Hosting services are at the heart of the current digital landscape, and they are the driving forces of the internet. A website is no longer a frivolous luxury, but rather a solid foundation from where business can expand their reach, encompassing everything from social media to the world of cloud services and apps.

Your website is the essence of your online presence, while the hosting service is the bedrock. No matter if you are a big or small business, service provider, or a freelancer, knowing crucial data on web hosting can be a game-changer, especially since it is moving the industry forward.

1.    Market Size & Share Statistics for Web Hosting

A lot of technological improvements have been made since the first website was hosted by a NeXT computer in 1989, which has given way to the current cyberspace behemoth. It is nearly impossible to find someone who isn’t using the internet in some capacity, whether it be for business, social, educational, or personal motives, which is why it is one of the most far-reaching inventions of all time. These are some key web hosting statistics you should know about:

  • According to HostAdvice, the three leading web hosts with the most users around the world are GoDaddy at 11.64%, Google Cloud Platform at 4.99%, and 1&1 at 4.34%.
  • The U.S. dominates 51.14% of web hosting market shares worldwide as of March 2021. Germany trails behind in second with 11.65%, and the UK comes in third with 4.19% (HostAdvice, 2021).
  • GoDaddy Group is at the helm of the market share with 6.6%, with Amazon in a close second place at 5.9% (Hosting Tribunal, 2020).

2.    Facts on Web Hosting

It’s been more than 30 years since the first website came online. According to TechJury, there are now approximately more than 1.8 billion websites and about 200 million of these are operational. These facts on web hosting will probably surprise you, and you’ll be glad you heard about them.

  • The total amount of internet users worldwide was about 4.66 billion in October 2020 (We Are Social, 2020).
  • About 40% of consumers state that if a site doesn’t load in under three seconds, they will leave that site (WebsiteBuilderExpert, 2021).
  • Social media users on mobile devices are the segment of internet users that show the most rapid growth (vpnMentor, 2021).
  • In the third trimester of 2020, there was a 3% increase in domain name registrations compared to the stats for the same timeframe in 2019 (Verisign, 2020).

3.    Economic effects of Web Hosting

No matter the use that the internet is given –gaming, file sharing, email messaging, research, entertainment, or education– a financial aspect is usually involved, among other factors. Some of the most important economic effects that we can share thanks to web hosting data are the following.

  • Fees for shared hosting range from $3 to $7 per month, whereas VPS hosting rates are in the range of $20-$30 per month (WebHostingSecretsRevealed, 2021).
  • Websites with lengthy loading times cost the economy of the U.S. about $500 million each year (WebsiteHostingRating, 2021).
  • In 2019, there was an 8.6% increase in people using eCommerce platforms to shop for consumer goods. There are currently 4.28 billion people who buy online (We Are Social, 2020).

4.    Key web hosting features

As with any other technology service or product, its success depends mostly on the quality of services offered. Even though users usually choose web hosts that fit their specific needs, some features are what make web hosting providers perform well and consistently in the market. These include features such as load time, uptime, and speed.

For example, taking the loading time from eight to two seconds represents an increase of 74% in conversion rates (Website Hosting Insider, 2017).

How to use these statistics when looking for a web hosting provider

Make sure to look for a provider who can provide speed, security, and support, like ServerPronto.

ServerPronto’s data centers can keep your servers up and running around the clock, guaranteed. Since the network belongs to ServerPronto, you can expect reliability and security for all of your digital assets. This also means that affordable dedicated servers and cloud hosting can be provided.

Be sure to take a look at the dedicated server packages ServerPronto has to offer.

A DNS server (Domain Name System), is a computer or a group of them connected to internet nodes, which have a database, our navigators consult regularly.
They work as a book of Internet addresses, resolve (translate) or convert domain names into IP addresses.

Not only browsers, but also mail programs when sending a message, mobile applications to operate, devices to connect to, and anything else that needs to find the address of a domain come to this server. They also have other functions.

Functions of DNS servers

Resolution of names

This term consists of returning the IP address that corresponds to a domain. Internet sites and services get identified by their numeric IP addresses, almost impossible to memorize by humans. For that reason, domain names were created. When requesting the browser for an address, it accesses the nearest DNS, which returns the IP corresponding to the requested site.

