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Published: 2024-02-10 11:55:13


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Tags: webdev, javascript
Published: 2024-02-10 11:39:38

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Tags: corewebvital, ecommerce, highranking, success
Published: 2024-02-10 11:25:44

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Tags: web, development, webdevelop
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Published: 2024-02-10 10:55:46

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هاست ابری چیست؟ cloud hostingبا هاست اشتراکی چه تفاوتی دارد
Tags: cloudcomputing, host, hosting, cloud
Published: 2024-02-10 10:52:58

[هاست ابری](https://tokanweb.com/cloud-hosting/) (Cloud Hosting) یک نوع خدمات هاستینگ است که بر روی یک زیرساخت ابری برپایه منابع مجازی ساخته شده است. این نوع هاستینگ برای میزبانی وب‌سایت‌ها و برنامه‌های اینترنتی استفاده می‌شود و از منابعی همچون پردازنده، حافظه، و فضای ذخیره‌سازی ابری استفاده می‌کند که بر روی یک شبکه از سرورهای فیزیکی قرار دارند. این سرویس به کاربران امکان می‌دهد تا به راحتی و با انعطاف‌پذیری بیشتر منابع مورد نیاز خود را مدیریت کنند و در مواجهه با نیازهای متغیر ترافیک وب‌سایت خود را بهبود دهند.

تفاوت اصلی میان **هاست ابری** و **هاست اشتراکی** در معماری زیرساخت و استفاده از منابع فیزیکی است. در هاست اشتراکی، چندین وب‌سایت بر روی یک سرور فیزیکی مشترک قرار می‌گیرند و منابع آن سرور (مانند پردازنده و حافظه) بین آن‌ها تقسیم می‌شود. این معماری به دلیل هزینه کمتر منابع محدود می‌تواند باعث محدودیت در عملکرد وب‌سایت‌ها شود.

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Published: 2024-02-10 10:50:52

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Published: 2024-02-10 10:29:19

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Difference Between Raw SQL and Dapper
Tags: csharp
Published: 2024-02-10 09:47:46

C# is a versatile programming language that allows developers to interact with various databases. When working with databases, developers often have to deal with writing SQL queries to retrieve or manipulate data. In this post, we will explore the difference between using raw SQL queries and using Dapper, a popular Micro ORM (Object-Relational Mapping) tool in C#.

Raw SQL:
Raw SQL refers to the traditional approach of writing SQL queries directly in our C# code using string literals. For example, let's say we have a table called "Employees" in our database, and we want to retrieve all employees with a specific name. Here's how we would write a raw SQL query to achieve that:

```csharp
string query = "SELECT * FROM Employees WHERE Name = 'John Doe'";
```

While raw SQL queries are straightforward, they have some limitations. One major drawback is that they can be prone to SQL injection attacks if not properly sanitized or parameterized. Additionally, raw SQL queries can become quite lengthy and cluttered, especially when dealing with complex queries involving multiple joins or subqueries.

Dapper:
Dapper, on the other hand, is a lightweight and high-performance ORM tool popularly used by C# developers. It simplifies the data access layer by allowing developers to map SQL query results directly to .NET objects without requiring any complex configuration or mapping files.

To compare with our previous example, here's how we would use Dapper to retrieve all employees with a specific name:

```csharp
string name = "John Doe";
string query = "SELECT * FROM Employees WHERE Name = @Name";

using (var connection = new SqlConnection(connectionString))
{
    var result = connection.Query<Employee>(query, new { Name = name }).ToList();
}
```

In the above code snippet, we first define our SQL query with a parameterized placeholder (`@Name`). Then, we simply pass the query and a parameterized object to Dapper's `Query` method, where the `Employee` class represents our data model. Dapper takes care of parameterizing the query and mapping the result set to a list of `Employee` objects automatically.

As we can see from the Dapper code snippet, it provides several benefits over raw SQL queries. Firstly, Dapper helps prevent SQL injection attacks by implementing proper parameterization. Secondly, it significantly reduces the amount of boilerplate code needed for mapping query results to objects, allowing for cleaner and more maintainable code. Moreover, Dapper is highly performant, as it utilizes a fast mapping algorithm and minimizes overhead compared to larger and heavier ORM tools.

