Pooja Mallelor’s Post

🚀 Day 40 of My SQL Learning Journey Today I worked on a SQL problem involving aggregation and string manipulation 🔥 🔹 Problem: Group sold products by date and list them in sorted order 🔗 Problem Link: https://lnkd.in/gp6qVmNt 🔹 Solution: SELECT sell_date, COUNT(DISTINCT product) AS num_sold, GROUP_CONCAT(DISTINCT product ORDER BY product SEPARATOR ',') AS products FROM Activities GROUP BY sell_date; 🔹 Key Learning: Using COUNT(DISTINCT) for unique values Combining rows using GROUP_CONCAT() Sorting values inside aggregation 💡 SQL can generate clean reports directly from raw data! Consistency continues 🚀 #SQL #LeetCode #90DaysOfCode #DataAnalytics #CodingJourney

🚀 Day 44 of My SQL Learning Journey Today I solved a SQL problem involving aggregation and distinct counting 🔥 🔹 Problem: Find number of unique leads and partners per day 🔗 Problem Link: https://lnkd.in/gXnubrbd 🔹 Solution: SELECT date_id, make_name, COUNT(DISTINCT lead_id) AS unique_leads, COUNT(DISTINCT partner_id) AS unique_partners FROM DailySales GROUP BY date_id, make_name; 🔹 Key Learning: Using multiple COUNT(DISTINCT) Grouping by multiple columns Real-world reporting queries 💡 SQL helps transform raw data into meaningful insights! Consistency continues 🚀 #SQL #LeetCode #90DaysOfCode #CodingJourney #DataAnalytics

Today I learned that CASE is one of the most underrated tools in SQL. Most people use it only for simple if-else logic, but it does much more than that. It lets you turn raw data into meaningful categories right inside the query. Example : - SELECT name, marks, CASE WHEN marks >= 90 THEN 'Excellent' WHEN marks >= 75 THEN 'Good' WHEN marks >= 50 THEN 'Average' ELSE 'Needs Improvement' END AS performance FROM students; What I like about CASE is that it keeps the logic close to the data. Instead of cleaning or labeling things later in code, you can classify rows right in SQL itself. That makes queries easier to read, easier to reuse, and much closer to how we actually think about data. In one line: CASE turns a plain table into something more human. #SQL #DataEngineering #LearningInPublic

Most SQL learners stop at: SELECT JOIN GROUP BY But the real unlock happens when you learn Window Functions. They help you: rank rows compare current vs previous values calculate running totals analyze trends without losing row-level detail The key idea: GROUP BY collapses rows Window functions keep rows and add context If you're learning Data Engineering, this topic is worth mastering early. 📌 Save this for later 💬 Which one should I explain next: RANK() or LAG()? #DataEngineering #SQL #LearningInPublic #DataEngineer #ETL #AnalyticsEngineering

❄️ LeetCode Daily Challenge 📅 Day 40 of 50 Days SQL Challenge Today’s challenge focused on subscription funnel analysis — a common real-world business analytics use case 📈 📌 Problem: Analyze Subscription Conversion 🔗Problem Link: https://lnkd.in/gSEZWxJb 💡 Problem Breakdown: Identify users who converted from free trial to paid subscription: ✔ Find users who moved from free_trial to paid ✔ Calculate average daily activity duration during free trial ✔ Calculate average daily activity duration during paid period ✔ Round averages to 2 decimal places 40 days of consistent SQL practice completed ✅ Daily practice is turning concepts into intuition 💪 Let’s grow one query at a time 🚀 💬 How would you approach this? Single query with conditional aggregation or separate CTEs? Drop your thoughts below 👇 #LeetCode #SQL #DataEngineering #AzureDataEngineer #ProductAnalytics #SQLPractice #LearningInPublic #50DaysChallenge #DataAnalytics

💡 SQL isn’t just a skill—it’s your data superpower. From basic commands like SELECT, INSERT, UPDATE, DELETE to advanced concepts like JOINs, window functions, and aggregations, mastering SQL is essential for every data professional. What stood out to me: ✔️ Understanding how data is retrieved and structured ✔️ Using JOINs to connect insights across tables ✔️ Leveraging functions like RANK(), GROUP BY, and CASE for deeper analysis The difference between knowing SQL and using SQL effectively? 👉 Practice + real-world problem solving. Because at the end of the day, data is only valuable if you can query it right. #SQL #DataAnalytics #DataScience #Learning #CareerGrowth #TechSkills

