Programming Valley’s Post

Stop Googling basic SQL syntax! I came across this comprehensive SQL Cheat sheet and knew I had to share it. Whether you are just starting your data journey or you're a seasoned pro who occasionally forgets the exact syntax for a FULL OUTER JOIN, this is a lifesaver. What’s inside: Categories: DDL, DQL, DML, DCL, and TCL. Joins & Sets: Clear visual representations of how data merges. Practical Examples: Real code snippets for creating tables, filtering data, and aggregating results. Database management is the backbone of data science and engineering. Having a quick reference like this keeps the workflow smooth and the queries sharp. Hope this helps someone today. #SQL #DataEngineering #DataAnalytics #Coding #Databases #LearningEveryday

Struggling to write precise SQL queries? 🤔 Here’s a simple way to master data filtering & make your queries powerful 🚀 🔹 Special Operators in SQL Special operators help filter data efficiently and handle real-world query conditions. 1️⃣ IN / NOT IN  Used to filter records based on multiple values in a single condition. 2️⃣ BETWEEN / NOT BETWEEN  Used to retrieve data within a specific range of values. 3️⃣ LIKE / NOT LIKE (📌Wildcard Search📌)  Used for pattern matching when exact values are unknown. % – Matches zero or more characters _ – Matches exactly one character 4️⃣ IS NULL / IS NOT NULL Used to identify missing or available data in a table 🔹 Understand relational operators to compare data easily > Greater Than < Less than >= Greather than or Equal <= Less than or Equal <> NOT EQUAL 🔹 Combine columns using Pipe Operator (||) 💡 Stop writing basic queries. Start writing smart queries that give real insights. #DataAnalysis #DataAnalyst #DataScience #CareerInData #DDL #DML #TechSkills #SQL #Oraclesql #plsql #sqlplus #sqlloader #Python #PowerBI #NoSQL #MongoDB #BigData #GrowthMindset #LearningData #TechDataCommunity #DataAnalytics #LearnSQL #Database #TechLearning #CodingTips #DataScience #LinkedInLearning #Upskilling #ContinuousLearning 📈✨

I stopped writing long SQL queries. And my work got better. Earlier, I thought complex problems needed complex queries. One giant script. Nested logic. Everything in one place. It looked impressive. It was also hard to debug, hard to explain, and easy to break. So I changed one habit. Now I write SQL like I’m telling a story. 🔹 Break it into steps 🔹 Use clear, meaningful names 🔹 Build logic layer by layer 🔹 Validate each step before moving on Most of my queries now are just a series of simple blocks stitched together. The result? Faster debugging. Cleaner logic. Easier handoffs. Here’s the truth: SQL isn’t about writing the smartest query. It’s about writing the clearest one. 🔍 If someone else reads your query tomorrow, will they understand it in 2 minutes? #SQL #DataAnalytics #DataEngineering #AnalyticsMindset #QueryOptimization #DataModeling #ETL #DataWorkflow #BigQuery #Snowflake #Database #DataProfessionals #TechCareers #CleanCode #DataBestPractices #AnalyticsCommunity #DataStorytelling #CodingTips

Today’s learning was around SQL fundamentals and how databases actually work behind the scenes. Started with understanding the difference between SQL and NoSQL, and how relational databases store data in a structured, tabular format. Worked on core SQL concepts like: Writing queries using SELECT Filtering and sorting data using WHERE and ORDER BY Using DISTINCT to get unique values Performing aggregation and grouping data Understanding pagination using LIMIT and OFFSET Also explored how data is defined and modified: Creating tables with proper data types and constraints Using SQL as DDL (Data Definition Language) and DML (Data Manipulation Language) Updating and modifying existing data One interesting part was understanding how pagination works behind the scenes #sql #database #backend #learninginpublic #chaicode

🗄️ 5 Basic SQL Queries Every Developer Must Know If you're starting your data journey — or just need a quick refresher — these are the building blocks of SQL that you'll use every single day. ✅ SELECT — Read/fetch data from a table ✅ WHERE — Filter rows based on conditions ✅ ORDER BY — Sort your results (ASC or DESC) ✅ INSERT INTO — Add new records to a table ✅ UPDATE — Modify existing records ✅ DELETE — Remove records you no longer need Together, these form the CRUD pattern: 📌 Create → INSERT 📌 Read → SELECT 📌 Update → UPDATE 📌 Delete → DELETE 💡 Pro tip: Always use a WHERE clause with UPDATE and DELETE. Running them without it affects every row in the table — a mistake no one wants to make twice! 😅 SQL is one of the most in-demand skills in tech, and the good news? The basics take just a few hours to learn. Start small, practice on real datasets, and build from there. What was the first SQL query you ever wrote? Drop it in the comments! 👇 #SQL #Database #DataEngineering #LearnSQL #BackendDevelopment #TechTips #Programming #DataScience #CodingLife #100DaysOfCode