In conclusion, using Dapper in C# provides a more convenient and efficient way of interacting with databases compared to writing raw SQL queries. Its simplicity, performance, and ability to handle complex mapping scenarios make it a popular choice for data access in C# applications.
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Mastering the Google Professional Cloud DevOps Engineer Exam: A Comprehensive Guide
Published: 2024-02-10 07:29:18

In the ever-evolving landscape of cloud computing, the demand for skilled professionals who can effectively navigate and optimize cloud environments continues to soar. Google Cloud Platform (GCP) stands at the forefront of this revolution, offering powerful tools and services tailored for modern DevOps practices. The **[Google Professional Cloud DevOps Engineer certification](https://www.dumpsbuddy.com/professional-cloud-devops-engineer-certification-exam.html)** validates the expertise required to leverage GCP tools for continuous integration, delivery, monitoring, and management of services.

Understanding the Google Professional Cloud DevOps Engineer Exam
The Google Professional Cloud DevOps Engineer exam is designed to assess the candidate's proficiency in designing and implementing DevOps practices on Google Cloud Platform. It evaluates various aspects of DevOps, including:

Continuous Integration and Delivery (CI/CD): Candidates are expected to demonstrate their ability to design and implement CI/CD pipelines using tools like Cloud Build, Jenkins, or GitLab on GCP.
Infrastructure as Code (IaC): Knowledge of Infrastructure as Code principles and proficiency in using tools such as Terraform or Deployment Manager to automate infrastructure provisioning and configuration.
Monitoring and Logging: Competence in setting up monitoring and logging solutions on GCP using tools like Stackdriver, Prometheus, and Grafana to ensure the reliability and performance of cloud services.
Site Reliability Engineering (SRE): Understanding of SRE principles and practices for designing highly available, scalable, and resilient cloud applications.
Security and Compliance: Familiarity with security best practices and compliance standards relevant to cloud environments, including IAM roles and permissions, VPC networking, and data encryption.
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DumpsMedia: Shaping the Minds of Today for Tomorrow
Published: 2024-02-10 10:09:43

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Navigating AMA PCM: A Comprehensive Tutorial
Published: 2024-02-10 10:09:01


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PRIVATE JETS CONSULTANTS
Tags: a
Published: 2024-02-10 10:05:46

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Can Expert Systems Help Me Make Better Decisions?
Tags: ai, machinelearning, deeplearning
Published: 2024-02-10 10:05:35

**Advantages of using expert systems for decision-making:
**
Access to specialized knowledge: They can be programmed with vast amounts of knowledge and data related to a specific domain, providing insights you might not have considered.
Consistent and unbiased: They analyze information based on their programmed rules, reducing the likelihood of bias or emotional influence in your decision-making.

Understand [What is Expert System In AI](https://www.javatpoint.com/expert-systems-in-artificial-intelligence)

**Speed and efficiency:
**
 They can process information and suggest solutions much faster than humans, especially for repetitive tasks.
Simulation and modeling: Some systems can create simulations of possible outcomes based on different choices, helping you visualize potential consequences.

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**Limitations of expert systems:
**
Limited scope: They are designed for specific problems and may not be adaptable to situations outside their domain.
Overreliance on rules: They struggle with complex, nuanced situations where rules don't apply perfectly, potentially leading to inaccurate recommendations.
Black box problem: In some cases, it's hard to understand the reasoning behind the system's decision, making it difficult to assess its validity.
Ethical considerations: Biases within the data or programming can lead to discriminatory or unfair outcomes.

**Overall:
**
Expert systems can be valuable tools for supplementary information and analysis, but they shouldn't be used as sole decision-makers. Here's how to use them effectively:
Clearly define the problem and ensure the system aligns with it.
Understand the system's limitations and don't blindly trust its recommendations.
Use your own judgment and critical thinking to evaluate the system's output.
Combine the system's suggestions with other sources of information and perspectives.