For example, when clicking on the link https://norfipc.com, we must wait  for the request to travel to the default DNS of the connection and return the result Then can the browser request the indicated page from this site. Of course, after that, this relationship is saved in the cache for a while, to speed up subsequent queries.

Inverse address resolution

It is the reverse mechanism to the previous, from an IP address get the corresponding hostname.

Resolution of mail servers

Given a domain name (for example, gmail.com), obtain the server through which the e-mail delivery should be made.

The DNS Servers store a series of data for each domain, which is known as “DNS Record”.
The registers A, AAAA, CNAME, NS, MX, among others contain the IP addresses, host names, canonical names, associated email addresses, etc.

Main Internet DNS servers

There are thousands of DNS servers located on different internet nodes. Some get managed by ISPs (Internet supplying companies), others by large companies and there are even personal DNS. Some of them have a small database and queries about sites that got not included, are “passed on” to others that are hierarchically superior.

There are 13 DNS servers on the Internet that are known as the root servers, they store the information of the servers for each of the highest level areas and constitute the center of the network. They get identified with the first seven letters of the alphabet, several of them are physically divided and geographically dispersed, a technique known as “anycast,” with the purpose of increasing performance and safety.

Delay in name resolution

When trying to access with our browser to a website that we had never been before, also little known and that is on a remote server, the request gets made to the default DNS server of our connection, which 80% of the time It’s from a telephone company.

This DNS is generally slow and with little information. The request will send it to another DNS of higher rank and so on until it succeeds.  If the application gets delayed for a certain amount of time, the browser will consider it an error and close the connection.

Errors and censorship in DNS

In addition to the slowness caused by poor quality DNS and poor performance, other factors conspire against the quality of navigation. One of them is errors in the resolution of names when it seems that the sites or internet services do not work and it does not. Another is the use of DNS to censor or block websites, an extended method in some countries.

Alternate internet DNS servers

Due to the difficulties explained above, the use of alternate servers on the Internet has become popular. They are independent services to providers, which generally offer free services, which often include the filtering of inappropriate or dangerous content, such as malware sites or adult-only content. The main ones offer much smaller response times than telephone companies, which considerably increases the quality and performance of navigation. The best known of these is the Google DNS Public Server, whose IP address is:

How to know the DNS servers of our connection

  1. Open Start, type CMD and press the Enter key to open the CMD Console or Command Prompt.
  1. In the black window write the command NSLOOKUP and press Enter again. The application will return the hostname and the IP address of the established DNS, as you can see in the following image.

Grid Computing is created to provide a solution to specific issues, such as problems that require a large number of processing cycles or access to a large amount of data. Finding hardware and software that allows these utilities to get provided commonly provides cost, security, and availability issues. In that sense, different types of machines and resources get integrated. Therefore a grid network is never obsolete, and all funds get used. If all the PCs of an office get renewed, the old and the new ones can be incorporated.

On the other hand, this technology gives companies the benefit of speed, which is a competitive advantage, which provides an improvement in the times for the production of new products and services.

Advantages and Disadvantages

It facilitates the possibility of sharing, accessing and managing information, through collaboration and operational flexibility, combining not only different technological resources but also diverse people and skills.

Regarding security in the grid, this is supported by the “intergrids,” where that security is the same as that offered by the Lan network on which grid technology gets used.

The parallelism can be seen as a problem since a parallel machine is costly. But, if we have availability of a set of heterogeneous devices of small or medium size, whose aggregate computational power is considerable, this would allow generating distributed systems of meager cost and significant computational power.

Grid computing needs different services such as the Internet, 24-hour connections, 365 days, broadband, capacity servers, computer security, VPN, firewalls, encryption, secure communications, security policies, ISO standards, and some more features … Without all these functions and features it is not possible to talk about Grid Computing.

Fault tolerance means that if one of the machines that are part of the grid collapses, the system recognizes it and the task gets forwarded to another device, which fulfills the objective of creating flexible and resistant operational infrastructures.

