GROUP BY SQL - Data Aggregation Key

Week 6 of my Data Analytics Journey Last week, I got introduced to SQL, and I won’t lie, it was confusing at first 😅 I kept asking myself: What exactly am I creating? What am I selecting? But after going back to the class recordings and practicing, things started to make more sense. I learned how to: - Create a database and tables - Define columns and set a primary key - Use SELECT to choose the table I want to work with - Insert values into a table - Delete a row using the primary key. It’s still early, but I’m starting to understand how data is actually stored and managed. Slowly moving from confusion to clarity, one step at a time. @TechCrush.pro #RisewithTechCrush #Tech4Africans #LearningwithTechCrush #DataAnalytics #SQL #LearningJourney #Beginner #Growth #TechSkills

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

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

I used to think SQL was complicated… But the real problem was simpler than I thought. I wasn’t filtering my data properly I used to run queries on full datasets which made analysis slower and confusing. Then I started using WHERE conditions effectively: • Focus only on relevant data • Reduce unnecessary rows • Get faster and clearer insights Example: SELECT * FROM sales WHERE revenue > 10000; This small change made my queries: ✔ Faster ✔ Cleaner ✔ Easier to understand Now I spend less time searching data and more time analyzing it 📊 Still learning SQL every day as I move into Data Analytics What’s one SQL tip that improved your workflow? #SQL #DataAnalytics #LearningJourney #Analytics

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

Must-know SQL queries for Data Analysts. Revisiting the fundamentals — because strong basics make better analysts. Here’s a quick cheat sheet covering: • Filtering • Joins • Aggregations • Window functions • CTEs Simple. Practical. Useful #SQL #DataAnalytics #DataAnalyst #LearnSQL #Analytics #TechCareers #DataScience

📌 Most beginners write longer SQL queries than needed. One simple habit can make your queries cleaner and more efficient: Use GROUP BY properly. Instead of writing multiple queries to analyze data, you can summarize everything in one query. For example: ◽ total sales per product ◽ number of orders per customer ◽ average revenue by category All of this can be done using: GROUP BY Because in Data Analytics, it’s not just about getting results; it’s about getting them efficiently and clearly. Cleaner queries = better analysis. 📊 #SQL #DataAnalytics #DataAnalyst #LearningSQL #Database #Analytics #SQLTips #LearningInPublic

If your SQL uses DISTINCT to fix duplicates, you might be hiding a bigger problem. DISTINCT is one of the most misused keywords in SQL. Yes — it removes duplicates. But here’s the problem: Duplicates usually don’t come from nowhere. They come from your logic. Most times, it’s caused by: • Wrong JOIN conditions • 1-to-many relationships • Poor understanding of the data So when you write: SELECT DISTINCT * FROM table; You’re not fixing the issue. You’re masking it. A better approach is: 1.Understand why duplicates exist 2.Fix the JOIN or aggregation 3.Control the data at the source Because clean output doesn’t always mean correct logic. #SQL #DataAnalysis #Analytics #DataEngineering #LearningInPublic

🚨 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

📊 Day 6/100: THE "SELECT" COMMAND Yesterday, we talked about queries, the questions we ask our data Mastering how and when to use the SELECT command is crucial, non-negotiable and a game changer in your journey as an analyst. with that being Today, let’s dive into the most important SQL command which is the SELECT command If SQL were a language, SELECT would be your voice. It allows you to retrieve data from a database. Simple, yet powerful. 🔹 Basic syntax: SELECT column_name FROM table_name; 🔹 Example: SELECT name, sales_amount FROM orders; This means: ➡️ “Show me the name and sales amount from the orders table.” 💡 Why SELECT matters: - It’s the foundation of data analysis - Every insight starts with retrieving the right data - It’s used in almost every SQL query (yes, almost ALL). #SQL #LearningInPublic #100Daysofanalysis #DataAnalyst #SelectCommand