They say you haven't truly lived as a developer until you’ve had a minor heart attack before hitting "Execute" on a DELETE query. 😅 I officially started my SQL journey today with Chai Aur Code, and the biggest "Aha!" moment wasn't about syntax—it was about architecture. The big realisation: Data doesn't go magically inside the database. It lives on the hard disk or SSD. The database is actually just highly intelligent software that acts as a bridge, helping us read and write to that disk efficiently. Whether it's SQL or NoSQL, at the end of the day, they both store data on physical disks. The real magic lies in the architecture each software follows to handle different use cases. Here’s how I’m breaking down the learning: DDL (Data Definition Language): Designing the blueprint of how that data is structured on the disk. DQL (Data Query Language): The efficient way to ask the software to "fetch" specific bits of data. DML (Data Manipulation Language): The power to change, add, or—frighteningly—remove data. Always write a SELECT query with your WHERE clause first to verify the results before you swap it for a DELETE. Better safe than sorry! Huge thanks to Hitesh Choudhary and Piyush Garg for making these deep architecture concepts so simple and practical. Onward to Joins and more! 🚀 #SQL #NoSQL #DBMS #ChaiAndCode #BackendDevelopment #CodingJourney #DataEngineering #SoftwareArchitecture Hitesh Choudhary, Piyush Garg, Akash Kadlag, Jay Kadlag

🚀Week 6 SQL journey :Scaling Up & Speeding Up with Oracle SQL My journey into Data Analytics is getting more technical and exciting! This week was all about moving from "writing queries" to "managing systems." Here are the four pillars of my learning this week: 🔗 Mastering Joins:  Data is rarely in one place. I dove deep into Equi Joins for perfect matches, Outer Joins to catch missing data, and the Self Join—a powerful tool for uncovering hierarchical relationships within a single table. 🔢 The ROWNUM Logic:  I finally cracked the "Subquery" secret for ROWNUM. Since it's a pseudo-column, you can't just pick "Row 5." I learned how to use nested queries to "freeze" row numbers, allowing for precise data pagination. 📥 SQL*Loader Automation:  Why insert rows one by one when you can automate? I practiced building Control Files (.ctl) to map external CSV data directly into my schema. Understanding FIELDS TERMINATED BY and OPTIONALLY ENCLOSED BY is a game-changer for high-speed data loading. ⚡ Materialized Views (MV):  The ultimate performance booster! I learned how to create Materialized Views that physically store query results. By using BUILD IMMEDIATE and REFRESH COMPLETE ON COMMIT, I can turn heavy, slow calculations into instant insights. It’s been a week of connecting the dots—literally and figuratively. Huge thanks to my mentor Praveen Kalimuthu and Tech Data Community for the guidance! #DataAnalysis #SQL #PLSQL #SQLLDR #Python #MONGODB #PowerBI #LearningJourney

I wrote 71 pages of SQL notes by hand. From scratch. Not ChatGPT. Not copy-paste. Real pen. Real paper. Real understanding. Here's what's inside 👇 ✅ DBMS vs RDBMS (and why it matters) ✅ Data types — CHAR vs VARCHAR vs VARCHAR2 (with memory diagrams) ✅ All 6 constraints with real examples ✅ Every SQL category: DDL / DML / TCL / DCL / DQL ✅ WHERE clause — AND, OR, NOT, IN, BETWEEN, LIKE (with % and _) ✅ NULL handling — IS NULL vs IS NOT NULL ✅ SRFC vs MRFC functions with GROUP BY rules ✅ HAVING vs WHERE — when each one fires ✅ Full ORDER OF EXECUTION diagram (FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY) ✅ Sub-queries — single row, multi row, nested, correlated ✅ Sub-query operators: ALL, ANY, EXISTS, NOT EXISTS ✅ ROWID vs ROWNUM — pseudo columns explained with examples ✅ All 5 JOIN types with output tables drawn by hand This is Oracle SQL but the concepts apply to every SQL database. MySQL. PostgreSQL. Snowflake. It all starts here. 📌 Save this if you're preparing for a Data Analyst or Data Engineer interview. ♻️ Repost if someone on your feed is learning SQL. I'm a Data Engineer at IBM, sharing my full learning journey → SQL → Python → Spark → Snowflake → dbt. Follow along if you want notes. What SQL concept gave you the most trouble? Drop it below 👇 I'll explain it in my next post. #SQL #OracleSQL #DataEngineering #SQLInterview  #LearnSQL #DataAnalytics #IBMConsulting  #TechNotes #InterviewPrep #HandwrittenNotes

Most people overcomplicate recursive SQL queries. Honestly, I used to be one of them, it felt far more complicated than it needed to be. It’s not magic. It’s just a loop. Sharing a blog post I wrote on this topic to breakdown this powerful concept to help you actually understand what’s going on under the hood 👇 I hope you find it useful. https://lnkd.in/d2fxr_Gu #SQL #DataScience #DataEngineering

Clean queries. Clear insights. That’s the power of mastering basic SQL commands. #SQL #DataScience #DataAnalytics #ProgrammingLife #LearnSQL #TechCareer #Analytics #Database #Coding #Growth #DataEngineering #BigData #DataVisualization #BusinessIntelligence #TechSkills #CodingJourney #DeveloperLife #DataDriven #Upskilling #CareerGrowth #DataTools #Querying #DataManagement #SoftwareDevelopment #ITSkills #DigitalSkills #LearningEveryday #TechCommunity #AnalyticsLife #FutureSkills