Applications of Grid Computing

Currently, there are five general applications for Grid Computing:

  • Super distributed computing- They are those applications whose needs can not get met in a single node. The needs occur at specific times of time and consume many resources.
  • Systems distributed in real time- They are applications that generate a flow of data at high speed that must be analyzed and processed in real time.
  • Specific services- Here we do not take into account the computing power and storage capacity but the resources that an organization can consider as not necessary. Grid presents these resources to the organization.
  • The intensive process of data- Are those applications that make great use of storage space. These types of applications overwhelm the storage capacity of a single node, and the data gets distributed throughout the grid. In addition to the benefits of the increase in space, the distribution of data along the grid allows access to them in a distributed manner.
  • Virtual collaboration environments- Area associated with the concept of Tele-immersion. So that the substantial computational resources of the grid and its distributed nature are used to generate distributed 3D virtual environments.

There are real applications that make use of mini-grids, which gets focused on the field of research in the field of physical sciences, medical and information processing. Also, there are various applications in the field of road safety. For example, this system allows translating the risk of injuring a pedestrian and the bumper resistance of a vehicle into a series of data that help design the most appropriate protection solution.

Among the first grid projects, Information Power Grid (IPG) emerged, which allows the integration and management of resources from NASA centers. The SETI @ Home project worldwide, of extra-terrestrial life research, or search for intelligent life in space, can be considered as a precursor of this technology. Although the idea of ​​Grid Computing is much more ambitious since not only, it is about sharing CPU cycles to perform complex calculations. But it is looking for the creation of a distributed computing infrastructure, with the interconnection of different networks, the definition of standards, development of procedures for the construction of applications, etc.

Computer science is, in short, the study of information (“data”), and how to manipulate it (“algorithms”) to solve problems. Mostly in theory, but sometimes also in practice.

You have to know that computer science is not the study of computers. Nor do they strictly need the use of computers. Data and algorithms can get processed with paper and pencil. Computer science is very similar to mathematics. So now many people prefer to call the subject “Computer.”

Often, computer science is confused with three fields, which are related but which are not the same.

Three Fields

  • Computer engineering- involves the study of data and algorithms but in the context of computer hardware. How do electrical components communicate? How to design microprocessors? How to implement efficient chips?
  • Software engineering- You can think of this branch as “applied computer science,” where computer scientists create abstract theories, while software engineers write real-world programs that combine theory with algorithms.
  • Information technology- This branch involves the software and hardware created so far. IT professionals help maintain networks and assist when others have problems with their devices or programs.

The disciplines of computer science

If you plan to study Computer Science, you should know that there are not two universities in the world that have the same curriculum. Universities can not agree on what “informatics” covers. Nor do they manage to decide which disciplines belong to the category of computer science.

  • Bioinformatics- It includes the use of information technology to measure, analyze and understand the complexity of biology. It involves the analysis of extensive data, molecular models and data simulators.
  • Theory of the computation- It is the study of algorithms and applied mathematics. It is not just about the creation of new algorithms or the implementation of existing algorithms. It is also about the discovery of new methods and the production of possible theorems.
  • Graphics computing- Is responsible for studying how data can get manipulated and transformed into visual representations that a human being understands. That includes themes such as realistic photo images, dynamic image generation, modeling, and 3D animation.
  • Video game development- It refers to the creation of entertainment games for PC, web or mobile devices. Graphics engines often involve unique algorithms and data structures optimized for real-time interaction.
  • Networks- Consists of the study of distributed computer systems. And how communications can get improved within and between networks.
  • Robotics- It deals with the creation of algorithms that control machines. It includes research to improve the interaction between robots and humans — interactions of robots with robots. And interactions with the environment.
  • Computer security- It deals with the development of algorithms to protect applications or software from intruders, malware or spam. It includes computer security, security in the cloud and the network.

A university degree should teach you at least the following:

  1. How computer systems work at the software and hardware level.
  1. How to write code in different programming languages.
  1. How to apply algorithms and data structures naturally.
  1. Mathematical concepts, such as graphics theory or formal logic.
  1. How to design a compiler, an operating system, and a computer.

Problem-solving is the primary skill to be developed by any computer scientist, software engineer or computer scientist. If you are not curious and you are not attracted to solving things, then you will not be pleased studying this career.

Also, technology is one of the fastest growing fields in the world so if you do not want to be at the forefront of new technologies, new programming languages, new devices, etc.

The formulas to turn enormous amounts of data into information with economic value become the great asset of the multinationals.

Algorithms are a set of programming instructions that, logically introduced in software, allow to analyze a set of previously selected data and establish an “output” or solution. These algorithms are being used by companies mainly to detect patterns or trends, and based on this, generate useful data to adapt their products or services better.

