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Programming

How Many Programming Languages Exist?

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.

Implementation

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.

Technique

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.
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Optimization

What is Stochastic Optimization?

The Stochastic Optimization seeks the best decision in a scenario dependent on random events, dependent on chance, whether those events the prices of a product, the duration of a task, the number of people in the queue of a cashier, the number of breakdowns in a fleet of trucks, or even the approval of a regulation, come on, anything.

Stochastic?

Stochastic is a particularly feared word. It is since is known that most functional languages ​​have been made a bad idea, by jealous experts who want to keep their secrets. There we have the legal jargon, the economic, or closer to our work; rumor has it that the creator of the C ++ language made it so complicated to differentiate good programmers from bad guys. The word “stochastic” is not dangerous; it means simply random, dependent on chance. The idea is quite simple, but as an adjective, it can complicate any discipline.

Problems of Stochastic Optimization

The problems of stochastic optimization are in general much more complicated than those that do not consider chance, mainly because luck implies that we do not have a single scenario to be optimized, but a set of possible scenarios. For example, if we want to maximize the design of an energy distribution network, we will work in an uncertainty scenario, in which we do not know the actual demand for energy at the time of use of the system. Instead of the demand data, we would have an estimate, perhaps a finite set of possible demands with an associated probability.

With this, we can already intuit that the world of the company is full of stochastic problems, what is usually done to solve them ?. In scenarios with simple decisions, that is to say, few decision variables and with few states, all the possibilities can be explicitly enumerated using decision trees that are also very intuitive.

Stochastic Optimization

Although it is considered that this discipline was born in the 50s with Dantzig and Beale, historically optimization has had enough to be restricted to non-stochastic problems, essentially due to the complexity that stochastic problems entail. Facing real problems is still impossible in many cases, but advances in computing capacity and the development of optimization techniques have allowed problems to be solved until recently unthinkable. In addition, sometimes, only a stochastic approach can greatly improve the solution, which translates into cost savings, service improvement, increased benefits, among others, all of them factors not insignificant.

An optimization problem has:

  • a series of variables or decisions that must be taken.
  • a series of restrictions that limit those decisions.
  • and an objective function, a measure of cost or quality of the set of decisions taken.

The data associated with the constraints and the objective function are usually known values, but what if random events gave these values? Then the problem is stochastic optimization.

There are two particularly uncomfortable questions:

What happens to feasibility when the restrictions are random?

A solution is feasible when it satisfies all the restrictions, but with arbitrary limits, we cannot speak strictly of feasibility but probability that a certain answer is viable. Thus, in the problem of planning the power distribution network, a restriction could be “the capacity of the distribution network is greater than or equal to the demand” but if the demand turns out to be very high, it can happen that we can not satisfy it.

How to redefine the objective function?

The answer to this question is less obvious, we could redefine the objective function as the expected value of the previous objective function, but it could be more interesting to reduce the risk of the decision using the worst case scenario. In general, these types of problems have been addressed using linear programming.

What are these problems complicated?

The complexity of these problems lies in their size. Think of the seemingly harmless lady who leaves the hairdresser. Suppose you also have to decide whether to go by bus or walk to do a message. Each new random variable and each new decision multiply the possibilities. Instead of considering four possible outcomes we should consider tens or hundreds.

If we continue to consider elements of uncertainty that may threaten the woman, the number of possible outcomes will increase exponentially. Let’s go back now to the problem of planning the energy distribution network; the demand is random, the future prices of energy are arbitrary, the production of wind energy is absolute, as well as the costs of fuels. The outcome of any decision in this context will depend on what happens with each of those random events.

Conclusion

Most of the real problems are of stochastic nature; there are few businesses in which all the data are known in advance, we cannot keep avoiding them. The stochastic optimization can allow us to face problems that until now have been solved by “intuition,” by “common sense,” or because “of all life has been done like this,” in a more efficient way, providing solutions that will place us in clear advantage over our competitors.

