#AdvancedSQL

@praveennaresit.bsky.social

4 months ago

📊 Power BI with Advanced SQL – New Course Batch Starts December 1st!
tr.ee/LBRnWU
⏰ 8:00 AM (IST)
👩‍🏫 Trainer: Ms. Mohana

#PowerBI #AdvancedSQL #DataAnalytics #BusinessIntelligence #PowerBITraining #SQLDeveloper #DataAnalyst #NareshIT #LearnPowerBI #DashboardDesign #BIAnalyst #CareerGrowth

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@srikanthmogili.bsky.social

8 months ago

Online Power BI training poster by Naresh i Technologies featuring Ms. Mohana, covering analytics with basic to advanced SQL. Includes tools like Excel, SQL, Power Query, Power BI, and DAX. Training runs from August 5th to 16th, 2025 at 11:00 AM IST with Zoom registration link."

📊 Power BI + Advanced SQL = Your Shortcut to Data

Careers!🚀 Limited Slots – Register Now: tr.ee/dL0YOf

🔥 Master data visualization + backend querying

🧠 With expert trainer Ms. Mohana
📅 Starts: 5th Aug @ 8:30 PM IST

#PowerBI #AdvancedSQL #DataAnalytics #NareshIT

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@srikanthmogili.bsky.social

8 months ago

📊 Turn raw data into stunning dashboards!
Master Power BI + Advanced SQL with Mr. Vijay Rama Raju

📅 Starts 16th July | 🕘 9:00 AM IST

🔗Enroll Link: tr.ee/lQAPFm

#PowerBI #AdvancedSQL #DataVisualization #NareshIT #BItraining #TechSkills #freshers

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@narsimulumogili.bsky.social

9 months ago

📈 Power BI + SQL = Career Power-Up 💼
🗓️ 26 June | 🕢 7:30 PM IST

🔗Register here: tr.ee/NMTMUl

🎙️ Free demo with Mr. Laxman

More Courses: linktr.ee/ITcoursesFre...

#PowerBI #AdvancedSQL #BITraining #NareshIT
#DataAnalytics #FreeClass #DataVisualization #LearnSQL

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@srikanthmogili.bsky.social

9 months ago

📊 Power BI with Advanced SQL – Free Demo!

📅 26 June | 🕢 7:30 PM IST

🎙️ By Mr. Laxman
🔗 Register here : tr.ee/NMTMUl

Build dashboards, master SQL & unlock data insights 💡

#PowerBI #AdvancedSQL #DataAnalytics #NareshIT #FreeDEMOCourse #BITraining #TechSkills #OnlineLearning

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Praveen_NareshIT

@praveennaresit.bsky.social

9 months ago

Power BI with Advanced SQL – Free Class!

📊 Free Training: Power BI with Adv SQL
📅 12th June | ⏰ 8:30 AM IST
👩‍🏫 Ms. Mohana
🔗 tr.ee/ejeZjg
Learn Dashboards, Queries, Reports — for FREE!

#PowerBI #AdvancedSQL #TechSkills #BlueskyTech

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@kattasairamesh.bsky.social

10 months ago

Ready to master data like a pro? 📈

Join Mr. Laxman for a FREE session on Power BI + Advanced SQL

🗓️ 22nd May 2025 | 🕠 5:30 PM IST

🔗 tr.ee/pUa84z
📚 More free courses: linktr.ee/ITcoursesFre...

#PowerBI #AdvancedSQL #DataAnalytics #FreeTechCourses #LearnSQL #BItools #OnlineLearning #DataSkills