It is not a novelty for companies to obtain data from advanced analytics to study the characteristics of the product they plan to put on the market; the price to which it wants to place it or even private decisions as sensitive as the remuneration policy for its employees. The surprising thing is the dimension.

It is not only that the number of data in circulation has recently multiplied to volumes that are difficult to imagine – it is estimated that humanity has generated 90% of the information of the whole history in the last five years. The possibilities of interconnecting them have also grown dramatically.

Algorithm revolution

This revolution has contributed to each of the millions of people who give their data every day for free and continuously, either uploading a photo to Facebook, buying with a credit card or going through the metro turnstiles with a magnetic card.

In the heat of giants like Facebook and Google, who base their enormous power on the combination of data and algorithms, more and more companies are investing increasing amounts of money in everything related to big data. It is the case of BBVA, whose bet is aimed both at invisible projects for customers -as the engines that allow processing more information to analyze the needs of its users- and at other easily identifiable initiatives, such as the one that enables bank customers to. Forecast the situation of your finances at the end of the month.

Dangers and Risks

The vast possibilities offered by the algorithms are not without risks. The dangers are many: they range from cybersecurity – to deal with hacking or theft of formulas – to the privacy of the users, going through the possible biases of the machines.

Thus, a recent study by the University Carlos III concluded that Facebook uses advertising for sensitive data of 25% of European citizens, who get tagged in the social network according to matters as private as their political ideology, sexual orientation, religion, ethnicity or health.
Cybersecurity, for its part, has become the primary concern of investors around the world: 41% said they were “apprehensive” about this issue, according to the Global Investors Survey of 2018.

What is the future of the algorithms?

This technology is fully functional to meet the objectives of almost any organization today, and although we do not know, is present in many well-known firms in the market. Its capabilities of analysis, prediction and report generation for decision making make it a powerful strategic tool.

Algorithms, either through specific applications or with the help of Business Intelligence or Big Data solutions open the way to take advantage of the information available in our company and turn it into business opportunities.

Thanks to the algorithms we know better how our clients and prospects behave, what they need, what they expect from us. And they also allow us to anticipate the actions of our competitors and market trends.

Like any technological innovation that has revolutionized our way of understanding the world since man is a man, it will take us some time to become aware of this new reality and learn to make the most of it. As citizens and as communicators we can turn algorithms into valuable allies.

The algorithm is at the heart of technologies potentially as powerful as artificial intelligence. Nowadays, algorithms are the basis of machine learning technologies, which surprise us every day with new skills. And it is behind techniques of the setting of virtual assistants or autonomous vehicles.

The data visualization allows us to interpret information in a simple and very visual way. Its primary objective is to communicate information clearly through graphics, diagrams, or infographics.

Sometimes, we are not aware of the importance of data in our routine life. We believe that it is something close to the professional world when, for example, simple indicators such as the percentage of your mobile’s battery or your car’s consumption data that will allow you to save fuel are fundamental.

At a professional level, the reading of data and its graphics visualization is a priority. Because at the end of the day they are the indicators that allow us to understand the tendency of the results. This, whether we are improving, maintaining the line or, on the contrary, getting worse in the tasks carried out by the different work team. Since at the end of it depends directly on the scope or not of the marked business objectives. Therefore, it is necessary to monitor these data constantly, to have a diagnosis of the company’s health at the moment.

The best way is to translate the data into a visual, graphic image, through some of the best tools available in the market. Most work in a similar way, importing the data, offering different ways of viewing and publishing them; all this with a simple usability level, according to people who are not experts in the field and with the necessary adaptation so that they can get seen in the different technological formats available in the market, including mobile ones.

Here are some and their main features:

Data Studio (Google)

The Californian giant is present in a leading role in the data visualization market thanks to Google Data, a free and easy to use tool. It connects with other means such as Google Analytics or Adwords, and through payment, you can also do it with others such as Facebook. It is accessed through the browser without the need to install additional software.


It is a favorite Business Intelligence tool that allows the interactive visualization of data. It is an ideal option for all audiences, whatever the purpose, since through its website they offer good tutorials to familiarize yourself with it. It only requires the initial investment in the license that best suits your needs after the end of the trial period. It meets all levels of demand and is a great choice also as a partner for corporate purposes.

