Mastery Today marks a special milestone as I dive deeper into advanced SQL concepts using subqueries through real LeetCode problems. These problems really tested my understanding of filtering, ranking, and aggregation logic! Problems Solved: 👉 Employees Whose Manager Left the Company (LeetCode 1978) Focused on identifying employees whose managers are no longer present using subqueries in WHERE clause 👉Department Top Three Salaries (LeetCode 185) Learned how to use ranking functions + subqueries to fetch top 3 salaries per department 👉 Friend Requests II - Who Has the Most Friends (LeetCode 602) Applied aggregation with subqueries to find the most connected user 👉Movie Rating (LeetCode 1341) Combined joins + subqueries + grouping to derive meaningful insights ✨Key Takeaways: →Subqueries can simplify complex filtering logic →Correlated subqueries help solve row-wise dependency problems →Ranking functions (DENSE RANK, RANK) are game-changers →Real-world SQL problems require combining multiple concepts ✨ Note: 30 days of consistency, learning, and growth in SQL! This journey is helping me think more analytically and solve problems efficiently. #SQL #LeetCode #40DaysOfCode #DataAnalytics #CodingJourney #Database #Learning

‼️ The JOIN nobody teaches you in tutorials: LATERAL JOIN ‼️ Every SQL tutorial teaches you INNER JOIN and LEFT JOIN. Nobody talks about LATERAL JOIN 🤯 I stumbled across it while trying to solve a problem that was turning my subqueries into a mess. Here’s what it does and when you actually need it 👇 ――― 🔍 The problem it solves Imagine a customers table and an interactions table. You want the last 3 interactions per customer — not just the latest one. Most people try something like this 👇 ❌ THE MESSY WAY SELECT c.customer_id, (SELECT event_type FROM interactions i WHERE i.customer_id = c.customer_id ORDER BY created_at DESC LIMIT 1) AS last_event FROM customers c; This works for 1 row. But getting the last 3? You’d need 3 subqueries 😵 It breaks down fast. ――― ⚡ LATERAL JOIN to the rescue ✅ CLEAN APPROACH SELECT c.customer_id, i.event_type, i.created_at FROM customers c JOIN LATERAL ( SELECT event_type, created_at FROM interactions WHERE customer_id = c.customer_id ORDER BY created_at DESC LIMIT 3 ) i ON true; 💡 LATERAL means: For each row in customers, run this subquery using that row’s values. 👉 It’s basically a for-loop inside SQL ――― 🚀 Why this matters at scale In real-world systems, this pattern shows up everywhere: • Last N orders per user 📦 • Top 5 products per category 🛒 • Recent activity per account 📊 👉 LATERAL JOIN handles all of them cleanly No repeated subqueries ❌ No messy self-joins ❌ No Python post-processing ❌ ――― 🧠 Where it works ✔ PostgreSQL ✔ BigQuery (as CROSS JOIN LATERAL) ✔ Redshift ✔ Most modern databases ――― 🔥 Day 2 done. 28 to go. #SQL #LearningInPublic #DataEngineering #SQLTips #TechLearning #CareerGrowth #30DayChallenge #PostgreSQL #AdvancedSQL #DataAnalytics

Ready to level up your database skills? 🚀 I'm hosting a Live Session on DBMS & SQL Server! This isn't just theory—we are diving deep into practical, real-world scenarios to help you master queries, optimize performance, and manage data like a pro. Whether you're a student, an aspiring developer, or looking to sharpen your backend skills, this session is designed for you. 🕒 What to Expect: Core DBMS Concepts: Understanding the "why" behind the data. SQL Server Deep Dive: Hands-on with T-SQL, Joins, and Indexing. Live Practical: Watch and learn as we build and query in real-time. Q&A: Get your specific database questions answered. 📢 Service Available Now! If you are interested in joining or want more details, please drop a DM or comment "SQL" below! #SQL #SQLServer #DBMS #DatabaseManagement #DataScience #Coding #Programming #TechEducation #WebDevelopment #DataAnalytics #MicrosoftSQLServer #BackendDeveloper #LearnToCode #DatabaseDesign #SoftwareEngineering #DataEngineering #SQLQuery #TechWorkshop #OnlineLearning #CareerGrowth #ITTraining #ComputerScience #TechCommunity #BigData #SkillsDevelopment #LiveSession #HandsOnLearning #DeveloperLife #TechTips #DatabaseAdmin