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DEV Community

@dev.to.web.brid.gy

10 months ago

10 SQL Anti-Patterns You Must Avoid in Production ## 10 SQL Anti-Patterns You Must Avoid in Production > “Slow SQL isn’t always about bad servers — it’s often about bad habits.” As SQL developers and data engineers, we often prioritize functionality and overlook **query quality**. But poor SQL habits can lead to: * Long response times * Bottlenecked applications * Excessive I/O and CPU * Poor scalability In this post, we’ll break down 10 critical **SQL anti-patterns** and how to fix them. ## ❌ 1. N+1 Query Pattern **Problem:** Querying in a loop for each record. -- BAD: Fetching orders per customer inside loop SELECT * FROM Customers; -- For each customer: SELECT * FROM Orders WHERE customer_id = ?; ✅ **Fix:** Join and aggregate in one query SELECT c.id, c.name, COUNT(o.id) AS order_count FROM Customers c LEFT JOIN Orders o ON o.customer_id = c.id GROUP BY c.id, c.name; ## ❌ 2. Wildcard Index Scans **Problem:** Leading wildcard disables index usage. SELECT * FROM Products WHERE name LIKE '%phone'; ✅ **Fix:** Use full-text search or structured LIKE -- Better (index usable) SELECT * FROM Products WHERE name LIKE 'phone%'; ## ❌ 3. Implicit Data Type Conversions **Problem:** Filtering numeric column with a string. SELECT * FROM Orders WHERE id = '123'; ✅ **Fix:** Match types explicitly SELECT * FROM Orders WHERE id = 123; 🔍 Use query plans to detect implicit conversion overhead. ## ❌ 4. Scalar Subqueries in SELECT **Problem:** Executing subquery for every row. SELECT id, (SELECT COUNT(*) FROM OrderItems WHERE order_id = o.id) FROM Orders o; ✅ **Fix:** Use join and aggregation SELECT o.id, COUNT(oi.id) AS item_count FROM Orders o LEFT JOIN OrderItems oi ON oi.order_id = o.id GROUP BY o.id; ## ❌ 5. SELECT * in Production **Problem:** Fetches unnecessary columns, causes bloat. SELECT * FROM Transactions; ✅ **Fix:** Select only what you need SELECT id, amount, transaction_date FROM Transactions; ## ❌ 6. Redundant DISTINCT **Problem:** Using `DISTINCT` to patch bad joins. SELECT DISTINCT name FROM Customers c JOIN Orders o ON o.customer_id = c.id; ✅ **Fix:** Analyze join logic and duplicates instead. ## ❌ 7. Missing WHERE Clause in DELETE/UPDATE -- Danger zone! DELETE FROM Users; UPDATE Orders SET status = 'Shipped'; ✅ **Fix:** Always qualify with a WHERE clause. DELETE FROM Users WHERE is_deleted = true; ## ❌ 8. No Index on Foreign Keys **Problem:** FK lookups scan entire referenced table. -- Missing index on Orders.customer_id ✅ **Fix:** Add supporting indexes CREATE INDEX idx_orders_customer ON Orders(customer_id); ## ❌ 9. Overusing OR instead of UNION ALL SELECT * FROM Orders WHERE status = 'pending' OR status = 'shipped'; ✅ **Fix:** Separate indexed paths with UNION ALL SELECT * FROM Orders WHERE status = 'pending' UNION ALL SELECT * FROM Orders WHERE status = 'shipped'; ## ❌ 10. Not Using ANALYZE or EXPLAIN **Problem:** Blindly writing queries without measuring impact. ✅ **Fix:** Always inspect query plans EXPLAIN ANALYZE SELECT ... ## Final Thoughts: Write It Once, Run It Well Avoiding anti-patterns isn't just about style — it's about **performance, reliability, and cost**. > "SQL is declarative. Let the engine help — but don’t tie its hands with bad habits." _#SQL #Performance #AntiPatterns #QueryOptimization #BestPractices #AdvancedSQL #DataEngineering_