Power BI

Microsoft also designed a set of tools dedicated to BI, from an editor and data modeling to visualization applications. It requires the download of software that fits your operating system and has a free version that can get expanded with personalized payment packages. It is intuitive and powerful, but not as simple to use as others in this list of options, hence it is focused mainly on business purposes of a particular demand.


Another free tool that offers a wide range of solutions to visualize imported data, from simple bar graphs too much more complex options.


This tool is a favorite especially among the media and educational purposes because their graphics can be added elements to the consumer’s taste as templates, icons, and even images and videos.


It has a free version that allows analyzing and creating dashboards, as well as manipulating and interacting with the information. The special features are limited to your payment service which you can access in test mode for free. It is a support that allows you to develop connections with other intermediate applications so that knowledge of programming languages ​​will enable you to squeeze it much better.


It is a data visualization tool specialized in infographics — thousands of templates and elements to create them in a personalized way that can be downloaded in different high-resolution formats or shared in an interactive way.


It is a more modest tool, but that according to your needs can be enough because it allows you to create graphics with great simplicity and then share them and display them in high resolution in any format.

The best thing, even if they all work similarly, is to choose the one that best meets the demands you need. It is not the same to look for a tool that allows you to build simple graphs that require advanced business intelligence functions. Therefore, within the list, there are eight options with different levels of development and functionalities. In each of its web pages, you can deepen more about them before opting for one.

In the information age, data has become an essential and essential element for any brand that wants to develop a precise and effective strategy and achieve the engagement of its target.

For this, many companies invest a lot of money in recruiting the best talent in this field, but when it comes to choosing which is better, a data scientist or a data analyst? And more importantly, do companies know what the difference between them is?

Although both professions are vital for the marketer world, it is essential to understand the differences between their jobs depending on the approach you want to give to a strategy. The truth is that the industry tends to name these professionals indistinctly and has generated a confusion that we want to clear up.

Advent of the data scientist

Companies saw the availability of large volumes of data as a source of competitive advantage and realized that if they used this data effectively, they would make better decisions and be ahead of the growth curve. The need arose for a new set of skills that included the ability to draw client/user perceptions, business acumen, analytical skills, programming skills, analytical skills, machine learning skills, visualization of data and much more. It led to the emergence of a data scientist.

Data scientists and Data analysts

Data scientist– You probably have a strong business sense and the ability to communicate effectively, data-driven conclusions to business stakeholders. A data scientist will not only deal with business problems but will also select the right issues that have the most value to the organization.

A data scientist and an analyst can take Big Data analytics and Data Warehousing programs to the next level. They can help decipher what the data is saying to a company. They are also able to segregate relevant data from irrelevant data. A data scientist and an analyst can take advantage of the company’s data warehouse to go deeper into them. Therefore, organizations must know the difference between data scientists and data analysts.

Data scientists are a kind of evolution of the role of analysts but focus on the use of data to establish global trends on the problems of a company to solve them and improve business strategy.

Data Analyst– Your job is to find patterns and trends in the historical data of an organization. Although BI relies heavily on the exploration of past trends, the science of data lies in finding predictors and the importance behind those trends. Therefore, the primary objective of a BI analyst is to evaluate the impact of certain events in a business line or compare the performance of a company with that of other companies in the same market.

The data analyst has the primary function of collecting data, studying it and giving it a meaning. It is a process that can vary depending on the organization for which you work, but the objective is always the same, to give value and meaning to some data that by itself has no use. Thus, the result of analyzing, extrapolating and concluding is a piece of relevant information by itself, comparable with other data and use to educate other industry professionals about its applications.

An analyst usually relies on a single source of data such as the CRM system while a data scientist can conclude from different sources of information that may not be connected.

Main differences between the two

  • Usually, a data scientist expects to ask questions that can help companies solve their problems, while a BI data analyst answers and answers questions from the business team.
  • It is expected that both functions write queries, work with engineering teams to obtain the correct data and concentrate on deriving information from the data. However, in most cases, a BI data analyst is not expected to construct statistical models. A BI data analyst typically works on simpler SQL databases or similar databases or with other BI tools/packages.
  • The role of the data scientist requires strong data visualization skills and must have the ability to convert data into a business history. Typically, a BI data analyst is not expected to be an expert in business and advanced data visualization.

