# Model (data handling)
class UserModel:
    def get_user(self, user_id):
        # Code to retrieve user from the database
        pass

# View (presentation)
class UserView:
    def render_user(self, user):
        # Code to render user data on the screen
        pass

# Controller (business logic)
class UserController:
    def __init__(self):
        self.model = UserModel()
        self.view = UserView()

    def show_user(self, user_id):
        user = self.model.get_user(user_id)
        self.view.render_user(user)​

In this example, responsibilities are clearly separated: UserModel handles the data, UserView manages presentation, and UserController handles business logic and the interaction between Model and View.

Conclusion

Separation of Concerns is an essential principle in software development that helps improve the structure and organization of code. By clearly separating responsibilities, software becomes easier to understand, maintain, and extend, ultimately leading to higher quality and efficiency in development.

A web application firewall (WAF) is a security solution that has been specially developed to protect web applications. It monitors traffic between web browsers and web applications to detect and block potentially harmful or unwanted activity. Essentially, a WAF acts as a shield that protects web applications from a variety of attacks, including

1. SQL injection: an attack technique where attackers inject malicious SQL queries to access or manipulate the database.
2. Cross-site scripting (XSS): An attack method where attackers inject scripts into websites to compromise users, such as by stealing session cookies or performing malicious actions on the user's behalf.
3. Cross-site request forgery (CSRF): An attack in which an attacker makes a fraudulent request on behalf of an authenticated user to perform unwanted actions.
4. Brute force attacks: Repeated attempts to log into a system using stolen or guessed credentials.
5. Distributed Denial of Service (DDoS): Attacks in which a large number of requests are sent to a web application in order to overload it and make it inaccessible.
   
   A WAF analyzes HTTP and HTTPS traffic and applies specific rules and filters to identify and block suspicious activity. It can be implemented both at server level and as a cloud-based solution and is an important part of a comprehensive security strategy for web applications.

The ELK Stack refers to a combination of three open-source tools for log management and data analysis: Elasticsearch, Logstash, and Kibana. These tools are often used together to collect, analyze, and visualize logs from various sources.

Here's a brief overview of each tool in the ELK Stack:

1. Elasticsearch: Elasticsearch is a distributed, document-oriented search engine and analytics engine. It is used to store and index large amounts of data, allowing it to be quickly searched and retrieved. Elasticsearch forms the core of the ELK Stack, providing the database and search capabilities for log processing.

2. Logstash: Logstash is a data processing pipeline designed for collecting, transforming, and forwarding log data. It can ingest data from various sources such as log files, databases, network protocols, etc., standardize it, and transform it into the desired format before sending it to Elasticsearch for storage and indexing.

3. Kibana: Kibana is a powerful open-source data visualization tool specifically designed to work with Elasticsearch. With Kibana, users can index and search data in Elasticsearch to create custom dashboards, charts, and visualizations. It enables real-time data visualization and provides a user-friendly interface for interacting with the data in the Elasticsearch cluster.

The ELK Stack is commonly used for centralized log management, application and system monitoring, security analysis, error tracking, and operational intelligence. The combination of these tools provides a comprehensive solution for capturing, analyzing, and visualizing data from various sources.