Injae Park’s Post

💡 TMDL + VSCode = a game changer for Power BI development Just tested the new TMDL VS Code extension v1.5.1 today — and it’s a massive productivity boost. Using it, I was able to: add a new table to the semantic model, adjust relationships, create new measures, and even build the frontend of a new report page — all directly in code. What used to take hours of clicking in Power BI Desktop now takes just a few minutes in VS Code. The result? ✅ huge time savings, ✅ full control over the model structure, ✅ minimal frontend formatting effort. TMDL brings Power BI development closer to real software engineering — structured, version-controlled, and incredibly efficient. 🔧 If you work with Power BI, you should definitely try the TMDL VS Code extension v1.5.1. It’s a complete game changer. Thanks to Rui Romano for the inspiration.

LINQ-to-SQL: Efficient Paging 📑📑📑 What It Is 📑📑📑 Paging = splitting results into smaller chunks (pages). Common in APIs, grids, reports. Without proper paging → huge data loads = slow + memory heavy. 💡💡💡 Example with Skip/Take 💡💡💡 ✅ Basic Paging: var pageSize = 20; var pageNumber = 3; var data = context.Orders .OrderBy(o => o.Id) // ordering is required .Skip((pageNumber - 1) * pageSize) .Take(pageSize) .ToList(); ⚠️ Problem: On large tables, Skip becomes expensive (OFFSET in SQL still scans rows). 🚦🚦🚦 Keyset Pagination 🚦🚦🚦 Faster alternative → use Last Seen Id instead of Skip. var lastId = 1000; // last id from previous page var pageSize = 20; var data = context.Orders   .Where(o => o.Id > lastId)   .OrderBy(o => o.Id)   .Take(pageSize)   .ToList(); ✅ Much faster for infinite scrolling, large datasets. ✅✅✅ Best Practice ✅✅✅ Use Skip/Take for small/medium data sets (admin grids, dashboards). Use Keyset pagination for APIs, infinite scrolling, or millions of rows. Always OrderBy before paging → SQL requires deterministic order. 🛠️🛠️🛠️ Developer Story 🛠️🛠️🛠️ A reporting API used .Skip() on a 5M row table. Page 2000 took 30s+. 👉 Switched to Keyset Pagination → same page under 300ms 🚀 #DotNet #DotNetCore #CSharp #EntityFramework #EFCore #LINQ #LINQtoSQL #Performance #Paging #SkipTake #KeysetPagination #QueryOptimization #FullStackDeveloper #SystemDesign #BackendDevelopment #MicrosoftDeveloper #CodeTips

#SQL_Saga | #Episode_22 🎬 | #D90 | #CORRELATED_SUBQUERY 🔹 Topic: A correlated subquery is a subquery that depends on the outer query for its values. Unlike a normal subquery, it runs row by row, making it powerful but sometimes slower. 🔹 Syntax: SELECT column1 FROM table1 t1 WHERE column2 > ( SELECT AVG(column2) FROM table2 t2 WHERE t1.group_id = t2.group_id ); 🔹 Example: -- Find employees whose salary is above the average salary of their department SELECT e.name, e.salary, e.department FROM employees e WHERE salary > ( SELECT AVG(salary) FROM employees d WHERE e.department = d.department ); 🔹 Where it’s useful? ✅ Employees → Compare salary to dept average ✅ Sales → Orders above region average ✅ Inventory → Products priced above category average ✅ Finance → Transactions above account average ✅ Quality → Defects higher than product-line average 💡 Pro Tip: Correlated subqueries are powerful but can be slow on large datasets. Optimize using indexes or consider rewriting with JOIN + GROUP BY. 🎯 Mastering Correlated Subqueries = precision within groups!

✅ #SQL_Saga | #Episode_8 🎬 | #D76 | #DISTINCT 🔹 Topic: The DISTINCT keyword in SQL is used to return only unique (different) values — eliminating duplicates from your result set. 🔹 Syntax: SELECT DISTINCT column1, column2 FROM table_name; 🔹 Example: -- Get unique departments SELECT DISTINCT department FROM employees; 🔹 Where it’s useful? ✅ Employee → Get unique departments or job titles ✅ Sales → Find unique customer names ✅ Inventory → List distinct product categories ✅ Education → Unique student courses ✅ Finance → Unique payment methods 💡 Pro Tip: Use DISTINCT wisely — it adds sorting overhead. For large data, apply only when needed. 🎯 Mastering DISTINCT = keeping your data clean and clutter-free!