Companies must know how to distinguish between these two functions and the areas in which a data scientist and a business analyst can add value.

Information is an essential asset for any organization and the potential of its value lies in the data that, on occasion, must be migrated to improve the performance of a database, update versions, reduce costs or implement security policies.

But what is data migration?

This process consists of the transfer of data from one system to another and usually takes place at times of transition caused by the arrival of a new application, a change in the mode or storage medium or the needs imposed by the maintenance of the base of corporate data.

Generally, a data migration occurs during a hardware upgrade or transfer from an existing system to an entirely new one. Some examples are:

  • Update of a database.
  • Migration to or from the hardware platform.
  • Migration to new software.
  • Fusion of two parallel systems into one that is required when one company absorbs another or when two businesses merge.

In no case should the term migration of data be confused with others that, although similar, show essential differences in the number of sources of origin and destination of data or their diversity. Consolidation, integration or updating of data are different processes with different purposes.

What is data migration, what does it imply and how can it be carried out?

Data migration gets represented by the initials ETL, which correspond to the terms: extraction, transformation, and loading. Although an ETL process can get applied with other objectives, when considering what data migration is, it is inevitable to allude to its primary task: extraction and loading (since the transformation does not have to be applied in all cases, only if necessary).

There are three main options for carrying out data migration:

  • Combine the systems of the two companies or sources into a new one.
  • Migrate one of the systems to the other.
  • Maintain the integrity of both systems, leaving them intact, but creating a common vision for both: a data warehouse.

The most suitable tool to carry out a data migration is one of extraction, transformation and loading, as opposed to less productive options, such as manual coding; other inapplicable, such as the integration of applications (EAI) or others that do not provide everything necessary to carry out the process with full guarantees, as is the case of replication.

To carry out a data migration it is necessary to go through the following steps:

1. Planning– from the definition of the strategy and scope of the project to the feasibility analysis.

2. Analytical– considering variables such as the integrity, accuracy or consistency of the data to be migrated and taking into account the characteristics of the databases of origin and destination.

3. Application selection– can be developed internally or acquired after evaluating the different alternatives.

4. Testing– application of the test cycles to the applications that will use the database.

5. Migration– includes the extraction, transformation and loading stages.

6. Evaluation– it is about measuring the results and analyzing them, determining the necessary adjustments.

Challenges that all data migration must face

Although data migration can be a simple process, its implementation may encounter challenges that will have to be addressed.

  • Discover that the source code of the source application is not available and the manufacturer of that application is no longer on the market anymore.
  • Find types or formats of source data that have no correspondence in destination: numbers, dates, sub-registers.
  • Coding problems that affect certain datasets.
  • The existence of optimizations in the data storage format, such as encoded decimal binary storage, non-standard storage of positive/negative numerical values ​​or storage types from which mutually sub-registers are excluded within a record.
  • Issues related to the appearance of redundancies and duplications when, at the same time as data migration was carried out, different types of users used the old or the new system or application.

Dismantling the myths

Those who consider what data migration is may find themselves in a difficult position, susceptible to falling into extended beliefs but lacking solidity. Understanding the implications of migration involves discerning the myths that have nothing to do with it:

  • Data migration is not a simple process of copying data.
  • Data migration is not carried out in one sitting, it is a complex process that has its phases and requires time.
  • Data migration cannot be solved only from the outside, it is necessary and highly recommended to have the support of the owners of the data.
  • The transformation and validation of data can not, under any circumstances, occur after loading. It must always be done beforehand and the result must be subjected to cycles of tests that demonstrate its suitability to be loaded at the destination.

Better Practices

  • Give data profiling the importance it deserves.
  • Do not underestimate the data mapping.
  • Carry out the profiling tasks at the right time and never after loading.

  • Prefer automatic options to manuals for data profiling.
  • Take advantage of data migration to improve the quality of data and metadata.
  • Relying on data modeling techniques to optimize integration.
  • Keep in mind the operative facet of the data and try to simplify the future user interaction in administrative, reporting or update tasks.

A programming language is an artificial language designed to express computations that can be carried out by machines such as computers. They can be used to create programs that control the physical and logical behavior of a device, to express algorithms with precision, or as a mode of human communication.

Is formed of a set of symbols and syntactic and semantic rules that define its structure and the meaning of its elements and expressions. The process by which you write, test, debug, compile and maintain the source code of a computer program is called programming.

