Chinmay Pisu’s Post

#TheDataMonk #SQL_Series #Day3 #QueryOptimisation #DataModeling 📌Number of Users enrolled - 1200+ 🎯Target - To Solve 188 Most Asked SQL Interview Questions in 10 Days 🔥 SQL Series - Day 3/10 🔥 📌Day 1 – Basic SQL Questions + Most Asked SQL Interview Questions - https://lnkd.in/gEQmw8pm 📌Day 2 – JSON, Date and Time SQL Interview Questions - https://lnkd.in/gjtmu3Hi 📌Day 3 – SQL Query Optimization and Data Modeling Interview Questions - https://lnkd.in/eSKH48Vp Topics covered in Day 3: ✅ Query Execution Plans - How to read them like a pro ✅ Index Optimization - When and why they're crucial ✅ JOIN Optimization - Master the 4 types of joins ✅ Subqueries vs CTEs - Which one actually wins? ✅ GROUP BY Optimization - Aggregating 100M rows efficiently ✅ Normalization vs Denormalization - The real-world tradeoff ✅ Star Schema vs Snowflake Schema 📌No. of Questions to cover - 188 📌Target Roles - Data/Business Analyst, Business Intelligence Engineer Data Engineer 📌No. of Days - 10 📌Prerequisite - None 📌Fee - FREE ✅ Ad-on ➡️ We will also be sharing a free e-book with 220+ Interview Questions at the end of the course We’re offering this eBook for FREE to help you strengthen your learning journey and catch up on anything you might have missed along the way. ✅ How to enroll for the FREE course 📍 Like this post 📍Comment your email ID below 📍Follow Nitin Kamal for more analytics-related interview questions and giveaways #DataScience #MachineLearning #DataAnalytics #BigData #DataEngineering #DataScientist #BusinessAnalytics #DataAnalysis #Analytics #SQL #ETL #DataScienceJobs

Still using basic GROUP BY for everything? You’re leaving SQL power on the table 👀 Window functions are what separate beginners from real data analysts/data engineers. Master these essentials: ✅ ROW_NUMBER() → assign unique row IDs ✅ RANK() → rank rows with ties ✅ LAG() → compare with previous rows ✅ NTILE() → split data into buckets ✅ SUM() OVER() → running totals These functions show up in: 📌 SQL interviews 📌 Data analyst tasks 📌 Business reporting 📌 Real-world dashboards Learn them once → use them forever. Save this before your next SQL interview 💻🔥 #SQL #DataAnalytics #DataEngineer #SQLInterview #LearnSQL #DataScience #BusinessAnalytics #TechCareers #Programming #DataAnalyst #SQLTips

SQL Real Scenario Questions Every Data Analyst Should Know 💡 I’ve compiled 10 important SQL questions with answers that are commonly asked in interviews and real projects. If you're preparing for Data Analyst roles, this will help you strengthen your fundamentals. Save this for later and keep practicing 🚀 #SQL #DataAnalytics #InterviewPrep #DataAnalyst #Learning #CareerGrowth

SQL Real Scenario Questions Every Data Analyst Should Know 💡 I’ve compiled 10 important SQL questions with answers that are commonly asked in interviews and real projects. If you're preparing for Data Analyst roles, this will help you strengthen your fundamentals. Save this for later and keep practicing 🚀 #SQL #DataAnalytics #InterviewPrep #DataAnalyst #Learning #CareerGrowth

🚀 Important SQL Queries Every Data Analyst Should Know As part of my Data Analytics learning journey, I’m practising essential SQL queries: ✅ SELECT & WHERE ✅ GROUP BY & ORDER BY ✅ Aggregate Functions ✅ JOINS (INNER, LEFT, RIGHT) ✅ Subqueries ✅ LIMIT These queries help in extracting insights, analyzing trends, and making data-driven decisions. I'm continuously improving my SQL skills along with Power BI, Excel, and Python to become a Data Analyst. Excited to keep learning and building projects! 📊 #SQL #DataAnalytics #DataAnalyst #LearningSQL #PowerBI #Excel #Python #CareerGrowth #AspiringDataAnalyst

10 SQL patterns that cover 90% of data science interviews. I've seen hundreds of SQL interview questions. Most of them boil down to the same 10 patterns. Once you recognize the shape of the problem, the query writes itself. Here they are: → Ranking Within Groups (ROW_NUMBER, RANK, DENSE_RANK) → Year-over-Year Comparison (self-join with date logic) → Running Totals (SUM with window function) → Finding Duplicates (GROUP BY + HAVING) → LAG / LEAD for Row Comparisons → Conditional Aggregation (CASE WHEN inside COUNT/SUM) → Gaps in Sequential Data (self-join or LEAD) → Retention / Repeat Users (self-join with date window) → CTEs to Break Down Complex Logic → Percentile / Median without built-in functions The mistake most people make: memorizing queries. Don't do that. Understand the pattern. Know when to use a window function vs a self-join vs a CTE. Know why you pick RANK over ROW_NUMBER. That's what separates candidates who pass SQL rounds from those who don't. I put all 10 patterns with code examples in a carousel. Check it out below. ♻️ Repost to help someone prepping for interviews right now. 📘 Preparing for data analyst interviews? Check out the book I co-authored with Pritesh and Amney with 150+ real questions: https://lnkd.in/dyzXwfVp 📩 I share tips on data analytics & data science in my free newsletter. Join 24,000+ readers → https://lnkd.in/dUfe4Ac6 #sql #datascience #dataanalytics #interviewprep