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Dedicated Server Best Practices

Advantages and Disadvantages of Primary and Secondary Data

The strategic use of information gives companies a competitive response capacity that requires the search, management, and analysis of many data from different sources. Among this information, the secondary data have an essential weight when it comes to extracting value for use in research or studies.

Faced with primary information, created expressly for a specific study, the researcher also has secondary data, valid information already developed by other researchers that may be useful for particular research.

Likewise, these data may have been generated previously by the same researchers or, in general, by the same organization that conducts the study or, where appropriate, has commissioned it. That is why, as a general recommendation, the search should start with the internal data.

Regardless of whether they get obtained inside or outside the organization, the primary data generated in an investigation will be considered secondary data.  They can get used in others to save time and money, since it would not be feasible to carry them out for obvious budget issues or, just, it is unnecessary because it has already got done.

Internal and external secondary data

Once the search for internal information has to get completed, the researcher should focus on external secondary data sources, ideally following a previous plan that serves as a guide to a large number of sources available today.

Therefore, secondary information can get roughly divided into internal and external secondary data:

  • Internal secondary data– information that is available within the company is included, from accounting data or letters from customers or suppliers and vendor reports or surveys from the human resources department to, for example, previous research.
  • External secondary data– is data collected by sources external to the company. They can get found in other organizations or companies, such as census data, institutional statistics, government studies, organizations and associations, research and data disseminated in periodicals, in books, on the internet or, for example, the same digital data.

The growing importance of secondary information

Secondary data is more accessible to obtain, relatively inexpensive and available.

Although it is rare for secondary data to provide all the answers to an unusual research problem, such data may be useful for the investigation.

The use of secondary data in research processes is a common practice for years. However, with all this emergence of Big Data and the greater ease of access to different sources of information, its use has gained a strong impetus as a tool of business intelligence, mainly for the following reasons:

  • It is easy to access and economical information.
  • It serves as a point of comparison of the organizational results with respect to the market.
  • It serves to focus and define new organizational projects.
  • Allows estimation of quantitative benefits for new organizational projects (ROI)
  • It allows estimating future market behavior based on facts and data.
  • It facilitates the strategic decision making of organizations.

Among the disadvantages of the secondary data, we find that initially they could be investigated for different purposes to the current problem. It limits the information we can obtain and need for research.

It is likely that the objectives, nature, and methods used to collect the secondary data are not adequate for the present situation. Also, secondary data may be inaccurate or not completely current or reliable. Before using secondary data, it is important to evaluate them concerning such factors.

As a tool of great value, which helps to provide a clear competitive advantage, it is essential that organizations allocate technological and human resources to the establishment of processes aimed at the identification, selection, validation (verification of its accuracy, coherence, and credibility), processing and secondary information analysis.




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Data

What can happen while handling large amounts of Data?

A database performance monitoring and management tools can be used to mitigate problems and help organizations to be more proactive so that they can avoid performance problems and interruptions.

Even the best-designed database experiences degradation of performance. No matter how well the database structures are defined or the SQL code gets written, things can and will go wrong. And if the performance problems are not corrected quickly, that can be detrimental to the profitability of a company.

Performance of a Database

When the performance of the database suffers, business processes within organizations slow down and end users complain. But that is not the worst of all. If the performance of the systems they see abroad is bad enough, companies can lose business, as customers who are tired of waiting for the applications to respond will go elsewhere.

Because the performance of database systems and applications can be affected by a variety of factors, the tools that can find and correct the causes of database performance problems are vital for organizations that rely on them in database management systems (DBMS) to run your mission-critical systems. And in today’s IT world, focused on databases, that applies to most companies.

Types of performance problems you should look for


Many types of database performance problems can make it difficult to locate the cause of individual problems. It is possible, for example, that the database structures or the application code are flawed from the beginning. Bad database design decisions and incorrectly encoded SQL statements can result in poor performance.

It may be that a system was well designed initially, but over time the changes caused the performance to begin to degrade. More data, more users or different patterns of data access can slow down even the best database applications. Even the maintenance of a DBMS – or the lack of regular maintenance of databases – can cause performance to plummet.