Also, the word programming gets defined as the process of creating a computer program, through the application of logical procedures, through the following steps:

  • The logical development of the program to solve a particular problem.
  • Writing the logic of the program using a specific programming language (program coding).
  • Assembly or compilation of the program until it becomes a machine language.
  • Testing and debugging the program.
  • Development of documentation.

There is a common error that treats the terms ‘programming language’ and ‘computer language’ by synonyms. Computer languages encompass programming languages and others, such as HTML. (language for the marking of web pages that is not properly a programming language but a set of instructions that allow designing the content and text of the documents)

It allows you to specify precisely what data a computer should operate, how it should be stored or transmitted, and what actions to take under a variety of circumstances. All this, through a language that tries to be relatively close to human or natural language, as is the case with the Lexicon language. A relevant characteristic of programming languages is precisely that more than one programmer can use a common set of instructions that are understood among them to carry out the construction of the program collaboratively.

The implementation of a language is what provides a way to run a program for a certain combination of software and hardware. There are basically two ways to implement a language: Compilation and interpretation. Compilation is the translation into a code that the machine can use. The translators that can perform this operation are called compilers. These, like advanced assembly programs, can generate many lines of machine code for each proposal of the source program.

Imperative and functional languages

The programming languages ​​are generally divided into two main groups based on the processing of their commands:

  • Imperative languages
  • Functional languages.

Imperative programming language

Through a series of commands, grouped into blocks and composed of conditional orders, it allows the program to return to a block of commands All this if the conditions get met. These were the first programming languages ​​in use, and even today many modern languages ​​use this principle.

However, structured imperative languages ​​lack flexibility due to the sequentiality of instructions.

Functional programming language

A functional programming language (often called procedural language) is a language that creates programs employing functions, returns a new result state and receives as input the result of other purposes. When a task invokes itself, we talk about recursion.

The programming languages ​​can, in general, get divided into two categories:

  • Interpreted languages
  • Compiled languages

Interpreted language

A programming language is, by definition, different from the machine language. Therefore, it must get translated so that the processor can understand it. A program written in an interpreted language requires an auxiliary program (the interpreter), which converts the commands of the programs as necessary.

Compiled language

A program written in a “compiled” language gets translated through an attached program called a compiler that, in turn, creates a new independent file that does not need any other program to run itself. This file is called executable.

Also, it has the advantage of not needing an attached program to be executed once it has compiled. Also, since only one translation is necessary, the execution becomes faster.

The interpreted language, being directly a readable language, makes that any person can know the manufacturing secrets of a program and, in this way, copy its code or even modify it.


The implementation of a language is what provides a way to run a program for a certain combination of software and hardware. There are basically two ways to implement a language: Compilation and interpretation. Compilation is the translation into a code that the machine can use. The translators that can perform this operation are called compilers. These, like advanced assembly programs, can generate many lines of machine code for each proposal of the source program.


To write programs that provide the best results, a series of details must be taken into account.

  • Correction.  Programs are correct if they do what they should do as they got established in the phases before their development.
  • Clarity. It is essential that the program be as clear and legible as possible, to facilitate its development and subsequent maintenance. When developing a program, you should try to make its structure coherent and straightforward, as well as take care of the style in the edition; In this way, the work of the programmer is facilitated, both in the creation phase and in the subsequent steps of error correction, extensions, modifications, etc. Stages that can be carried out even by another programmer, with which clarity is even more necessary so that other programmers can continue the work efficiently.
  • Efficiency. The point is that the program does so by managing the resources it uses in the best possible way. Usually, when talking about the efficiency of a program, it is generally referred to the time it takes to perform the task for which it got created. And the amount of memory it needs, but other resources can also get considered when obtaining the efficiency of a program. It all depends on its nature (disk space it uses, network traffic it generates, etc.).
  • Portability. A program is portable when it can run on a platform, be it hardware or software, different from the one on which it got developed. Portability is a very desirable feature for a program, since it allows, for example, a program that has been designed for GNU / Linux systems to also run on the family of Windows operating systems. It will enable the program to reach more users more efficiently.

What is a VLAN?

