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 23, 2023 | Uncategorized
Big Query is a advanced and multi-cloud data warehouse designed by Google, created with the aim of business flexibility.
What do you need to know about Big Query?
Big Query is a scalable data warehouse (cloud data warehouse) designed by Google, a global giant known by almost every internet user. What are the areas of application for this solution? It must be admitted that Big Query offers many possibilities.
Big Query allows for handling millions of queries and conducting advanced analysis of significant amounts of data in SQL language. Knowledge of this language is quite important here. At the same time, entities that use this solution do not have to worry about high costs associated with maintaining advanced technological infrastructure or scaling or balancing traffic. Google offers all new customers $300 of free funds that can be spent on Big Query. Additionally, all customers receive completely free 10 GB of storage space and even 1 TB of queries per month.
Data can be quickly uploaded or downloaded from Big Query and then analyzed in detail. We only pay for the data that we analyze, and only after exceeding the aforementioned 1 TB limit. To use Big Query, we do not have to invest in expensive equipment and tools and technologies, while the configuration is exceptionally simple.
What are the main advantages of Big Query data warehouse?
Big Query is one of the most popular data warehouses. This is mainly due to the fact that it offers a wide range of features, which are appreciated by thousands of data processing and analysis entities worldwide.
The most important advantages of the Big Query data warehouse are as follows:
- No need to invest in your own server – all data is stored in cloud technology.
- Data analysis using Big Query is fast and efficient. Big Query data warehouse stands out for its ability to analyze large amounts of data significantly faster than traditional databases. One petabyte is processed in about 3 minutes, and one terabyte in just a few seconds. This fast operation time ensures that, regardless of how much data we have to analyze, we will get results at a rapid pace. Data analysis is performed in real-time, and all changes can be observed in real-time.
- Full control over costs. In Big Query, we only pay when the number of analyzed data exceeds 1 TB per month. This billing model gives us full control over the expenses. If we do not use the tool at all or do not exceed the specified limit, we will not pay a penny.
- BigQuery offers a machine learning feature. Big Query ML feature enables creation and development of machine learning capabilities using classic SQL queries. This tool allows you to check trends, which helps to design a long-term strategy for the company in specific areas.
- The Big Query data warehouse can be invaluable in any industry. The need for fast and efficient information analysis is visible in many industries – finance, industry, marketing, logistics, etc. Therefore, any company that wants to gain significant competitive advantages should consider using it.
Why choose Big Query data warehouse?
Year after year, more and more entities analyzing significant amounts of data are choosing Google Big Query. This is because with this solution, we do not have to invest in modern equipment, manage infrastructure, perform configuration or software updates. Google engineers are responsible for ensuring proper tool operation. We can then focus on proper analysis and data collection.
To be able to use the possibilities offered by Big Query, we do not have to make major changes or rewrite the source code. This is because Big Query supports the ANSI SQL:2011 standard and also offers ODBC and JDBC programming interfaces for free.
In Big Query, we also do not have to worry about creating backups – the program performs backups on its own, which are later stored for 7 days. During this time, we can familiarize ourselves with the entire history of changes and, if necessary, restore one of the previous versions.
Big Query also has a very high level of security – the tool is known for its reliable security, management, and reliability mechanisms. All data stored in the program is encrypted by default. Google states on its website that it guarantees 99.99% uptime.
Limitations of Big Query data warehouse
Big Query data warehouse has certain limitations and limits in terms of information processing. The most important of these are as follows:
- Maximum number of exported bytes per day. The limit is 50 terabytes per day.
- Maximum number of exports per day. The limit is 100,000 exports per day.
- Number of daily queries. There are no limits on the number of bytes that can be processed in queries within a given project.
- Number of daily queries per user. There are no limits on the number of bytes that users can process in queries per day.
- Number of bytes of processed query data per hour. The limit is 1 terabyte per hour.
- Maximum size of a single table. The limit is 10 terabytes.
- Maximum size of a single partition. The limit is 4 terabytes.
- Maximum number of columns in a table. The limit is 10,000 columns.
- Maximum number of rows in a table. The limit is 1 trillion rows.
- Maximum number of partitions in a table. The limit is 10,000 partitions.