🎯 Built an Invoice & Payment Tracking System using SQL Server I'm excited to share my latest project - a complete financial database system that helps businesses track invoices, payments, and accounts receivable! What I Built: 📊 Database with 3 normalized tables (Customers, Invoices, Payments) 🔗 Foreign key relationships ensuring data integrity ✅ CHECK constraints for data validation 📈 Automated Accounts Receivable report showing: • Outstanding balances • Overdue invoices with days past due • Payment history by customer • Priority sorting (Overdue → Partially Paid → Unpaid → Paid) Technical Skills Demonstrated: Database design & normalization T-SQL (SQL Server) Data relationships & constraints Business reporting & analytics Financial data management Real-World Impact: This system helps businesses instantly identify which customers owe money, how much is overdue, and who to follow up with first - turning hours of manual tracking into seconds. Tools Used: SQL Server, Visual Studio Code, T-SQL This project reinforced my understanding of relational databases and how proper data structure can solve real business problems. 💭 What financial reporting challenges have you faced in your business? I'd love to hear your thoughts! #SQL #DatabaseDesign #DataAnalytics #FinancialData #SQLServer #DataEngineering #BusinessIntelligence #TechSkills #Learning

🚀 From Data Chaos to Database Clarity: My Journey Building an Open-Source Excel-to-SQL Converter in TypeScript As a developer, I’ve often wrestled with messy data; PDFs, Word docs, Excel sheets; scattered across formats and impossible to query directly. I needed a tool that could transform Excel files into clean, production-ready SQL. But every solution I found was either too rigid, too pricey, or just not flexible enough. So I built my own. Introducing Excel-to-SQL Converter; a TypeScript library that: ✅ Automatically detects data types ✅ Converts Excel, CSV, and JSON to SQL ✅ Supports CLI and programmatic use ✅ Is completely free and open source Since using it in my own projects, I’ve: ⏱️ Cut import time from hours to minutes 🧹 Eliminated manual errors 📈 Scaled to handle larger datasets 💡 Made data imports reproducible and painless This project reminded me that sometimes the best solution is the one you build yourself. And by making it open source, I hope it helps others streamline their data workflows too. Check it out on GitHub via https://lnkd.in/d8G3p5PG or install via npm: https://lnkd.in/dAUBREeu Curious to hear how others are solving similar challenges. What tools or tricks have helped you tame your data chaos? #TypeScript #OpenSource #DataEngineering #SQL #Excel #DeveloperTools #npm #GitHub #RidbayBuilds

📌 #Day16 of my SQL Learning Journey — Subqueries Today I practiced subqueries: nesting queries inside queries to solve problems step by step. I used a simple subquery to find employees earning above the company average. What are subqueries? A subquery (or nested query) is a query inside another query. Use them to compute values, filter results, or check existence — letting you break complex problems into smaller steps. Example 1 — Simple (non-correlated) subquery Problem: Return employees who earn more than the company average. Step 1 (inner): compute average salary. Step 2 (outer): get employees above that value. Quick tips & when to use 1.Use simple subqueries to compute a single value (AVG, COUNT) used in the outer query. 2.Use IN when inner query returns multiple values to match against. 3.Use correlated subqueries when you need row-by-row context (but be mindful of performance). 4.When performance matters, consider rewriting as a JOIN with GROUP BY or CTE — especially for large datasets. Subqueries help me break complex questions into smaller, logical steps — and that makes SQL feel a lot more powerful. 🚀 #Day16 #SQL #Subquery #LearningJourney #DataSkills #Upskilling

📊 Day 8 of #100DaysOfSQL 💻 Yesterday was all about confusion slowly turning into clarity I spent time solving SQL problems on LeetCode — one about finding employees who earn more than their managers (which helped me understand self joins properly), and another involving pattern matching using the LIKE operator. Takeaways: 1. I first wrote LIKE 'DIAB1%' OR '% DIAB1', but SQL only checks what comes after each LIKE, not after OR. I learned it should be LIKE 'DIAB1%' OR LIKE '% DIAB1%' — SQL needs each pattern to have its own LIKE. 2. In the self-join problem, I initially compared salary > managerId instead of comparing the employee’s salary with the manager’s (e.salary > s.salary). Didn't solve much problems today but Day 8 reminded me to always slow down and visualize table relationships before writing joins:) #100DaysOfSQL #SQL #LearningInPublic #ProblemSolving #Leetcode #Day8

Documentation in Business Intelligence (Dashboarding) is also just as important. A lot of analysts do not seem to realize that documentation of metrics is just as important as documenting programming code. You will never be in a team forever, and your report will have to be taken over by someone else in the future. How do you make life easy for your successor? By documenting your "transformation steps" and the "Logics" you create. In Power Query, it's as easy as: 1. Clicking into your table of interest 2. "Right clicking" on the Transformation step of choice 3. Select properties and in the window 4. Write a description of the step. #dataanalytics, #businessintelligence, #Powerbi, #analytics, #powerquery

📌 SQL NTILE Function Explained With Practical Examples NTILE in SQL transforms raw lists into structured distributions before they reach a dashboard. By pushing distribution logic upstream, it divides ordered rows at the query level, so the insights in Power BI, Tableau, or Excel are accurate, not just polished visuals. 🔗 This guide explores its syntax, practical examples, and comparisons with other ranking functions: https://lnkd.in/eZBffW7w It also highlights how database IDEs, like dbForge Edge, bring NTILE insights directly into analysis and reporting workflows. Ready to put NTILE into practice? ✅ Download a 30-day free trial of AI-powered dbForge Edge: https://lnkd.in/dzRKBi9T    Such features as visual SQL editor, data grid, and reporting let you design NTILE queries quickly, inspect tile assignments interactively, and push results into BI workflows without friction. #dbForge #Devart #dbForgeEdge #dbForgeTools #SQLTools #SQLManagement #SQLDevelopment #DBA #DatabaseManagement #DatabaseTools