Optimize SQL Queries for Better Performance

Views, Materialized Views, and Tables may look similar but they solve very different problems. Choosing the right one can impact performance, maintenance, and scalability of your system. Here’s a simple breakdown to understand when to use what : #DataEngineering #SQL #Analytics #BigData #DataAnalytics

✅ Solved a SQL problem on StrataScratch — Day 52 of my SQL Journey 💪 Numbers don’t mean much… until you compare them over time 📊 Today’s problem was about calculating the month-over-month percentage change in revenue — a simple metric, but one that tells a powerful story. The approach: • Aggregated total revenue per month • Joined each month with its previous month • Calculated percentage change using the classic MoM formula • Handled NULL cases for the first month cleanly What I practised: • Time-based aggregation using DATE_FORMAT() • Self joins for period comparison • Percentage calculations in SQL • Handling edge cases in analytical queries What stood out — Raw numbers show what happened. Comparisons show what changed. A revenue of 10K means nothing alone… but +25% or -10% tells the real story. That shift — from totals to trends — is what turns data into insight. Consistent learning, one query at a time 🚀 #SQL #StrataScratch #DataAnalytics #LearningInPublic #SQLPractice

Indexed Columns vs Non‑Indexed Columns When working with databases or large datasets, how you store data can be just as important as the data itself. Here’s a simple breakdown: Indexed Columns Think of an index like a table of contents in a book. - Much faster searches and filtering - Improves overall query performance --Trade‑off: Uses more storage and can slow down INSERT / UPDATE / DELETE operations Best for: Columns frequently used in WHERE, JOIN, GROUP BY, or ORDER BY Non‑Indexed Columns This is like reading a book page by page. - Smaller storage footprint - No extra overhead on write operations - Slower query performance on large tables Best for: Columns rarely used in filters or joins How do you decide which columns to index in your environment? Let’s discuss in Comment section. #DataEngineering #SQL #Databases #PerformanceOptimization #Analytics #PowerBI #DataAnalytics #MashapaAnalytics

📊 SQL Revision Day – Strengthening the Foundations Today, I focused on revising core SQL concepts by working on a dummy cellular company dataset. 🔍 Concepts I practiced: ✔️ SELECT & data filtering using WHERE (AND / OR, LIKE) ✔️ Sorting data using ORDER BY ✔️ Aggregating insights using GROUP BY ✔️ Combining multiple tables using INNER JOIN 💡 What stood out: Understanding how data connects across tables using JOINs really helps in seeing the bigger picture — especially when analyzing customers, plans, and subscriptions together. 📈 This hands-on revision is helping me move beyond theory and build practical confidence in SQL. #SQL #DataAnalytics #LearningJourney #DataSkills #BusinessAnalysis

✅ Solved a SQL problem on StrataScratch — Day 53 of my SQL Journey 💪 Data isn’t always clean… Sometimes it comes packed inside a single column 📦 Today’s problem was about analysing business categories — But the twist? Multiple categories were stored in one field. The approach: • Split comma-separated categories into individual rows • Used SUBSTRING_INDEX() to extract each category • Generated sequence numbers to iterate through values • Aggregated total reviews per category • Sorted to identify the most reviewed categories What I practised: • String manipulation in SQL • Handling multi-value fields • Using LENGTH + REPLACE for dynamic splitting • Transforming unstructured data into an analysable format What stood out — Real-world data is rarely perfect. Sometimes the problem isn’t analysis… It’s preparing the data so analysis becomes possible. Once you break structure out of chaos, insights start to appear naturally. Consistent learning, one query at a time 🚀 #SQL #StrataScratch #DataAnalytics #LearningInPublic #SQLPractice

DAY 18 Understanding Data Questions: The Real Skill Behind SQL Anyone can learn SQL syntax, but the real magic starts before you even touch the keyboard. Understanding what the data question is really asking is half the battle. Is it about trends, comparisons, or anomalies? Are we summarizing individual records or aggregated patterns? Do we need a single metric or a story from multiple joined tables? Once you truly understand the question, you can pick the right SQL tool for the job: GROUP BY + aggregates for summaries and KPIs JOINs to connect relationships across datasets CASE WHEN for conditional logic WHERE for filtering rows based on condition The stronger your grasp of data logic, the more powerful your SQL becomes. It’s not just about writing queries it’s about turning questions into insights. #DataAnalytics #SQL #DataAnalysis #BusinessIntelligence #DataThinking

Day 4/30 of SQL Challenge Today I learned: -> LIMIT Key idea: LIMIT is used to control how many rows are returned in a query result. Example: SELECT * FROM products LIMIT 5; What I understood: When working with large datasets, we don’t always need all the data. LIMIT helps us quickly preview or focus on a smaller portion. Small insight: LIMIT is often used with ORDER BY to get top or bottom results. Example: SELECT name, price FROM products ORDER BY price DESC LIMIT 3; This returns the top 3 most expensive products. Practice thought: What if I want to skip some rows and then get results? Example: SELECT * FROM products LIMIT 5 OFFSET 5; This skips the first 5 rows and returns the next 5. Note: OFFSET is used to skip some data. In another day we just learn the OFFSET. #SQL #LearningInPublic #Data #BackendDevelopment #DataEngineer #DataAnalyst

Day 28/30 Today’s sql class was a reminder that data analysis is not just about writing queries,it’s about making decisions through structure. On the surface, this looks like SQL. Tables, queries, outputs. But what we worked on was deeper than that. We took raw data and applied logic to categorize it, defining what is cheap, moderate, or expensive. And that right there is the work. Because data on its own doesn’t carry meaning. The analyst gives it meaning. How you group it. How you define it. How you choose to interpret it. That’s what shapes the insight. At the end of the day, business decisions are not made from raw tables, they’re made from structured, interpreted insight. Still building. Still refining. Still showing up. #Day28 #SQL #DataAnalytics #LearningInPublic #DataThinking #CareerGrowth

💡 SQL Quick Tip! Need to find the TOP performing records per category? Here's a classic pattern using ROW_NUMBER(): 📊 PARTITION BY product_category 📊 ORDER BY sales DESC This gives you the TOP 3 products in EACH category. Which SQL technique would you like to see next? #SQL #DataAnalytics #QueryTips #Analytics #Learning #TechSkills

Over time, I’ve realized SQL isn’t about writing complex queries - it’s about writing clear and efficient ones. Here are 3 SQL techniques I use almost daily: • CTEs (WITH clause) for breaking down complex logic • CASE WHEN for applying business rules directly in queries • Window functions for ranking, deduplication, and trend analysis These simple techniques have helped me write cleaner queries and make data easier to work with. Sometimes, it’s the basics done right that make the biggest difference. What SQL feature do you find yourself using the most? #SQL #Data #Analytics #DataAnalyst #BusinessIntelligence