Unlocking Data Insights with SQL Window Functions

I always assumed SQL queries execute from top to bottom… turns out that’s not how it works 😅 While practicing SQL, this was one of the biggest confusions for me. I used to think the SELECT statement runs first, but in reality, SQL follows a completely different execution order. Here’s the actual flow: FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY In simple terms: data is fetched → filtered → grouped → filtered again → then selected → and finally sorted. Let’s take an example: SELECT department, COUNT(*) AS total FROM employees WHERE salary > 50000 GROUP BY department HAVING COUNT(*) > 5 ORDER BY total DESC; How SQL processes this step by step: • FROM → collects all records from the table • WHERE → filters rows based on condition • GROUP BY → groups the data • HAVING → filters grouped results • SELECT → picks the required columns • ORDER BY → sorts the final output Understanding this changed the way I write SQL queries and made things much clearer. If you're learning SQL, this concept is a game changer 💡 #SQL #DataAnalytics #Learning #DataAnalyst #SQLQueries #TechLearning #CareerGrowth #OpenToWork

🚀 Day 2/30 — SQL Basics Every Beginner Must Know If you’re starting with SQL, these 3 commands are your foundation 👇 1️⃣ SELECT → Used to choose columns from a table 2️⃣ WHERE → Used to filter data 3️⃣ ORDER BY → Used to sort results 📌 Example: Imagine you have a sales table SELECT name, revenue FROM sales WHERE revenue > 5000 ORDER BY revenue DESC; 👉 This query shows top-performing customers with revenue greater than 5000 Simple, but powerful. 💡 What I realized today: SQL is not about memorizing queries, it’s about asking the right questions from data. 💬 Question for you: What was the first SQL query you learned? Let’s learn together 🤝 #SQL #DataAnalytics #LearningSQL #DataAnalyst #OpenToWork

Both SQL and Power Query can clean data. So which one do you actually use? This came up a lot while working on projects and it took a while to figure out where one ends and the other begins. Here is how the thinking settled. SQL is where the heavy lifting happens. When the dataset is large, when the logic is complex, or when the data needs to be reshaped before it even enters a tool, SQL is the right place to do it. It is closer to the source, faster on big data, and the queries are reusable and easy to document. Power Query takes over after that. Once the data is structured and the hard logic is done, Power Query handles the final formatting before the dashboard is built. Renaming columns. Fixing data types. Removing what does not need to be there. Small, clean, repeatable steps. The way to think about it: SQL shapes the data. Power Query prepares it for presentation. There is overlap and neither is wrong. But once this distinction clicked, it became much clearer which tool to reach for and why. Using both together makes the whole workflow cleaner. #SQL #PowerQuery #PowerBI #DataAnalytics #DataAnalyst #OpenToWork

As I continue exploring SQL fundamentals in more depth, today I focused on understanding the difference between CHAR, VARCHAR, and NVARCHAR.   These data types are commonly used, but choosing the right one is important for performance and storage.   What I learned: CHAR(n) : Fixed length  Always uses the defined space. If the value is smaller, it fills the remaining space with extra spaces.   VARCHAR(n) :Variable length  Stores only the actual data length, which helps save storage.   NVARCHAR(n) : Variable length (Unicode support)  Used to store multilingual data (like special characters, different languages).   Example: If we store 'Info' in CHAR(10), it still takes 10 characters. But in VARCHAR(10), it only uses space for 'Info'. And NVARCHAR is useful when storing values like names with different languages or special characters.   Key takeaway: Use CHAR for fixed-length data (like codes) Use VARCHAR for regular text Use NVARCHAR when Unicode or multiple languages are required   Revisiting these basics helped me understand how proper data type selection can impact database design and efficiency.   Still learning and improving step by step.   #DataEngineering #SQL #SQLServer #DatabaseDesign #DataAnalytics #LearningInPublic #OpenToWork

