Hany Ouf

Important characteristics of value objects Value object implementation in C# How to persist value objects in the database with EF Core 2.0 and later Persist value objects as owned entity types in EF Core 2.0 and later Additional resources  Tip This content is an excerpt from the eBook, .NET Microservices Architecture for Containerized .NET Applications, available on .NET Docs or as a free downloadable PDF that can be read offline. .NET Microservices Architecture for Containerized .NET Applications eBook cover thumbnail. As discussed in earlier sections about entities and aggregates, identity is fundamental for entities. However, there are many objects and data items in a system that do not require an identity and identity tracking, such as value objects. A value object can reference other entities. For example, in an application that generates a route that describes how to get from one point to another, that route would be a value object. It would be a snapshot of points on a speci...

في أواخر 2023 وبداية 2024، Scrum.org (بقيادة كين شوابر، واحد من مؤسسي السكرام) أطلقت الـ Expansion Pack دي مش عشان تغير السكرام، لكن عشان "تدعمه" بقوة الذكاء الاصطناعي. الفكرة مش إننا نغير الـGuid، الفكرة إننا نغير "الطريقة". إليك التفاصيل: 1. مفهوم "الذكاء الاصطناعي كعضو فريق" (The AI Teammate) الـ Expansion Pack بتدفعنا لمرحلة إن الـ AI ميبقاش مجرد "أداة" زي الـ Calculator، بل Digital Collaborator.   * في الـ Daily Scrum : ممكن الـ AI يكون هو اللي بيحلل الـ Burndown Chart ويقول للفريق: "يا شباب، بالسرعة دي إحنا مش هنخلص الـ Sprint Goal، محتاجين نتحرك في التاسك الفلاني".   * التكامل: بيعلم الفرق إزاي يعملوا "Onboarding" للـ AI كأنه موظف جديد، له صلاحيات وله حدود. 2. تسريع الـ Feedback Loops (السرعة القصوى) جوهر الـ Agile هو إننا نتعلم بسرعة . الـ Expansion Pack ركزت على إن الـ AI بيخلي الـ Sprint نفسه أسرع:  * Backlog Refinement: الـ AI بيساعد الـ Product Owner إنه يحول "أفكار العميل المشوشة" لـ User Stories واضح...

Data exploration: An introduction for data analysts Data exploration is the foundational phase of data analysis, where you familiarize yourself with your dataset. It's about understanding its structure, identifying potential issues, and beginning to formulate questions for deeper investigation. Data exploration encompasses a diverse range of activities, each designed to reveal different aspects of your dataset. These activities can be broadly categorized into three core areas: understanding your data, uncovering relationships, and formulating hypotheses. Understanding your data This phase involves getting familiar with the individual variables and their characteristics within your dataset. The first step is to identify the types of variables you're working with. Are they numerical (continuous or discrete) or categorical (nominal or ordinal)? Understanding the nature of your variables is fundamental for choosing appropriate analysis techniques and visualizations. Next, you'l...