The following are three important indicators that could indicate database performance issues in your IT department:

1. Applications that go slower. The most important indication of potential performance problems in the database is when things that used to run fast start running at a slower pace. Including online transaction processing systems that are used by employees or customers, or batch jobs that process data in large quantities for tasks such as payroll processing and end-of-month reports.

Monitoring a processing workload without database performance management tools can become difficult. In that case, database administrators (DBAs) and performance analysts have to resort to other methods to detect problems, in particular, complaints from end users about issues such as application screens taking too much time to upload or nothing to happen for a long time after the information is entered into an application.

2. System interruptions. When a system is turned off, the performance of the database is obviously at its worst. Interruptions can be caused by database problems, such as running out of storage space due to increased volumes of data or by a resource that is not available, such as a data set, partition or package.

3. The need for frequent hardware updates. The constantly upgrading of servers to larger models with more memory and storage are often candidates for database performance optimization. Optimizing database parameters, tuning SQL statements and reorganizing database objects can be much less expensive than frequently updating expensive hardware and equipment.

On the other hand, sometimes hardware updates are needed to solve database performance problems. However, with the proper tools for monitoring and managing databases, it is possible to mitigate the costs of updating by locating the cause of the problem and identifying the appropriate measures to remedy it. For example, it may be cost-effective to add more memory or implement faster storage devices to resolve I / O bottlenecks that affect the performance of a database. And doing so will probably be cheaper than replacing an entire server.

Problems that tools can help you manage

When the performance problems of the database arise, it is unlikely that its exact cause will be immediately evident. A DBA should translate vague complaints about end-user issues into specific issues, related to performance, that can cause the problems described. It can be a difficult and error-prone process, especially without automated tools to guide the DBA.

The ability to collect the metrics on database usage and identify the specific problems of the database – how and when they occur – is perhaps the most compelling capability of the database performance tools. When faced with a performance complaint, the DBA can use a tool to highlight current and past critical conditions. Instead of having to look for the root cause of the problem manually, the software can quickly examine the database and diagnose possible problems.

Some, database performance tools can be used to set performance that, once triggered, alert the DBA of a problem or trigger an indicator on the screen. Also, DBAs can schedule reports on database performance to be executed at regular intervals, in an effort to identify the problems that need to be addressed. Advanced tools can both identify, and help solve any situations.

There are multiple variations of performance issues, and advanced performance management tools require a set of functionalities.

The critical capabilities provided by the database performance tools include

  • Performance review and SQL optimization.
  • Analysis of the effectiveness of existing indexes for SQL.
  • Display of storage space and disk defragmentation when necessary.
  • Observation and administration of the use of system resources.
  • Simulation of production in a test environment.
  • Analysis of the root cause of the performance problems of the databases.

The tools that monitor and manage the performance of databases are crucial components of an infrastructure that allows organizations to effectively deliver the service to their customers and end users.

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Data

What is the Difference between Data, Information, and Knowledge?

When we talk about measurement, we must understand how knowledge differs from data and information.

In an informal conversation, the three terms get often used interchangeably, and this can lead to a free interpretation of the concept of knowledge. Perhaps the simplest way to differentiate the words is to think that the data get located in the world and experience is located in agents of any type, while the information adopts a mediating role between them.

An agent does not equal a human being. It could be an animal, a machine or an organization constituted by other agents in turn.

Data

A data is a discrete set of objective factors about a real event. Within a business context, the concept of data gets defined as a transaction log. A datum does not say anything about the way of things, and by itself has little or no relevance or purpose. Current organizations usually store data through the use of technologies.

From a quantitative point of view, companies evaluate the management of data regarding cost, speed, and capacity. All organizations need data, and some sectors are dependent on them. Banks, insurance companies, government agencies, and Social Security are obvious examples. In this type of organizations, good data management is essential for their operation, since they operate with millions of daily transactions. But in general, for most companies having a lot of data is not always right.