📊 Today I Learned: Using JOIN in SQL As part of my SQL learning journey, today I explored how to combine data from multiple tables using JOIN. 🔍 This is one of the most important concepts in SQL because real-world data is usually spread across different tables. 👉 I practiced using INNER JOIN to connect customer data with their orders. 💡 Key Learning: By joining tables, we can easily analyze relationships — like which customer purchased which product. 📈 This is a powerful step toward turning raw data into meaningful insights. #SQL #DataAnalytics #LearningJourney #BusinessAnalysis #OpenToWork

Today I spent some time revisiting the difference between NOT IN and NOT EXISTS in SQL to strengthen my understanding. At a basic level, both are used to filter out records, and they often look interchangeable. But when I explored a bit more, I realized they behave differently in certain cases. One important thing I noticed is how NULL values can affect the result. If the subquery used in NOT IN contains NULL, it can lead to unexpected results or even return no data. On the other hand, NOT EXISTS works differently and checks row by row, which makes it more reliable in such scenarios. Takeaways: NOT IN works fine when you are sure there are no NULL values NOT EXISTS is safer when dealing with real-world data Small differences in SQL logic can have a big impact on results This helped me understand how important it is to not just write queries, but to think about how they behave with actual data. Still learning and improving step by step. #SQL #DataEngineering #Database #LearningInPublic #OpenToWork

📊 Day 6/100 ~ Querying Data Using SQL Let’s talk about something simple… but powerful: SQL queries. A lot of people think SQL is about memorizing commands. It’s not. At its core, a query is just a question you ask your data. 💡 For example: SELECT * FROM employees; This is not just code it’s a question: “What data exists in this table?” 🧠 Here’s the real insight: Data is useless if you don’t know what to ask. The difference between a beginner and a data analyst is simple: 👉 The ability to ask the right questions ** In the real world, you won’t be asked to “write SQL” You’ll be asked: • Which products are performing best? • Why are sales dropping? • Who are our top customers? SQL is just the tool. Thinking is the real skill. So If you can ask better questions, you will always get better answers from your data. #SQL #DataAnalytics #LearningInPublic #opentowork

🔍 Unlocking Deeper Insights with SQL Subqueries & Views As I continue my journey in Data Analytics, today I explored how SQL helps in writing more powerful and structured queries using subqueries and views. Here’s what I learned: 🔹 SQL Subqueries A subquery is a query written inside another query. It helps break down complex problems into smaller, manageable parts and makes data retrieval more efficient. Types of subqueries I explored: Single-row subquery → Returns one value Multiple-row subquery → Returns multiple values Correlated subquery → Depends on the outer query and runs for each row 🔹 SQL Views Views are virtual tables created from SQL queries. They do not store data physically but display data from one or more tables. Why views are useful: Simplify complex queries Improve data security by restricting access Make data more organized and reusable ✨ Key Takeaway: Subqueries help in solving complex queries step by step, while views help in simplifying and securing data access. Together, they make SQL more powerful and efficient for real-world data analysis. Continuing to explore and improve my SQL skills every day 🚀 #DataAnalytics #SQL #LearningJourney #DataAnalyst #LearningInPublic #CareerGrowth #OpenToWork

Started learning SQL today, and even the basics felt powerful. Using SELECT, FROM, and WHERE, I wrote a few simple queries on a sample dataset to view records, filter customers by city, and identify high-value sales. What surprised me most was how quickly SQL can turn thousands of rows into useful insights. A simple query like WHERE Sales > 5000 instantly highlighted the top-performing transactions. Big takeaway from today: good analysis starts with asking the right question before writing the query. Next step: learning GROUP BY and joins. #SQL #LearningSQL #DataAnalytics #DataAnalyst #BusinessAnalyst #MIS #OpenToWork

I used to save SQL questions for later but this time I actually solved them. I came across a post by Pradeep M with SQL interview questions and decided to work through them one by one. • Solved 10 questions • Practiced joins, aggregations, and window functions • Worked on real-world scenarios like DAU, retention, and segmentation I focused not just on the solution, but on understanding why it works - especially around edge cases and function choices. Sharing my solutions and approach in the document below 👇 Thanks Pradeep M for sharing these, really helpful for practice. Open to feedback and always looking to improve. #SQL #DataAnalytics #Learning #InterviewPreparation #OpenToWork