Struggling with Slow SQL Queries? This One Concept Can 10X Your Performance! If your queries are taking too long, you're probably missing this powerful optimization tool SQL INDEX – The Game Changer Concept (Simple Explanation): An INDEX in SQL is like a table of contents in a book. Instead of scanning the entire table (full table scan), the database uses the Index to quickly locate the required rows. Without Index = Slow search (O(n)) With Index = Fast search (O(log n)) Why INDEX is Important? ✔ Speeds up SELECT queries ✔ Improves JOIN performance ✔ Enhances WHERE filtering ✔ Reduces query execution time Types of Indexes: 🔹 Primary Index 🔹 Unique Index 🔹 Composite Index 🔹 Clustered vs Non-Clustered Index When NOT to Use INDEX? ❌ On small tables ❌ On columns with frequent updates ❌ On low cardinality columns (e.g., Gender) Pro Tip: Too many indexes can slow down INSERT, UPDATE, DELETE operations. Use wisely! #SQL #DataEngineering #DataScience #DatabaseOptimization #QueryPerformance #Indexing #BigData #Analytics #TechCareers #LearningSQL Interview Insight: “Explain indexing in SQL and how it improves performance” “Difference between clustered and non-clustered index” If you're preparing for Data Analyst / Data Scientist interviews, mastering this topic is a must!

🗓️ Complete SQL Roadmap in 2 Months — From Zero to Interview-Ready! Most people spend months learning SQL with no clear direction. Here's a structured 8-week plan that takes you from absolute basics to analytics-level SQL. 👇 📅 MONTH 1 — Strong SQL Foundations Week 1 — Database & Query Basics ✅ Tables, rows, columns ✅ Primary key & foreign key ✅ SELECT, DISTINCT, WHERE (AND, OR, IN, BETWEEN) 🎯 You understand data structure & can filter data Week 2 — Sorting & Aggregation ✅ ORDER BY, LIMIT ✅ COUNT, SUM, AVG, MIN, MAX ✅ GROUP BY | HAVING vs WHERE 🎯 You summarize data like a pro Week 3 — Joins Fundamentals ✅ INNER JOIN, LEFT JOIN, RIGHT JOIN ✅ Join conditions ✅ Handling NULL values 🎯 You combine multiple tables correctly Week 4 — Joins Practice & Cleanup ✅ Remove duplicate rows after joins ✅ SELF JOIN with examples ✅ Data cleaning using SQL 🎯 You stop making join mistakes 📊 MONTH 2 — Analytics-Level SQL Week 5 — Subqueries & CTEs ✅ Subqueries in WHERE & SELECT ✅ Correlated subqueries ✅ Common Table Expressions (CTEs) 🎯 You write structured, readable queries Week 6 — Window Functions ✅ ROW_NUMBER, RANK, DENSE_RANK ✅ PARTITION BY & ORDER BY ✅ Running totals | Top N per category 🎯 You solve advanced analytics queries Week 7 — Date & String Analysis ✅ Date functions for daily/monthly analysis ✅ Year-over-year & month-over-month logic ✅ String functions for text cleanup 🎯 You handle real business datasets Week 8 — Project & Interview Prep ✅ Build a SQL project (Sales or HR data) ✅ Write KPI queries ✅ Daily interview question practice 🎯 You are SQL interview ready! 🚀 🖥️ Practice Platforms: 🟠 LeetCode SQL 🟢 HackerRank SQL 🔵 Kaggle Datasets 💡 SQL is the #1 skill for any Data Analyst role in 2025. You don't need a degree. You need a plan. Save this roadmap 🔖 and start today. Share with someone who needs direction in their data journey! 🤝 #SQL #SQLRoadmap #DataAnalytics #LearnSQL #DataAnalyst #SQLForBeginners #TechSkills #DataSkills #CareerGrowth #SQLInterview #WindowFunctions #CTEs #SQLJoins #DataEngineer #Analytics #UpSkill #LinkedInLearning #TechCareer #DataScience #SQLTips

🚀 Level Up Your SQL Game with 25 Reusable Patterns! If you're working with data—whether as a Data Analyst, BI Developer, or Software Engineer—mastering SQL patterns can save hours of effort and make your queries more efficient and scalable. I recently explored a powerful collection of 25 SQL reusable patterns that cover real-world scenarios we deal with daily 👇 🔹 Data filtering (NULL-safe conditions) 🔹 Aggregations & summaries with GROUP BY + HAVING 🔹 Funnel analysis using conditional counts 🔹 Anti-joins to find missing data 🔹 Dataset merging using UNION ALL 🔹 Ranking & Top-N analysis with window functions 🔹 Running totals & rolling averages 🔹 Cohort & retention analysis 🔹 Funnel conversion tracking 🔹 De-duplication techniques 🔹 Pivot & Unpivot transformations 🔹 Sessionization & event tracking 🔹 Gap analysis & duration calculations 🔹 Change detection & streak analysis 🔹 Percentiles & data distribution 🔹 Event attribution logic 🔹 String aggregation (CSV per entity) 🔹 Recursive CTEs for hierarchies & sequences 💡 These patterns are not just theoretical—they’re highly practical and widely used in real-world analytics, reporting, and data engineering workflows. ✨ Why this matters? Instead of rewriting complex queries from scratch, you can reuse these patterns to: ✔ Improve query performance ✔ Write cleaner SQL ✔ Solve business problems faster ✔ Crack data interviews with confidence 📌 Whether you're preparing for interviews or building dashboards, this is a must-have SQL toolkit! #SQL #DataAnalytics #BusinessIntelligence #DataEngineering #PowerBI #Analytics #Learning #CareerGrowth #TechSkills