Organizations store nonsense data. This attitude does not make sense for two reasons. The first is that too much data makes it more complicated to identify those that are relevant. Second, is that the data have no meaning in themselves. The data describe only a part of what happens in reality and do not provide value judgments or interpretations, and therefore are not indicative of the action. The decision making will get based on data, but they will never say what to do. The data does not say anything about what is essential or not. In spite of everything, the info is vital for the organizations, since they are the base for the creation of information.

Information

As many researchers who have studied the concept of information have, we will describe it as a message, usually in the form of a document or some audible or visible communication. Like any message, it has an emitter and a receiver. The information can change the way in which the receiver perceives something, can impact their value judgments and behaviors. It has to inform; they are data that make the difference. The word “inform” means originally “shape” and the information can train the person who gets it, providing specific differences in its interior or exterior. Therefore, strictly speaking, it is the receiver, and not the sender, who decides whether the message he has received is information, that is if he informs him.

A report full of disconnected tables can get considered information by the one who writes it, but in turn, can be judged as “noise” by the one who receives it. Information moves around organizations through formal and informal networks. Formal networks have a visible and defined infrastructure: cables, e-mail boxes, addresses, and more. The messages that these networks provide include e-mail, package delivery service, and transmissions over the Internet. Informal networks are invisible.

They are made to measure. An example of this type of network is when someone sends you a note or a copy of an article with the acronym “FYI” (For Your Information). Unlike data, information has meaning. Not only can it potentially shape the recipient, but it is organized for some purpose. The data becomes information when its creator adds sense to it.

We transform data into information by adding value in several ways. There are several methods:

• Contextualizing: we know for what purpose the data were generated.

• Categorizing: we know the units of analysis of the main components of the data.

• Calculating: the data may have been analyzed mathematically or statistically.

• Correcting: errors have been removed from the data.

• Condensing: the data could be summarized more concisely. Computers can help us add value and transform data into information, but it is tough for us to help analyze the context of this information.

The widespread problem is to confuse information (or knowledge) with the technology that supports it. From television to the Internet, it is essential to keep in mind that the medium is not the message. What gets exchanged is more important than the means used to do it. Many times it is commented that having a phone does not guarantee to have brilliant conversations. In short, that we currently have access to more information technologies does not mean that we have improved our level of information.

Knowledge

Most people have the intuitive feeling that knowledge is something broader, deeper and more productive than data and information. We will try to make the first definition of knowledge that allows us to communicate what we mean when we talk about knowledge within organizations. For Davenport and Prusak (1999) education is a mixture of experience, values, information and “know-how” that serves as a framework for the incorporation of new skills and knowledge, and is useful for action. It originates and applies in the minds of connoisseurs. In organizations, it is often not only found in documents or data warehouses, but also organizational routines, processes, practices, and standards. What immediately makes the definition clear is that this knowledge is not pure. It is a mixture of several elements; it is a flow at the same time that it has a formalized structure; It is intuitive and challenging to grasp in words or to understand logically fully.

Knowledge exists within people, as part of human complexity and our unpredictability. Although we usually think of definite and concrete assets, knowledge assets are much harder to manage. Knowledge can be seen as a problem or as stock. Knowledge is derived from information, just as information gets derived from data. For information to become knowledge, people must do practically all the work.
This transformation occurs thanks to

• Comparison.

• Consequences.

• Connections.

• Conversation.

These knowledge creation activities take place within and between people. Just as we find data in registers, and information in messages, we can obtain knowledge from individuals, knowledge groups, or even in organizational routines.

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Data

Difference Between Data and Information

Information and data are fundamental concepts in computer science. A data is nothing more than a symbolic representation of some situation or knowledge, without any semantic sense, describing circumstances and facts without transmitting any message.

While the information is a set of data, which are processed adequately so that in this way, they can provide a message that contributes to the decision making when solving a problem. Also to increasing knowledge, in the users who have access to this information.

The terms information and data may seem to mean the same; however, it is not. The main difference between this concept is that the data are symbols of different nature and the information is the set of these data that have gotten treated and organized.

Information and data are two different things, although related to each other.

