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CONFIGURING SEMANTIC MODEL IN POWER BI

INTRODUCTION Configuring a Power BI semantic model involves refining data structures, creating relationships, and setting up calculations. Semantic model is the last stop in the data pipeline before reports and dashboards are built. It is the end product of the raw data that has been extracted, transformed, loaded, modeled, built relationship, and written calculation. The Semantic model consist of Data connections to one or more data sources, Transformations that clean and prepare the data for reporting, Defined calculations and metrics based on business rules to ensure consistent reports and Defined relationships between tables. Key words to note in Semantic Modelling are; 1. Fact table and Dimension table: The Fact table records the quantitative and numerical data. It is where every single details are recorded. The Dimension table act as the descriptive companion to the fact table, containing the attributes or characteristics that provide context to the data. 2. Primary and Foreign Key: Primary Keys are unique identifier assigned to a specific record with a database table ensuring that no two rows are identical or repeated. foreign Keys are columns or group of columns in one table that provides a link between data in two tables by referencing the primary key of another. 3. Star Schema Star Schema is a data modeling technique where a central fact table is surrounded by several dimension tables that provide descriptive content. 4. Cardinality Cardinality defines the kind of relationship between two tables. They are; One to Many (1.*) Many to one (*.1) One to One (1.1) Many to Many ( . ) The cardinality of a relationship is described by the "one" (1) or "many" (*) icons located at the ends of the relationship line. 5. Cross Filter Direction The direction determine how filters propagate. Possible cross filter options are dependent on the relationship cardinality type. One to Many - Single or Both sides One to One - Both sides Many to Many - Single to either table or b

2026-05-31 原文 →
开发者

Microsoft is threatening legal action for disclosing exploits

Microsoft is facing criticism for its handling of zero-day exploits. Someone going by the name Nightmare Eclipse has been publicly feuding with the company, posting proof-of-concept exploit code. Some of their posts suggest that they're a disgruntled former employee. But what caught cyber security researcher Kevin Beaumont's eye was how Microsoft has responded. Microsoft suggests […]

2026-05-30 原文 →
开发者

Microsoft teases new Surface hardware and ‘a new era of PC’

I pondered the other day what's next for Microsoft's Surface PC lineup, and it looks like we're about to find out. Windows and Surface chief Pavan Davuluri has just teased "something new is coming for developers," complete with a mysterious image of what looks like a curved display edge. Davuluri notes that whatever is coming […]

2026-05-30 原文 →
AI 资讯

Microsoft 365 Copilot gets a speed boost and cleaner design

Microsoft is launching a revamped version of Microsoft 365 Copilot, offering a cleaner design that the company claims loads twice as fast. As part of this update, Copilot will provide more reliable and structured responses that are easier to scan, according to Microsoft. The redesign, which is rolling out across desktop and mobile devices, comes […]

2026-05-29 原文 →
开发者

What’s next for Microsoft’s Surface PCs?

Nearly 10 years ago I reviewed my favorite Surface device. Microsoft hand-delivered its Surface Studio all-in-one PC to me, and I was hooked from the moment I switched it on. It had a beautiful floating touchscreen that you could push all the way down into a drawing board mode, making it unlike anything I had […]

2026-05-29 原文 →
开发者

Meet the G2 Nano: A 1GHz Dev Board Built for Robotics

What if a development board could be as friendly as an Arduino, yet powerful enough to drive industrial-grade robots? That is exactly the gap the new G2 Nano sets out to close. Most hobby boards handle simple robot builds with ease, but they hit a wall once a project demands tight, simultaneous control of several motors. Embedded systems engineer Ryan Strace noticed that the custom controllers built for these complex machines tend to look remarkably alike, with motor coordination as the recurring headache. Rather than reinventing that hardware on every project, he designed a single accessible platform to handle it, and the G2 Nano is the result. Precise motor control usually leans on closed-loop techniques like PID, but real-world gremlins such as integrator windup, sensor noise, mechanical saturation, and phase delay can all degrade performance. Robots also need smooth multi-axis motion with managed acceleration to avoid jerky, stressful movement, plus solid fault handling so an unexpected state does not wreck expensive parts. Strace is tackling all of this with a low-cost motion-control IC he is developing, and the G2 Nano is the high-performance platform built to prove out that future chip. What's under the hood Processor: NXP Arm Cortex-M7 clocked at a brisk 1 GHz Wireless: u-blox MAYA-W1 module with dual-band Wi-Fi and Bluetooth Motion sensing: six-axis IMU (3-axis accelerometer plus 3-axis gyroscope) and a dedicated magnetometer for compass heading Form factor: just 0.8 by 3 inches, breadboard-friendly, on a six-layer PCB stackup for clean high-speed signals On the software side, the board targets native micro-ROS and the Zephyr real-time operating system, with planned MicroPython support so you can prototype in Python without paying the usual speed penalty, thanks to that unusually high clock. Every design file and document is open-source and published on GitHub. Build it yourself If you want to follow along, the core ingredients are clear: an NXP Cortex-M7 a

2026-05-28 原文 →
AI 资讯

Microsoft Introduces MDASH for Large-Scale AI Vulnerability Research

Microsoft has introduced a new AI-driven vulnerability discovery system called MDASH, a multi-model agentic security platform designed to automate large-scale code auditing across Windows and other Microsoft software environments. The system combines more than 100 specialized AI agents that work together to scan, validate, debate, and prove vulnerabilities across complex codebases. By Robert Krzaczyński

2026-05-26 原文 →