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by Teknita Team | Jan 20, 2023 | Uncategorized
The Java Virtual Machine (JVM) is an abstract computing machine that enables a computer to run Java programs. It is a virtual machine that provides a runtime environment for executing Java bytecode, which is a program compiled from Java source code. The JVM acts as a “layer” between the Java code and the underlying hardware and operating system, allowing Java programs to be run on any platform that has a JVM implementation. This makes Java a “platform-independent” programming language.
To use the Java Virtual Machine (JVM), you first need to have the Java Development Kit (JDK) installed on your computer. The JDK includes the JVM, as well as other tools necessary for developing and running Java programs.
Once you have the JDK installed, you can use the JVM by doing the following:
- Write your Java code using a text editor or an Integrated Development Environment (IDE) such as Eclipse or IntelliJ IDEA.
- Compile your Java code using the Java compiler (javac) that comes with the JDK. This will convert your source code into bytecode, which can be executed by the JVM.
- Run your bytecode using the Java interpreter (java) that comes with the JDK. This will start the JVM and execute your bytecode on the machine.
It’s important to note that the JVM is not just for running Java code, it is also used for running other JVM-based languages like Kotlin, Scala, and Groovy.
You should use the Java Virtual Machine (JVM) when you want to run Java or other JVM-based programming languages, such as Kotlin, Scala, or Groovy, on your computer. JVM provides a runtime environment for executing Java bytecode, which is a program compiled from Java source code.
Here are some specific scenarios where you might use the JVM:
- When you want to write cross-platform software that can run on any operating system, such as Windows, macOS, or Linux, that has a JVM implementation.
- When you want to write server-side applications, such as web servers or backend services, that need to handle multiple concurrent connections and perform complex computations.
- When you want to use the vast ecosystem of Java libraries and frameworks, such as Spring, Hibernate, or Apache Tomcat, that are available for various application domains.
- When you want to write code that can leverage the security, performance, and scalability features provided by the JVM, such as automatic memory management, built-in support for multithreading, and JIT compilation.
- When you want to use JVM based languages, such as Kotlin, Scala, or Groovy, that are built on top of JVM but offer features like functional programming, concise syntax, and improved type inference.
In summary, JVM is a powerful and versatile platform that allows you to develop and run a wide variety of software applications, from small scripts to enterprise-grade systems, on any platform that supports the JVM.
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by Teknita Team | Jan 12, 2023 | OpenText, Uncategorized
Valuable information is buried in emails – from your clients, sensitive projects and legal matters. It is increasingly difficult to find, organize and see the full set of relevant information lawyers and other knowledge workers need to respond quickly and stay on top of their projects and cases. An ever-growing volume of email often leads to “content chaos” – burdened email servers, increased compliance risk, such as organization’s retention policies, and the inability of employees to locate relevant content in those emails when searching for specific information.
Finding information in email drains productivity
Knowledge workers are spending more and more time each day on administrative tasks related to email organization and management; in fact, it’s estimated that 28% of their time is spent reading and sending emails. Simply stated, finding information in email drains employee productivity. Even with automated filters, email filing administration is time-consuming and overwhelming, because emails never stop pouring in and each message is accompanied by the expectation of a timely response.
OpenTextTM Email Filing, eDOCS Edition makes knowledge workers more productive with these time-savers:
Quick filing: Easily save emails to specific client folders via a button on your Microsoft Outlook ribbon or with a right mouse click – using predictive filing suggestions. Save time as suggestions are displayed based on the most recently accessed profiles and email threads.
Bulk filing: Quickly file large volumes of email without slowing productivity. With Bulk Filing, users can automate the email filing process by assigning profiling data to specific Outlook folders. All emails that are moved into the Outlook folders are automatically filed into eDOCS with the assigned profile data of the folder. Email can also be filed on mobile devices by dragging and dropping them into monitored folders.
Marking: See in Outlook when your email has been automatically stored to eDOCS document management system (DM). With Email Marker, users can color-code their emails according to filing status and profile data via their Outlook categories field. Save time by keeping track of filing status at-a-glance with this visual cue and never miss filing (or duplicate filing) again.
Email Filing Assistant: Analyze message history and inbound email addresses to suggest automatically the best place to store those emails in the eDOCS library.
You can read more about OpenTextTM Email Filing, eDOCS Edition here.
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