The differences between both are the following:

Data

  • They are symbolic representations.
  • By themselves, they have no meaning.
  • They can not transmit a message.
  • They are derived from the description of certain facts.
  • The data is usually used to compress information to facilitate the storage of data, and its transmission to other devices on the contrary that the report, which tends to be very extensive.

Information

  • It is the union of data that has been processed and organized.
  • They have meaning.
  • You can transmit a message.
  • Increase knowledge of a situation.
  • The information or message is much higher than the data since the data gets integrated by a set of data of different types.
  • Another remarkable feature of the information is that it is a message that has communicational meaning and a social function. While the data does not reflect any word and usually is difficult to understand by itself for any human being, lacking utility if it is isolated or without other groups of data that create a consistent message.


The main difference gets centered on the message that the information can transmit, and that a data on its own cannot perform. A lot of info is needed to create a news or information. There is a difference between data and information, and that this difference is quite significant. Therefore, these terms should not be confused, especially within the computing and computer field, as well as, within the area of ​​communications.

For this to be information as such, you must meet these 3 requirements:

  • Be useful– What is the use of knowing that “The price of X share will rise by 10% in the next 24 hours” if I want to see the definition of Globalization?
  • Be reliable– What good is a piece of information, if we do not know if it is true, accurate or at least reliable? Not every part of the data will be correct, but at least it must be reliable. It could be making a decision based on the wrong information.

  • Be timely– What is the use of knowing that it rains in the United States if I live in Argentina? I am looking to see if it will rain in the afternoon in my country to know if I should go out with an umbrella or not.

What is data?

Data are symbolic representations of some entity, can be alphabetic letters, points, numbers, drawings, etc. The data unitarily have no meaning or semantic value, that is, they have no impact. But when correctly processed, they become meaningful information that helps make decisions. The data can be grouped and associated in a specific context and produce the data.


Classification of data

  • Qualitative– Are those that indicate qualities such as texture, color, experience, etc.
  • Continuous– These are data that are expressed in whole or complete numerical form.
  • Discrete– These data are expressed in fractions or using decimals.
  • Quantitative– Data that refers to the numerical characteristic, can be numbers, sizes, quantities.
  • Nominal– They includes data such as sex, academic career, qualifications. They can be assigned a number to process them statistically.
  • Hierarchized– They are those that throw subjective evaluations and are organized according to achievement or preference.


What is information?

Information is the grouping of data whose organization allows to convey a meaning. It will enable the uncertainty to decrease and the knowledge to increase. The info is elementary to solve problems because it provides everything necessary to make appropriate decisions.

In an organization, information is one of its most vital resources so that it lasts over time. For data to become information must be processed and organized, always fulfilling some characteristics, some exclusionary, others only important but may not be.


Characteristics of the information

  • Relevance– Must be relevant or important to generate and increase knowledge. The incorrect decision making is often due to the grouping of too many data, therefore the most important ones must be collected and grouped.
  • Accuracy– must have sufficient accuracy, taking into account the purpose for which it is needed.
  • Complete– All the information needed to solve a problem must be complete and available.
  • Reliable source– The information will be reliable as long as the source is reliable.
  • Deliver to the right person– The information must be given to whoever is entitled to receive it, only then can it fulfill its true objective.
  • Punctuality– The best information is the one that is communicated at the precise moment when it is needed and will be used.
  • Detail– You must have specific details so that this is effective.
  • Comprehension– If the information is not understood, it can be used and will not have any value for the recipient.

The process of transformation of data into information and knowledge

There are many instances from which one receives data until that data is a factual knowledge that we will enjoy benefits, and even one of those intermediate instances is information.