According to Wikipedia, a VLAN, an acronym for virtual LAN (Virtual Local Area Network), is a method to create independent logical networks within the same physical network. The IEEE 802.1Q protocol is responsible for the labeling of the frames that is immediately associated with the VLAN information.

What does this mean? Well, it’s simple, it’s about logically dividing a physical network, you’ll understand it better with the following example:

Imagine a company with several departments in which you want them to be independent, that is, they can not exchange data through the network. The solution would be to use several switches, one per department, or to use a switch logically divided into small switches, that is precisely a VLAN. We already have the different departments separated, but now we need to give them access to services like the internet, the different servers, and more.

For this, we have two options:

  • Use a switch or layer 3 and 4 switch, that is, with the ability to “route” the different VLANs to a port.
  • Or use a firewall with VLAN support, that is, in the same physical interface, it allows to work with several VLANs as if it had several physical interfaces.

Types of VLANs

Level 1 VLAN

The level 1 VLAN defines a virtual network according to the port of the switch used, also known as “port switching.” It is the most common and implemented by most switches in the market.

Level 2 VLAN

This type of VLAN defines a virtual network according to the MAC addresses of the equipment. In contrast to the VLAN per port, it has the advantage that computers can change ports, but all MAC addresses must be assigned one by one.

Level 3 VLAN

When we talk about this type of VLAN it should be noted that there are different types of level 3 VLANs:

  • VLAN-based network address connects subnets according to the IP address of the computers.
  • Protocol-based VLAN allows creating a virtual network by type of protocol used. It is very beneficial to group all the computers that use the same protocol.

How does a VLAN work per port?

The IEEE 802.1Q protocol is responsible for the tagging (TAG) of the frames that gets immediately associated with the VLAN information. It consists of adding a tag or TAG to the header of the structure that indicates to which VLAN the frame belongs.

Based on the “tagged” VLANs, we can differentiate between:

  • TAGGED– When the connected device can work directly with VLAN, it will send the information of the VLAN to which it belongs. Thanks to this feature, the same port can work with several VLANs simultaneously.

When we configure a port with all the VLANs configured in TAGGED, we call it Trunk and it is used to join the network device in cascade. This system allows the packets of a VLAN to pass from one switch to another until finding all the equipment of said VLAN. Now we need to give them access to services like the internet, the different servers, and more.

For this, we have two options:

  • Use a switch or layer 3 or 4 switch, that is, with the ability to “route” the different VLANs to a port.
  • Or use a firewall with VLAN support, that is, in the same physical interface, it allows working with several VLANs as if it had several physical interfaces, each of which will give access to a VLAN to the services.

Choosing one or the other depends on whether the firewall used supports VLANs, if we pass communications through the firewall, we will always have more control over them, as I will explain later.

Advantages of segmenting your network using VLANs

The main benefits of using VLANs are the following:

  • Increase Security- By segmenting the network, groups that have sensitive data are separated from the rest of the net, reducing the possibility of breaches of confidential information.
  • Improve performance- By reducing and controlling the transmission of traffic on the network by division into broadcast domains, performance will be enhanced.
  • Reduction of costs- The cost savings result from the little need for expensive network upgrades and more efficient use of links and existing bandwidth.
  • The higher efficiency of the IT staff- The VLAN allows to define a new network over the physical network and to manage the network logically.

In this way, we will achieve greater flexibility in the administration and the changes of the network, since the architecture can be changed using the parameters of the switches, being able to:

  • Easily move workstations on the LAN.
  • Easily add workstations to the LAN.
  • Easily change the configuration of the LAN.

Advantages of having a firewall with VLAN support

  • More significant cost savings- We will not have to invest in a switch with “routing capacity,” and it will be worth a layer 2, currently very economical.
  • Greater security and control- We do not “route” one VLAN to another without any power, being able to create access rules between the VLANs and inspect all traffic.
  • The higher performance of the network- We will have the possibility to prioritize by QoS (Quality of service) specific VLANs or protocols.

Voice over IP (VoIP) traffic since it requires:

  • Guaranteed bandwidth to ensure voice quality
  • Priority of transmission over network traffic types
  • Ability to be routed in congested areas of the network
  • Delay of less than 150 milliseconds (ms) through the network

Therefore, as you have seen, having a Firewall with VLAN support supposes a series of significant advantages when managing your information systems. Not only will you get performance improvements, but you’ll also simplify your administration tasks.