Strengthening SQL Fundamentals with Subqueries and LeetCode Practice

🚀 Day 27 & Day 28 – SQL Learning Journey: Mastering Subqueries Over the past two days, I deepened my understanding of Subqueries in SQL by exploring both their types and behavior. 🔹 Day 27: Subqueries Based on Result Type Learned how subqueries differ based on what they return: • Scalar Subquery → returns a single value • Row Subquery → returns a single row • Table Subquery → returns multiple rows & columns 💡 Applied these concepts in real queries using SELECT, WHERE, and aggregations like SUM() and COUNT(). 🔹 Day 28: Subqueries Based on Dependency Explored how subqueries behave based on their relationship with the outer query: • Correlated Subquery → depends on outer query, executes row-by-row • Non-Correlated Subquery → independent, executes once (better performance) 💡 Key Insight: Choosing the right type of subquery is crucial for both query efficiency and performance optimization. 🔥 Consistency in learning is helping me build a strong foundation in SQL and data analysis. #SQL #DataAnalytics #LearningJourney #Subqueries #SQLPractice #DataAnalyst

🚀 Day 19 of My SQL Learning Journey Today I explored Subqueries (Case 2) — a powerful concept in SQL! 📌 What I learned: Subqueries are used when the data we want to retrieve depends on another query. In simple terms, when the condition or result comes from another table, we can use a subquery to connect them logically without explicitly joining tables. 🔍 Example scenario: Finding employees who earn more than the average salary in another department or retrieving records based on values from a different table. 💡 Key takeaway: Subqueries help in: Breaking complex problems into smaller queries Fetching data across multiple tables Writing more dynamic and flexible SQL queries Every day, I’m getting more comfortable working with data and understanding how databases communicate behind the scenes. 📈 Looking forward to diving deeper into joins and advanced queries next! #SQL #LearningJourney #DataAnalytics #Database #100DaysOfCode

🚀 Day 29 of My SQL Journey – 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀 (𝗕𝗮𝘀𝗲𝗱 𝗼𝗻 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻) Today I focused on understanding how subqueries can be used based on their location within SQL queries. Instead of just learning concepts, I explored where exactly subqueries fit in real-world scenarios. 🔹 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝘆 𝗶𝗻 𝗦𝗘𝗟𝗘𝗖𝗧 – Used to display calculated values alongside each row 🔹 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝘆 𝗶𝗻 𝗪𝗛𝗘𝗥𝗘 – Helps filter data based on conditions 🔹 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝘆 𝗶𝗻 𝗙𝗥𝗢𝗠 (Derived Table) – Creates temporary tables for further analysis 🔹 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝘆 𝗶𝗻 𝗛𝗔𝗩𝗜𝗡𝗚 – Filters grouped results using aggregate conditions 💡 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Subqueries make complex problems easier by breaking them into smaller steps and placing logic exactly where it’s needed. 📊 Practicing these concepts is helping me think more analytically and write more efficient SQL queries. #SQL #LearningJourney #DataAnalytics #Database #SQLQueries #Subqueries #TechSkills #StudentDeveloper

🚀 Day 26 – SQL Learning Journey Today’s focus was on one of the most powerful concepts in SQL: Subqueries & Correlated Subqueries 🔍 🔹 Subqueries (Inner Queries) - A query written inside another query - Used to break complex problems into simpler steps - Can be used in "SELECT", "WHERE", or "FROM" clauses - Example use: finding customers with above-average spending 🔹 Correlated Subqueries - A subquery that depends on the outer query - Executes row-by-row, making it more dynamic - Useful for comparisons within groups (like per customer, per store, etc.) 💡 Key Learnings: ✔ Simplified complex filtering logic ✔ Learned when to use subqueries vs joins ✔ Understood performance impact of correlated queries ✔ Practiced real-world scenarios like ranking, filtering, and segmentation 📊 Realizations: Subqueries are great for clarity, but correlated subqueries require careful use due to performance considerations. Consistency is the key 🔥 — one step closer to mastering SQL! #SQL #DataAnalytics #LearningJourney #Subqueries #CorrelatedSubqueries #Day26 generate a image and take a example of dataset