The process will vary depending on the sample (type, quantity, and quality of data) and depending on our objectives, but the process is somewhat similar to this:

  • Data – We receive a series of data, which may be few or many, may be useful or not, we still do not know.
  • The data are selected – Now we have to see them, one by one and we have to really see which ones are useful to us. Based on this we will have a list of selected data.
  • Pre Process – Now with that data selected, now perhaps only 20% of those that were original, we have to organize them to be able to enter them into some processing system.
  • Processed data – They are no longer just selected data, now they are organized and processor, now we are faced with a professed transformation of those data because we are looking for a result.
  • Transformed data – It is no longer raw data much less, and practically has the form of information and in fact, roughly we can find certain things that may get our attention.
  • Patterns – When we repeatedly have precise information and apply it to look for patterns, in some occasions that information can be useful, reliable and obviously timely, but nobody has the absolute truth; Some piece of information may have some error/deviation, however slight it may be.
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Data Center

How Can a Data Center Transform Your Business?

A data center is the place where the computing, storage, networking and virtualization technologies that are required to control the life cycle of the information generated and managed by a company are centralized.

It plays a fundamental role in the company operations, since data centers help them to be more efficient, productive and competitive. At the same time, they adjust to the new needs of the businesses and respond quickly to even the most demanding consumers.

Data centers have adapted to this new reality and have developed services, not only to store valuable information of a company, but also with the purpose of automating processes and guaranteeing that each enterprise takes advantage of 100% of their data.

How a data center can help your business

  • Higher productivity– By having a data center, companies can increase their agility and productivity by simplifying their administrative processes and obtaining flexible and scalable environments that meet each of their objectives. Most companies and individuals have to deal with problems related to the flow of their work, customer service, and information management on a daily basis. All these situations distract the management teams, impairing their ability to keep the boat afloat and focus on sales or product development.
  • Technological flexibility– Through data centers, companies can also obtain flexibility in their technical infrastructure, since part of their information can be migrated to the cloud, operated on internally, or given to a third party. It brings other benefits such as low operating costs, high levels of security, and confidentiality of their information.
  • Automatization– A data center can help automate your processes and services. Thanks to advances in artificial intelligence, now you can establish automated customer service channels and monitor the tasks of each area of ​​your company through project management platforms.
  • Physical security– A data center provides an efficient team to perform a series of activities, such as monitoring alarms (and in some cases, calling security agents for emergencies), unauthorized access, controlling access through identity confirmation of the collaborator, issuing reports, and answering telephone and radio calls.
  • Refrigeration and Energy– Excellent cooling and energy systems ensure the proper functioning of equipment and systems within a data center. Refrigeration plays the role of maintaining the temperature of the environment at the right levels so that everything operates in perfect condition. Generally, to avoid damage and problems with the power supply, the system as a whole has no-breaks and generators, in addition to being powered by more than one power substation. This ensures performance and efficiency — your business does not need to invest in either of these critical services, saving you a lot of money.  
  • Business visibility– Companies can have visibility into the traffic of their data centers, both physical and virtual, since they allow gathering business intelligence information, identifying trends and acting quickly and intelligently. This facilitates quick decision making.


You can try to establish your servers, with limited human resources and resources at hand, to protect all your know how, or you can trust an expert and ensure the computer security of your company and the welfare of your business — but a data center is always a good option. You get everything you need with an affordable price and all the features you would want.

Data centers must be designed with an appropriate infrastructure to support all the services and systems of the company, in such a way as to allow the perfect functioning of the center and foresee its future growth by adapting to emerging technologies.

Do not forget that the primary function of a data center is to provide technology services for the development of your operations and ensure the integrity and availability of your business information. So make sure your provider helps solve the needs of your company. In a world where information has become an invaluable asset, each company is tasked with making the best use of their data and protecting themselves.

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Data

Can You Get Paid for Your Data?

It is said that data is the new oil of this era because it nourishes the economy in one and a thousand ways. Social networks, search engines, and e-commerce platforms use data to generate personalized ads; some companies use it to optimize processes and thus save money or to create products increasingly oriented to the needs of their customers.

The point is that currently this data is delivered for free every time a person registers on a platform, when using a browser and visiting a page that, through cookies, stores the user’s movements within the site. Telephone companies can also obtain lots of data because they know the location of users at any given time.

Even when a person goes out, and sensors or cameras capture the image or movements in the city, digital data is produced that is used to create solutions that could translate into money. It is how the big data universe works.