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Forem

@forem.com.web.brid.gy

10 months ago

10 SQL Anti-Patterns You Must Avoid in Production ## 10 SQL Anti-Patterns You Must Avoid in Production > “Slow SQL isn’t always about bad servers — it’s often about bad habits.” As SQL developers and data engineers, we often prioritize functionality and overlook **query quality**. But poor SQL habits can lead to: * Long response times * Bottlenecked applications * Excessive I/O and CPU * Poor scalability In this post, we’ll break down 10 critical **SQL anti-patterns** and how to fix them. ## ❌ 1. N+1 Query Pattern **Problem:** Querying in a loop for each record. -- BAD: Fetching orders per customer inside loop SELECT * FROM Customers; -- For each customer: SELECT * FROM Orders WHERE customer_id = ?; ✅ **Fix:** Join and aggregate in one query SELECT c.id, c.name, COUNT(o.id) AS order_count FROM Customers c LEFT JOIN Orders o ON o.customer_id = c.id GROUP BY c.id, c.name; ## ❌ 2. Wildcard Index Scans **Problem:** Leading wildcard disables index usage. SELECT * FROM Products WHERE name LIKE '%phone'; ✅ **Fix:** Use full-text search or structured LIKE -- Better (index usable) SELECT * FROM Products WHERE name LIKE 'phone%'; ## ❌ 3. Implicit Data Type Conversions **Problem:** Filtering numeric column with a string. SELECT * FROM Orders WHERE id = '123'; ✅ **Fix:** Match types explicitly SELECT * FROM Orders WHERE id = 123; 🔍 Use query plans to detect implicit conversion overhead. ## ❌ 4. Scalar Subqueries in SELECT **Problem:** Executing subquery for every row. SELECT id, (SELECT COUNT(*) FROM OrderItems WHERE order_id = o.id) FROM Orders o; ✅ **Fix:** Use join and aggregation SELECT o.id, COUNT(oi.id) AS item_count FROM Orders o LEFT JOIN OrderItems oi ON oi.order_id = o.id GROUP BY o.id; ## ❌ 5. SELECT * in Production **Problem:** Fetches unnecessary columns, causes bloat. SELECT * FROM Transactions; ✅ **Fix:** Select only what you need SELECT id, amount, transaction_date FROM Transactions; ## ❌ 6. Redundant DISTINCT **Problem:** Using `DISTINCT` to patch bad joins. SELECT DISTINCT name FROM Customers c JOIN Orders o ON o.customer_id = c.id; ✅ **Fix:** Analyze join logic and duplicates instead. ## ❌ 7. Missing WHERE Clause in DELETE/UPDATE -- Danger zone! DELETE FROM Users; UPDATE Orders SET status = 'Shipped'; ✅ **Fix:** Always qualify with a WHERE clause. DELETE FROM Users WHERE is_deleted = true; ## ❌ 8. No Index on Foreign Keys **Problem:** FK lookups scan entire referenced table. -- Missing index on Orders.customer_id ✅ **Fix:** Add supporting indexes CREATE INDEX idx_orders_customer ON Orders(customer_id); ## ❌ 9. Overusing OR instead of UNION ALL SELECT * FROM Orders WHERE status = 'pending' OR status = 'shipped'; ✅ **Fix:** Separate indexed paths with UNION ALL SELECT * FROM Orders WHERE status = 'pending' UNION ALL SELECT * FROM Orders WHERE status = 'shipped'; ## ❌ 10. Not Using ANALYZE or EXPLAIN **Problem:** Blindly writing queries without measuring impact. ✅ **Fix:** Always inspect query plans EXPLAIN ANALYZE SELECT ... ## Final Thoughts: Write It Once, Run It Well Avoiding anti-patterns isn't just about style — it's about **performance, reliability, and cost**. > "SQL is declarative. Let the engine help — but don’t tie its hands with bad habits." _#SQL #Performance #AntiPatterns #QueryOptimization #BestPractices #AdvancedSQL #DataEngineering_

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Tpoint Tech

@techtpoint.bsky.social

11 months ago

Start Coding with Flutter: A Complete Tutorial for New Developers | Tpoint Tech Get more from Tpoint Tech on Patreon

Start Coding with Flutter: A Complete Tutorial for New Developers

Visit Website: www.patreon.com/posts/start-...

#SQLTutorial, #LearnSQL, #SQLForBeginners, #SQLLearning, #SQLCourse, #SQLBasics, #AdvancedSQL