by Teknita Team | Feb 7, 2023 | Uncategorized
HTTP (Hypertext Transfer Protocol) is a protocol for transmitting data over the internet. It is the foundation of data communication for the World Wide Web and is used for the transfer of data from a web server to a web browser in order to display websites. HTTP is based on a request-response model, where a client makes a request to a server and the server returns a response to the client.
It works as follows:
- A client (e.g. a web browser) sends an HTTP request to a server (e.g. a web server) specifying the desired resource.
- The server processes the request and returns an HTTP response, which includes the requested resource or an error message.
- The client receives the response and renders the resource, such as a web page or image, for the user to view.
The request and response each have specific components, including a method (e.g. GET, POST), a header (which includes information such as the type of content being requested), and a body (which contains the actual data being transmitted).
These methods are used to indicate the desired action to be performed on a resource:
- GET: The GET method is used to retrieve a resource from the server. This is the most common HTTP method and is used to request data from a server.
- POST: The POST method is used to submit data to the server for processing. This method is often used to submit form data or upload a file.
- PUT: The PUT method is used to update an existing resource on the server.
- DELETE: The DELETE method is used to delete a resource from the server.
- HEAD: The HEAD method is similar to GET, but only the headers of the response are returned, without the actual resource.
- PATCH: The PATCH method is used to make partial updates to a resource.
- OPTIONS: The OPTIONS method is used to retrieve information about the communication options available for a resource.
These methods are often used in RESTful APIs to perform operations on resources.
HTTP is a stateless protocol, which means that each request and response are independent and do not maintain any information about previous requests and responses. To maintain state or track user sessions, other technologies, such as cookies or session IDs, are often used in conjunction with HTTP.
Differences Between HTTP and HTTPS
HTTPS (Hypertext Transfer Protocol Secure) is a variant of the HTTP protocol that is used for secure communication over the internet. It uses SSL/TLS encryption to protect the privacy and security of data exchanged between a client (e.g. web browser) and a server (e.g. website). When a user connects to a website via HTTPS, the website’s SSL/TLS certificate is verified and a secure, encrypted connection is established. This protects sensitive information, such as login credentials and payment information, from being intercepted and compromised by third parties.
HTTP and HTTPS are similar in that they are both used for transmitting data over the internet, but they differ in the level of security they provide:
- Security: HTTPS uses SSL/TLS encryption to secure the data transmitted between a client and a server, HTTP does not.
- Authentication: With HTTPS, the website’s SSL/TLS certificate is verified to ensure that the user is connecting to the correct website. HTTP does not provide any form of authentication.
- Privacy: HTTPS protects the privacy of the data transmitted between a client and a server, HTTP does not.
- URL: HTTPS uses the URL prefix “https://” while HTTP uses “http://”.
In summary, HTTPS is preferred over HTTP when transmitting sensitive information or when security and privacy are a concern.
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by Teknita Team | Feb 2, 2023 | Uncategorized
GitHub Copilot is a new AI-powered feature from GitHub that aims to help developers write code faster and with fewer errors. It uses advanced machine learning algorithms to provide real-time code suggestions and recommendations as users type, taking into account the context of the code and the developer’s preferences and workstyle. GitHub Copilot also integrates with other GitHub tools, such as pull requests and issues, to provide a seamless experience for developers. The goal of GitHub Copilot is to make software development more productive and efficient, by providing developers with the information and support they need to write high-quality code quickly and easily.
GitHub Copilot works by analyzing code as it is being written and using advanced machine learning algorithms to provide real-time suggestions and recommendations. It takes into account the context of the code and the developer’s preferences and workstyle to provide personalized and relevant suggestions.
Here’s how it works:
- As you write code in a GitHub repository, GitHub Copilot analyzes the code and provides suggestions in real-time.
- GitHub Copilot considers the context of the code, such as the programming language, the libraries being used, and the code structure, to provide relevant suggestions.
- The suggestions are presented in a pop-up window or as inline suggestions, allowing developers to quickly make selections and continue coding.
- GitHub Copilot also integrates with other GitHub tools, such as pull requests and issues, to provide a seamless experience for developers. For example, it can suggest relevant pull requests or issues as you work.