What would happen if companies could be charged for the use of that personal information?


Sometimes you let companies use your data, just by accepting privacy conditions without reading, downloading apps that need access to view your photos, allow a GPS to know at all times where we are, or storing images in a cloud, to name a few.

Aware of the growing value of information in the economy, more and more companies are emerging that try to treat people’s personal info with care as a differential value.

One solution would be to create a decentralized market of data so that users can appropriate their information and sell it safely and anonymously.

It is estimated that, at present, the data that a user passively generates annually just by browsing the web, using social networks or different applications can be worth USD $240.

From the point of view of the data-buyer


Organizations receive anonymous data packages and use them for their research or projects. Being a decentralized market of anonymous data, the challenge is to know if that information is reliable because there could be many false profiles generated from different devices to create money.

Banks, for example, could be financial data verifiers and telephony companies could be responsible for verifying geolocation. The truth is that all entities that can collect and control data could eventually become verifiers.

Who would want to buy data that circulates for free?


For starters, it should be noted that although several companies collect information, not all can do so in an adequate, safe and orderly manner. Proof of this is that there are companies responsible for processing the large volume of information that is circulating on the web and then offering it, anonymously, to different companies.

Within the various measures that are specified in this regulation is the portability of data — which will allow the user to receive the personal information that has been provided to an entity, in a structured and commonly used format, to grant to another organization. It will work like number portability, but in this case, the asset that the user has is his personal info.

This initiative puts greater responsibility concerning one’s data in the hands of the user. In this sense, rights of the user are recognized, and a mechanism is provided to enforce these. 

Democratize access to data and the benefits it generates


The battle for some is not to oppose the collection and processing of data but to ensure that users can also take advantage of this new form of wealth generation. At present, the benefits are concentrated in few hands, but through some new proposals, data could be democratized and its benefits distributed in a more equitable way.

With a positive outcome, we will be able to cash in on our data and have extra income just for doing data-generating day-to-day activities.

In the centralized internet model, the user transfers his data to large giants such as Facebook, Google or Microsoft. In return, he receives information of all kinds and for different utilities: from a job offer to meeting friends and beyond.

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Data

Preparing for the Data Professions of the Future

Due to the accelerated pace of technology, young people today have to start preparing their studies for the future with professions that do not yet exist or are beginning to exist due to technological advances.

Studies have already shown that two out of every three young people belonging to the ‘millennial’ generation are convinced that they will devote themselves in the future to professions that do not yet exist due to technological advances.

Professions previously in-demand are no longer necessary and new ones are born each day. To get on the wave successfully, it’s essential to train and do it consistently.

Data scientist

Big data is here to stay. Data science takes advantage of the advances of connectivity and Internet penetration to generate, record, and model vast volumes of information following the scientific method. Its objective is to identify, process, and convert large amounts of data into valuable information for decision-making in any field.

What skills do you need to master to be a data scientist?

  • Mathematical and statistical skills.
  • Big data architecture through the use of software such as Hadoop, relational and non-relational databases, and using programs and languages such as Cassandra, MongoDB, MySQL or PostgreSQL.
  • Programming languages ​​such as R, Python, S, C, SAS.
  • Management of databases such as SQL and programming in HIVE.
  • Data visualization programs with software such as Kibana, Tableau, Clip View, or even Excel.
  • Being curious to look for relationships between data points that do not necessarily seem related.


A fundamental ability to be a data scientist that is considered a “soft skill” is to be curious to look for relationships between data that are not connected or logical to each other — an intuitive, exploratory mind is key.


Expert in artificial intelligence


It is not a secret that, in the technological sector, AI experts receive astronomical salaries due to the high demand of this profile and the shortage of specialists.

Artificial intelligence creates systems capable of learning and prediction from reading data — either from other systems or directly from the environment. This information is processed and stored in the form of “knowledge” that is then used to issue recommendations and actions.

As with the introduction of office computing, artificial intelligence will not replace workers as much as it will force them to acquire skills to complement it. As technology changes the skills needed for each profession, workers will have to adjust. That’s why it’s essential to learn about artificial intelligence now, while it’s still in its relative infancy.

