Unlocking SQL Superpower with Window Functions

Day 7 of posting about Data Analytics. Ever feel like your SQL data is playing hide-and-seek?String functions are your secret weapon! From cleaning up messy text with TRIM() to extracting just the right info with SUBSTRING(), these functions are incredibly powerful. They help you transform raw data into actionable insights, making your reports look great. Today I revisited a few string functions. What's your go-to SQL string function? Share your favorite in the comments. Mine is CONCAT.I love how it gracefully handles NULLS as opposed to using + #DataAnalytics #SQL #StringFunctions ##Datascience #Techcommunity

Just explored SQL Window Functions and honestly… they make analytics so much cleaner 🚀 From calculating running totals with AVG() to ranking data using RANK() and comparing rows with LAG(), it’s all about analyzing data without losing row-level detail. If you’re working with SQL and not using window functions yet, you’re definitely missing out on some powerful insights. #SQL #DataAnalytics #DataScience #Learning #Analytics #SQLTips

I wish I knew this earlier in SQL. It would have saved me hours of confusion. Most beginners use GROUP BY for everything. But that’s a mistake. Because GROUP BY: → Removes your original data → Limits your analysis WINDOW FUNCTION does the opposite. They let you: - Keep all rows - Add calculations on top - Analyze data like a pro For example: ✔️ Running totals ✔️ Ranking ✔️ Comparing past vs current values All without losing your dataset. 👉 Once you understand this, SQL starts to feel easy. 👉 Save this — you’ll need it when practicing. Follow for more simple data concepts. Aman kr Singh LinkedIn #DataAnalytics #SQL #DataAnalyst #LearnSQL #Analytics #CareerGrowth #DataScience #LinkedInGrowth

Drop your answer before I hit depth 👇 This one trips up a lot of people. For the earliest year in the dataset, there’s no prior row for LAG() to look back at, so it returns NULL. Not 0, not an error, but NULL. And if you need a fallback value, LAG() actually has a built-in third argument for that: LAG(COUNT(*), 1, 0) gives you 0 instead of NULL. Window functions on aggregates can be next-level SQL, but knowing the edge cases is what separates good from great. 💾 Save this for your next interview and tag a data person who’d get this wrong. P.S. If you need to perform this type of operation or those involving window functions, in general, and the data will be consumed in a dashboard or view sitting on top of a BI tool, then it is highly likely the calculation should be in that layer and not in SQL. #sql #windowfunctions #dataengineering #datascience

Hi everyone 👋 I have uploaded a new video on SQL. In this video, I explained DDL (Data Definition Language) in a very simple way. If you are learning SQL or Data Analytics, this will help you 👍 🎥 Video link: https://lnkd.in/giHF-hq7 Please watch and share your feedback 😊

Day 6 – SQL 15 Days Challenge 📉 Today’s dataset told a story… but not the complete one. It had numbers lined up neatly: 1, 2, 3, 5, 6, 8, 9, 10 At first, it looked like a perfect sequence. But perfection in data is often an illusion. I paused and thought: 👉 “What is this data NOT telling me?” That’s when it clicked. Instead of trusting what was present, I focused on what was absent. So I created the full picture — a continuous sequence from start to end — and let the gaps reveal themselves. And there they were: ⚡ Missing → 4, 7 💡 Lesson of the day: Good analysts don’t just read data… they question its silence. Grateful for this perspective shift. Thanks to Ratan Kumar jha for the challenge 🙌 What’s one thing your data might be hiding? 👇 #SQL #DataAnalytics #SQLChallenge #ThinkLikeAnAnalyst #LearningInPublic #Day6

While practicing SQL, I started noticing something interesting… The same problem can be solved in multiple ways. Earlier, I used to stop after finding one solution. Now, I try different approaches to build my SQL muscle and understand the logic better. Sharing one simple example where I explored a few ways to solve it. #SQL #DataAnalytics #DataAnalyst #BusinessIntelligence #DataScience #Analytics #TechCareers #EnjoyLearning

𝗗𝗔𝗬 𝟰 : 𝗦𝗤𝗟 𝟭𝟱 𝗗𝗔𝗬𝗦 𝗖𝗛𝗔𝗟𝗟𝗘𝗡𝗚𝗘 𝗦𝗤𝗟 𝗤𝗨𝗘𝗦𝗧𝗜𝗢𝗡 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗼𝘂𝘁𝗽𝘂𝘁 𝗼𝗳: 𝗜𝗻𝗻𝗲𝗿 𝗝𝗼𝗶𝗻, 𝗟𝗲𝗳𝘁 𝗝𝗼𝗶𝗻, 𝗥𝗶𝗴𝗵𝘁 𝗝𝗼𝗶𝗻, 𝗳𝘂𝗹𝗹 𝗷𝗼𝗶𝗻, 𝗰𝗿𝗼𝘀𝘀 𝗷𝗼𝗶𝗻 CREATE TABLE Left_table (   A int ); INSERT INTO Left_table VALUES (1), (1), (1), (2), (3), (4), (7), (NULL), (NULL), (NULL); CREATE TABLE Right_table (   B INT ); INSERT INTO Right_table VALUES (1), (1), (2), (3), (3), (5), (NULL), (NULL); 𝗗𝗮𝘆 𝟯:  https://lnkd.in/gTAq3MJZ 𝗗𝗮𝘆 𝟮 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: https://lnkd.in/gruerkxN 𝗗𝗮𝘆 𝟭 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 : https://lnkd.in/ggKUdnjE #sql #sql15dayschallenge #20arrays #data #dataanalytics

Most people think SQL is complicated, but it really comes down to a few core commands that do 90% of the heavy lifting. 📊 Whether you're building a dashboard or conducting a deep-dive analysis, these basics are your best friend. From filtering data with WHERE to aggregating insights with GROUP BY, mastering these commands is the first step toward data fluency. 📌 Save this cheat sheet for your next project! #DataAnalytics #SQL #DataScience #TechTips #ContinuousLearning

Median in SQL can be derived using row positioning, total count, selective filtering, and averaging of middle values #SQL #DataAnalytics #DataAnalyst #BusinessIntelligence #DataScience #Analytics #TechCareers #EnjoyLearning