- The more you use GitHub Copilot, the more it learns about your preferences and workstyle, and the more personalized its suggestions become.
GitHub Copilot is designed to help developers write code faster and with fewer errors, by providing relevant and personalized suggestions at the right time. This helps to increase productivity and efficiency, and makes software development a smoother and more enjoyable experience. By using GitHub Copilot, developers can enjoy increased productivity, better code quality, and a more personalized experience, while also benefiting from the integration with other GitHub tools. This can help make software development a smoother and more enjoyable experience.
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by Teknita Team | Jan 31, 2023 | Uncategorized
A programming language is a formal language that specifies a set of instructions that can be used to produce various types of output. It is used to create computer programs that control the behavior of a machine, perform specific tasks, and process data. Programming languages can be used to create software, websites, mobile apps, and other applications.
There are many different types of programming languages, each with its own syntax and purpose, such as:
- Object-Oriented languages (e.g. Java, Python, C#)
- Procedural languages (e.g. C, Pascal)
- Scripting languages (e.g. JavaScript, Python, Perl)
- Functional languages (e.g. Haskell, Lisp)
- Low-level languages (e.g. Assembly, C)
Programming languages are designed to be used by both human programmers and computers, and can be used to create a wide range of applications, from simple scripts to complex software systems.
According the results of the international Stack Overflow 2021 study, in which tens of thousands of programmers from around the world took part, Java Script is the most popular programming language on a global scale.
JavaScript is a high-level, interpreted programming language that is primarily used for creating interactive effects within web browsers. It is a scripting language that runs on the client-side (in the browser) and enables dynamic behavior, such as interactive forms, animation, and updating content without requiring a page reload. JavaScript can also be run on the server-side using Node.js, making it a versatile language for both front-end and back-end web development.
JavaScript is the most popular due to several reasons:
- It’s flexible and can be used for web development, server-side development, and even for desktop applications.
- The widespread use of the web and the need for dynamic, interactive content has made JavaScript an essential tool for front-end web development.
- It has a large community of developers and a wealth of libraries and frameworks available, making it easier to use and allowing for quicker development.
- The language is easy to learn and relatively simple compared to other programming languages, making it accessible to a wider range of developers.
- JavaScript has good browser compatibility, meaning it runs consistently on most browsers, making it easier to create cross-platform applications.
Another language which lately gains huge popularity is Python. Python is a high-level, interpreted programming language that is used for a wide range of tasks including web development, scientific computing, data analysis, artificial intelligence, and more. It is known for its readability, easy-to-learn syntax, and support for multiple programming paradigms, including procedural, object-oriented, and functional programming. Python also has a large number of libraries and frameworks available, which makes it a popular choice for developers. It is used by companies such as Google, NASA, and IBM, among others.
JavaScript and Python are both widely used, high-level programming languages, but have some key differences:
- Purpose: JavaScript is primarily used for web development, whereas Python is used for a wider range of tasks including web development, scientific computing, data analysis, artificial intelligence, and more.
- Syntax: JavaScript syntax is based on C and Java, whereas Python has a more straightforward and readable syntax.
- Dynamically typed: JavaScript is a dynamically typed language, meaning you don’t need to declare the type of a variable, while Python is a dynamically typed language but with optional type hints.
- Performance: JavaScript is executed directly by the browser or Node.js runtime, which provides fast performance, while Python is an interpreted language and may run slower for certain tasks.
- Libraries and frameworks: Both languages have a large number of libraries and frameworks available, but Python has more libraries for scientific computing, machine learning, and data analysis. JavaScript has more libraries for web development, such as React, Angular, and Vue.
Ultimately, the choice between JavaScript and Python depends on the specific use case and personal preferences of the developer.
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by Teknita Team | Jan 30, 2023 | Security
Increasing the efficiency of processes carried out within not only the IT industry but also the SEO industry requires companies to adopt new, unexpected measures. One of them is the introduction of special roles such as blackhat and whitehat.
Blackhat refers to unethical or illegal practices in the realm of computer security or hacking, usually with malicious intent. It involves unauthorized access to computer systems or networks for personal gain or to cause harm. Examples include hacking into computer systems without permission, distributing malware, and conducting online fraud or theft.
