How to use SQL NTILE function with examples and dbForge Edge

💡 How to Use the DAX FILTER Function for Data Filtering and Context Control DAX (Data Analysis Expressions) is the engine behind Power BI, Excel Power Pivot, and SQL Server Analysis Services (SSAS).  But at its core, DAX is a context-driven language. Every calculation relies on two essential contexts: row context (the current row under evaluation) and filter context (the subset of data visible to the calculation). Failing to grasp how these interact is one of the most common causes of unexpected or incorrect results in Power BI reports. 🔗 This guide breaks down the syntax of the DAX FILTER function, its advanced use cases, and key function combinations: https://lnkd.in/ezaMcmbq  To use DAX FILTER effectively, ensure your SQL Server data models are well-prepared and validated. Many analysts use SQL Server IDEs, such as dbForge Studio for SQL Server, to clean schemas, test queries, and verify data integrity before applying DAX measures. ✅ Download the AI-powered dbForge Studio for SQL Server for a free 30-day trial: https://lnkd.in/ga7QZSgm With a solid SQL foundation, FILTER becomes a precision tool for controlling context and delivering accurate insights across Power BI dashboards. #dbForge #Devart #dbForgeStudio #SQLServer #SQLServerIDE #SQLTips #SQLCoding #SQLTutorial #DataAnalysis #SQL 

SQL at a Glance! Whether you’re just starting with SQL or brushing up your skills, this chart is a great snapshot of how SQL commands are structured and categorized 👇 🔹 DQL (Data Query Language) – Extract data using SELECT, WHERE, JOIN, and more. 🔹 DML (Data Manipulation Language) – Manage your data with INSERT, UPDATE, and DELETE. 🔹 DDL (Data Definition Language) – Define and modify database structures using CREATE, ALTER, and DROP. 🔹 DCL (Data Control Language) – Handle permissions with GRANT and REVOKE. 🔹 Functions & Window Functions – Perform calculations, rankings, and aggregations to uncover insights. This visual makes it easy to see how SQL ties together — from querying to managing and analyzing data. #SQL #DataAnalytics #Database #LearningSQL #DataScience #DataVisualization #PowerBI #CareerGrowth

SQL vs DAX – LEFT ANTI JOIN Concept Simplified! While working with SQL and Power BI, I explored how both handle the same logic differently. In SQL: We use a LEFT JOIN + WHERE condition to get records from one table that don’t exist in another. SELECT a.* FROM TableA a LEFT JOIN TableB b ON a.ID = b.ID WHERE b.ID IS NULL; This gives us all records from TableA that have no match in TableB. In Power BI (DAX): We can achieve the same using the EXCEPT() function — which acts like a LEFT ANTI JOIN. Result = EXCEPT(SELECTCOLUMNS(TableA, "ID", TableA[ID]), SELECTCOLUMNS(TableB, "ID", TableB[ID])) Both approaches return unmatched rows — one in SQL syntax, the other in DAX logic. #SQL #PowerBI #DAX #DataAnalytics #LearningJourney #DataAnalyst

✨ Day 24 of 30✨ ❓ Question: How does SQL fit into SFMC and where do you use it? ✅ Answer: SQL is how you shape the audience. You use it to pick who qualifies, clean noisy data, and enrich tables before a send. Where it runs Automation Studio → Query Activity. Your SELECT writes into a target Data Extension you set up. What it is great for Building segments from multiple tables De-duping and fixing keys Rolling up behavior into simple flags like high_intent = 1 Creating send-ready DEs on a schedule How to stay safe Create a staging DE first, then promote to the live one Match column names and data types exactly Choose the right write mode: Overwrite for full rebuilds, Append for logs, Update when matching on a key Add a Verification step to block empty or oversized results Log run time, row counts, and status to a small monitoring DE Mental model. SQL decides who. Journeys and Studios decide when and how. In the image you can see a basic query execution in Query Studio. 🔥 Tomorrow (Day 25): How to create a Triggered Send Data Extension (TSDE) in SFMC? #SFMC #SalesforceMarketingCloud #SQL #QueryStudio

🔍 Common Table Expressions (CTEs) vs Temporary Tables: What’s the difference? Let's dive in! Common Table Expressions (CTEs): Definition: A temporary result set defined within the execution scope of a single SELECT, INSERT, UPDATE, or DELETE statement. Scope: Limited to the statement in which they are defined. Readability: Improve query readability, making complex queries easier to manage. Performance: Can be optimized by the SQL engine; however, they might be less efficient for large datasets compared to temp tables. Temporary Tables: Definition: A physical table that exists temporarily in the database and can store data across multiple statements. Scope: Accessible throughout the session or until explicitly dropped. Flexibility: More flexible for complex data manipulation and can have indexes. Performance: Better for large datasets or when you need to perform multiple operations on the same data. When to Use Which? Use CTEs for simpler, single-query operations where readability is key. Use Temp Tables for complex operations or when dealing with large datasets that require multiple manipulations. 💡 Tip: Always consider the specific use case and performance implications of your choice! #CTE #TemporaryTables #LakkiData #LearningSteps

🚀 Scenario #19 — Retrieve Discount % from Another Table using LOOKUPVALUE() 🧩 Goal: You need to bring the Discount % from the Discount Table into the Product Table based on matching Code. 🧠 Requirement: Create a calculated column in the Product Table that displays the Discount % from the Discount Table based on matching Code. 🧮 DAX Formula In Product Table, create a new calculated column: Discount % = LOOKUPVALUE( 'Discount Table'[Discount %], -- Result column 'Discount Table'[Code], -- Search column 'Product Table'[Code] -- Matching column ) #PowerBI #DataAnalytics #PowerBIDeveloper #BusinessIntelligence #DAX #DataAnalyst #DashboardDesign #PowerQuery #SQL #MicrosoftPowerBI #DataVisualization #DataModeling #DataDriven #DataProjects #DataCommunity #PowerBIService

Caption: My personal goal this month was to solidify my understanding of advanced SQL concepts. I compiled this detailed reference sheet covering everything from the basics to Window Functions and CTEs! ✅ I'm posting this to hold myself accountable for continuous learning and to share a resource that breaks down key SQL topics: DDL/DML commands, Joins, Aggregate Functions, and more. Feel free to save the PDF and let me know if you find it helpful! Happy querying! #CareerGrowth #SQLskills #TechLearning #DatabaseManagement #LinkedInPost

The process involves selecting a data source and navigating a familiar flow, akin to desktop environments. Data is brought in, tables are chosen, transformations are applied, and modeling takes place. The field, appropriately named 'Power Query,' facilitates fine-tuning queries and performing data preparation and ETL operations. One can select tables and then transform data, which brings in the Power Query Editor experience. #dataModeling #powerQuery #ETL #dataTransformation https://lnkd.in/g2jdyxTm

Ever forgotten a semicolon in SQL and wondered why your command wasn't executing? Day 2 of my SQL journey was all about those "aha!" moments - understanding the query buffer, learning why consistency in naming matters, and discovering cleaner ways to work with data. Today's highlights: → Mastered INSERT operations (goodbye repetitive single inserts!) → Learned proper SQL naming conventions (snake_case for life) → Discovered the \i command for executing SQL files → Got comfortable with SELECT, ORDER BY, and DISTINCT The article includes practical examples you can run immediately, plus best practices I've learnt. Check it out here: https://lnkd.in/dQaGeYsH I am learning it here: https://lnkd.in/d4c4WhkW from Amigoscode by Nelson Djalo What's one SQL concept that finally "clicked" for you? Let me know in the comments! #SQL