𝗔 𝗦𝗶𝗺𝗽𝗹𝗲 𝗦𝗤𝗟 𝗛𝗮𝗯𝗶𝘁 𝗧𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗠𝘂𝗰𝗵 𝗘𝗮𝘀𝗶𝗲𝗿 One small habit that improved how I write SQL queries: I 𝗯𝗿𝗲𝗮𝗸 𝗺𝘆 𝗾𝘂𝗲𝗿𝘆 𝗶𝗻𝘁𝗼 𝘀𝘁𝗲𝗽𝘀 instead of writing everything at once. When I started learning SQL, I used to write long queries immediately. SELECT… JOIN… WHERE… GROUP BY… If something went wrong, it was difficult to know where the problem was. Over time I learned a better approach. 𝗦𝘁𝗲𝗽 𝟭: Start with the base table SELECT * FROM sales 𝗦𝘁𝗲𝗽 𝟮: Add filters WHERE year = 2024 𝗦𝘁𝗲𝗽 𝟯: Add grouping or calculations GROUP BY product 𝗦𝘁𝗲𝗽 𝟰: Add joins if needed JOIN products ON sales.product_id = products.id Breaking queries into steps helps with: • Debugging errors • Understanding the logic • Writing cleaner queries SQL becomes much easier when you 𝗯𝘂𝗶𝗹𝗱 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝘀𝘁𝗲𝗽 𝗯𝘆 𝘀𝘁𝗲𝗽 instead of all at once. Sometimes the difference between a confusing query and a clear one is simply 𝗵𝗼𝘄 𝘆𝗼𝘂 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘆𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴. #SQL #DataAnalytics #LearningSQL #DataThinking #Analytics

🔥 Day 5 – SQL Challenge 📌 Problem Statement: You are given a table orders with the following columns: order_id customer_id order_date order_amount 👉 Write a SQL query to find the top 3 customers who have spent the highest total amount. 💡 Expected Output: customer_id total_spent 🧠 Solution: SELECT customer_id, SUM(order_amount) AS total_spent FROM orders GROUP BY customer_id ORDER BY total_spent DESC LIMIT 3; 🎯 Concepts Covered: GROUP BY SUM() ORDER BY LIMIT 📢 : Today’s focus: Finding top customers based on total spending 🧠 Solved using aggregation and sorting 🚀 Consistency is the key — learning step by step! #SQL #DataAnalytics #Learning #CareerGrowth #100DaysOfCode #DataAnalyst #100DaysSQLChallenge 💻🔥

🚀 Day 25/50 of My #LeetCode SQL Challenge Today I solved “Product Sales Analysis III” 💡 This problem was about identifying the first year each product was sold and analyzing the corresponding sales data. 🔍 Key Learnings: How to identify the earliest occurrence of data within groups Strengthening concepts around data grouping and filtering Understanding how to extract meaningful insights from historical records 📊 Real-world relevance: This type of analysis is useful for: Tracking product launch performance Analyzing early sales trends Making data-driven business decisions ✨ Takeaway: Finding the first occurrence in data is a powerful technique that appears frequently in analytics and interviews. Consistency matters — one problem a day keeps improvement on track 💪 #Day25 #LeetCode #SQL #DataAnalytics #LearningInPublic #100DaysOfCode

Today was all about strengthening my SQL filtering and sorting skills — small steps, but powerful learning! 🔹 What I Learned Today: ✔️ Using WHERE clause for filtering data ✔️ Applying conditions like >, <, = ✔️ Sorting data using ORDER BY ✔️ Using LIMIT to get top records 🔹 Practice Queries I Worked On: 📌 Find top 5 sales based on boxes 📌 Get highest sales amount 📌 Filter customers with more than 200 purchases 📌 Sort data by location 💡 Key Learning: Filtering + Sorting = Better insights Understanding how to narrow down data is the first step toward real analysis. ⚡ Slowly building confidence in SQL… consistency is the key! #Day5 #SQLLearning #DataAnalyticsJourney #BeginnerToPro #Consistency #LearningEveryday #FutureDataAnalyst