Choosing Views, Materialized Views, or Tables for Data Engineering

Slow dashboards… Long-running queries… Unexpected compute costs… Most of the time, the problem isn’t the data size — It’s how the SQL query is written. Using SELECT * unnecessarily. Overusing DISTINCT. Missing indexes on key columns. Poor query structure. Small optimizations can make a huge performance difference. In this carousel, I’ve covered: • Best practices to optimize SQL queries • Common mistakes analysts make • Practical tips to improve query performance 👉 Swipe through the slides to see real-world optimization techniques. 💬 What’s the biggest SQL performance issue you’ve faced? 🔁 Save this for later 👍 Like if this helped 🔔 Follow for daily SQL & analytics tips #SQL #DataAnalytics #SQLPerformance #SQLTips #AnalyticsCareers

✅ 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

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

We simplified how recommendations are shown in Releem SQL Query Analytics. You can now see analysis and recommendations for top and slowest queries without extra clicks. If a query is fine, you’ll still see that it was checked and when it was last analyzed.

📊 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

🚀 Day 40 of SQL Series Creating Calculated Columns Most beginners just SELECT data… But real analysts CREATE new data 👇 📊 Example: SELECT order_id, amount, quantity, -- Calculated columns amount * quantity AS total_revenue, amount * 0.18 AS tax, amount + (amount * 0.18) AS final_price FROM orders; 💡 What are Calculated Columns? 👉 Columns created using formulas inside SELECT You’re not just reading data… You’re transforming it ⚡ Save this for later 🔖 Follow for daily SQL tips 🚀 #SQL #DataAnalytics #LearnSQL #BusinessAnalytics #TechSkills

🚨 Most SQL problems are not SQL problems. They are grain problems. A lot of analysts open SQL, join 3 tables, aggregate, and then wonder why the numbers are wrong. The failure usually starts before the first line of SQL: What does 1 row represent? That single question decides whether your output is correct or garbage. In real systems, grain is rarely clean. One table may be: 1 row per transaction Another may be: multiple status updates for the same transaction Another may be: multiple fee records for the same transaction Now someone joins all 3 and writes: count(*) sum(amount) Looks normal. It is not normal. It is a multiplier. That is how dashboards end up showing: inflated transaction volumes duplicated revenue fake operational trends numbers that die in reconciliation ✅ The right approach is simple: Define the business question Define the target grain Reduce each source to that grain Then join Then aggregate SQL does not save you from bad logic. It scales it. The best analysts do not start with functions. They start with grain. #SQL #DataAnalytics #DataEngineering #BusinessIntelligence #Analytics #SQLTips #DataModeling

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

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

Spotting duplicates like a pro 🧠💻 Master the SQL query that helps you find duplicate records instantly — because clean data = powerful insights!#SQL #SQLQueries #DataAnalytics #DataCleaning #DuplicateRecords