🚀 Day 5 – SQL Learning Journey | Indexes & Optimization Today I explored one of the most powerful concepts in SQL — Indexes, which play a key role in improving query performance. 📚 What I learned today: 📌 Indexes & Types – Improve data retrieval speed – Clustered vs Non-Clustered indexes ⚖️ Clustered vs Non-Clustered – Clustered → Physical order of data – Non-Clustered → Logical structure (separate from table) 🛠️ Index Commands – CREATE INDEX – DROP INDEX – REBUILD INDEX 🎯 Index Strategy – Use indexes on frequently filtered columns – Covering Index for better performance – Smart index selection is important 🔑 Index Types – Unique Index – Composite Index – Filtered / Partial Index 💡 Key Takeaway: A well-designed index can turn a slow query into a fast one 🚀 But over-indexing can also hurt performance — balance is key! Learning not just SQL, but how to write optimized and scalable queries 💪 Code pushed to GitHub 📂 🔗 GitHub Repository: https://lnkd.in/gm8Mw8CE #SQL #Database #Performance #LearningJourney #AspNetDeveloper #TechGrowth #InterviewPreparation

Day 27 of my SQL Night Learning Journey 🌙 Today, I learned about the SELECT INTO statement, and it made things feel a lot more practical. In simple terms, SELECT INTO helps you create a new table from an existing one while copying the data into it at the same time. Think of it like: “Take this table, duplicate it, and save it as a new one.” This is especially useful when: You want to create a backup of your data You need a temporary table to run the analysis without touching the original data One important thing I learned: The new table copies the columns and data types, but it does NOT automatically include things like: Primary keys Indexes NOT NULL constraints So it’s not a perfect clone, but it gets the job done for most use cases. A simple example that stood out to me: Creating a backup of a table by copying everything into a new one. Little by little, SQL is starting to feel less intimidating and more like a tool I can actually use 🛠️ #SQL #LearningInPublic #DataAnalytics #BeginnerFriendly #TechJourney #Consistency #WomenInTech

SQL Cheat Sheet👇 SQL isn’t just about writing queries it’s about understanding how they execute. Every query follows a flow: FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT This means data is first picked, then filtered, grouped, and only at the end selected and sorted. Once you understand this sequence along with basics like JOINs and aggregations, your queries become more accurate and efficient. #SQL #DataAnalytics #DataEngineering #Learning #TechSkills

day-31 🚀 Day 31 of My SQL Journey – 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗖𝗧𝗘𝘀 (𝗖𝗼𝗺𝗺𝗼𝗻 𝗧𝗮𝗯𝗹𝗲 𝗘𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻𝘀) Today I explored 𝗖𝗧𝗘𝘀, a powerful SQL feature that helps write cleaner, more structured, and readable queries. 🔹 Learned how to use the `WITH` clause to create temporary result sets 🔹 Understood how CTEs simplify complex queries and replace nested subqueries 🔹 Practiced grouping and filtering data using CTEs 🔹 Explored real use cases like data transformation and hierarchical queries 💡 𝗢𝗻𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: CTEs not only improve readability but also make SQL queries more maintainable and professional. Here’s a simple example I worked on: calculating total amount per customer and filtering those above a threshold using a CTE. 📈 Step by step, getting better at writing optimized and structured SQL queries! hashtag#SQL hashtag#CTE hashtag#DataAnalytics hashtag#LearningJourney hashtag#Database hashtag#100DaysOfCode hashtag#TechSkills

Second step in my transition — building a strong foundation in SQL. Over the past few days, I focused on learning and practicing core SQL concepts, moving beyond basics. Covered: · Subqueries · Joins · Aggregations · Stored Procedures · User-Defined Functions Instead of just learning syntax, I worked on understanding how these concepts are used to handle structured data and solve real problems. Currently applying these concepts in small practice datasets and preparing for more project-based work next. Step by step — focusing on building practical skills. #SQL #DataAnalytics

🚀 Day 27 – SQL Journey | Subqueries Deep Dive (Advanced Practice) Today, I explored one of the most powerful and essential concepts in SQL — Subqueries 🔍 I focused on understanding how subqueries work internally and how they help solve complex problems by breaking them into smaller, logical steps. 💡 What I Learned: ✔ Subqueries inside SELECT and WHERE clauses ✔ Handling intermediate results using nested queries ✔ Comparing values dynamically using subqueries ✔ Writing flexible and condition-based SQL queries 📌 Types of Subqueries Practiced: 🔹 Single Row Subqueries 🔹 Multi Row Subqueries (IN, ANY, ALL) 🔹 Correlated Subqueries (row-by-row execution) ⚙️ Key Takeaways: Subqueries execute from inside → outside Outer queries depend on inner results Data type compatibility is important Can be nested at multiple levels 🔥 Real-World Insight: Subqueries are powerful but can impact performance if not used efficiently. In many cases, JOINs can be a better alternative depending on the scenario. Subqueries are not just a concept — they are a problem-solving mindset in SQL. #SQL #Subqueries #AdvancedSQL #DataAnalytics #LearningJourney #SQLPractice #RDBMS