On the other hand, whitehat is a term used in the cybersecurity industry to describe ethical hacking practices. It involves the use of hacking techniques to identify security weaknesses in a computer system or network, with the goal of improving security. Whitehat hackers are often hired by organizations to test their defenses and help prevent unauthorized access or attacks.
Greyhat refers to hacking practices that fall between ethical (whitehat) and unethical (blackhat) behavior. Greyhat hackers may not have malicious intent, but they may engage in unauthorized access to computer systems or networks without permission. This behavior can range from harmless exploration to actions that may cause harm or violate laws. Greyhat activities blur the line between ethical and unethical behavior and can sometimes result in legal consequences.
Blackhat hacking should not be used at any time, as it involves unauthorized access to computer systems or networks, distribution of malware, and online fraud or theft. Engaging in these activities can result in serious legal consequences and harm to individuals and organizations.
Instead of blackhat hacking, organizations should use ethical hacking practices. Some common use cases include:
- Penetration testing: simulating a real-world attack on a system to identify vulnerabilities and assess the strength of security measures.
- Vulnerability assessments: regularly scanning systems for security weaknesses and vulnerabilities.
- Compliance testing: ensuring that systems and networks meet industry regulations and standards for security.
- Application security testing: evaluating the security of software applications before deployment.
These activities are performed with the owner’s permission and are designed to improve the overall security of a system or network.
The key difference between blackhat and whitehat hacking lies in the intention behind the actions and the methods used. Blackhat hacking is malicious and illegal, while whitehat hacking is ethical and done with the owner’s permission.
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by Teknita Team | Jan 26, 2023 | Process Automation
Autonomous Database is a cloud-based solution that uses machine learning to automate database optimization, security, backups, updates, and other routine management tasks traditionally performed by database administrators. Unlike a conventional database, an autonomous database performs all these and other tasks without human intervention.
The amount of data available to the enterprise is growing faster and faster. This increases the demand for efficient and secure database management that enhances data security, reduces downtime, improves performance, and is not prone to human error. An autonomous database can help you achieve these goals.
Types of data stored in databases
Information stored in a database management system can be highly structured (e.g., accounting records or customer information) or unstructured (e.g., digital images or spreadsheets). Data can be accessed by customers and employees directly or indirectly through enterprise software, websites or mobile applications. Additionally, many types of software—such as business analytics, customer relationship management, and supply chain applications—use information stored in databases.
Elements of an autonomous database
The standalone database consists of two key elements that are tailored to the types of workloads.
- The data warehouse performs numerous functions related to business analytics and uses data that has been previously prepared for analysis. The data warehouse environment also manages all database lifecycle operations and can scan millions of rows for queries. They can be scaled according to business needs and implemented almost on the spot.
- Transaction processing tools enable timely handling of transactional processes such as real-time data analytics, personalization and fraud detection. Transaction processing typically involves a very small number of records, relies on predefined operations, and allows simple application development and deployment.
How an autonomous database works
The autonomous database uses AI and machine learning to provide full, end-to-end automation for provisioning, security, updates, high availability, performance, change management, and error prevention.
In this regard, an autonomous database has specific characteristics.
- It’s automatic
All database and infrastructure management, monitoring and optimization processes are automated. DBAs can now focus on more important tasks, including data aggregation, modeling, data processing and management strategies, and helping developers take advantage of the features and functions available in the database with minimal changes to the application code.
- Protects itself automatically
Built-in security protects you from both external attacks and malicious internal users. This helps eliminate the fear of cyberattacks on unpatched or unencrypted databases.
- Self-repairs
This can prevent downtime, including unscheduled maintenance. A standalone database may require less than 2.5 minutes of downtime per month, including patching .
The benefits of an autonomous database
An autonomous database provides several benefits:
- Maximum database uptime, performance and security – including automatic patching
- Elimination of manual, error-prone management tasks as a result of automation
- Lower costs and increase productivity by automating routine tasks
An autonomous database also allows an enterprise to redeploy its database management staff to more responsible tasks that deliver greater business value to the enterprise, such as modeling data, helping developers define data architecture, and planning future resource requirements. In some cases, an autonomous database can help a company reduce costs by reducing the number of DBAs needed to manage databases or by adapting them to more strategic tasks.
You can read more about Autonomous Database here.
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