What requirements do you need to become a sought-after AI expert?

  • Know the basics of data processing.
  • Master the development of applications or software with programming languages like ​​R, Python, C #, and C++, among others. Unlike traditional software, whose objective is limited and focused on a series of specific tasks, the one used in AI is focused on constant learning.
  • Mastery of big data architecture.
  • Extensive knowledge of machine learning and machine learning software.

The possibilities of developing AI can be grouped into:

  1. Specific– focused on reading information of a single type and provides solutions based on a specific purpose.
  1. General– seeks to copy the multiple ways of thinking and acting, emulating a human being. The AI then decides on their learning patterns and decisions — although this is still not fully developed because it is a vast and complex problem and requires more robust technological solutions.

Society is changing, and that’s why we have to be prepared for the future before it happens. There are new developments in biotechnology, genetic engineering or robotics; these also begin to provide new forms of employment that will be decisive for innovation in the societies of the future.

For entering the world of AI, it is advisable to have studied some software engineering and have a high command of mathematics, statistics, and programming. With the mastery of these skills, you can create systems that use information to generate knowledge and make decisions in the mode of patterns and probabilities. These talents will serve well in the AI-driven economy of the future.

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Domain

How to Increase the Value of a Domain Name

To increase your domain authority is not something that can be done in a few days, it is a medium-term strategy. From the moment you register your domain, ensure that the theme always follows the same line — a domain that radically changes thematically is seen by Google as unreliable and can be penalized. Post new content on a frequent basis which is also useful for the user, increasing the chances of other websites linking that content.

Tips for increasing domain authority now


1. Domain age– The older it is, the more reliable a domain is in the eyes of search engines. If you have been managing a website in your domain for a long time, with which you have gradually gained traffic, for search engines your website is performing the purpose for which it was created, and that has made it a “reliable” site.”

2. The popularity of the domain- The popularity of a domain is measured by the number of websites that link it. SEO and link-building are important factors to determine the reputation of a site and get links back. Getting quality links is one of the most critical tasks to increase your web presence, and you can do it in several ways: writing blogs and articles, commenting on other blogs, writing on forums, press appearances, posting on social networks, etc.

3. Site size– Imagine your website as a tree and your blog posts or individual web pages as the branches of that tree. The more content our website generates, the more likely we are that other users will be able to find this content and that it will linked from other sites. Also, the size of our publication also helps our site to be seen as a purveyor of “quality content.”

Not only does the number of links matter, but they must be high-quality. It is preferable to have a single link from a domain with a high impact, such as a newspaper or a well-known website, than to have several links that come from small and anonymous sites with low results.


How to measure the authority of your domain?



SEO Toolbar MOZ– An extension that you can install in your favorite browser (Google Chrome or Mozilla Firefox) that you can activate to measure the authority of a domain or search in Google, and deactivate when you do not need it (removing fixed bars that occupy an unnecessary space in the browser).

Open Site Explorer– If you do not want to install anything, I recommend that you use this tool that will measure the authority of the domain and tell you the best quality linkage that is contributing substantially to that authority.


Conclusions

  • Having a great domain authority will help you have a good search position for your content.
  • Not all domains of great authority have a great positioning or visibility in the search engine since there are other factors that can make for better or worse SEO; internal links, the health of the back-links, lack of quality content, duplicate content, broken links, etc.
  • The construction of the authority of a website is not built in a day — we have to do it based on effort and work, to gradually reap the results.
  • In order for your domain to have good authority you will need 1 to 2 years, the process can’t be rushed.

The use of social networks can not only help you generate traffic to your website and ensure that the content reaches more users, but it is also another factor that search engines take into account to determine the authority of a domain. If your content is shared and linked by users in different social networks, search engines will interpret that your content is useful and adds value to the user.

One of the best ways to increase the number of times your content is shared on social networks is through videos and infographics that complement your articles. It’s demonstrated that visual content is the one that tends to generate shares in social networks and that results in more virality.

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