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What is Centralized Database?

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Why do we Need a Centralized Database? Functions of a Centralized Database

The large organizations use Centralized Database so that the data can be available at a single place which will improve their decision making process. The basic function of a Centralised Database is to have a complete view of the data of the organization. Having a complete view of data reduces the time consumption in implementing the actions as the organizations do not have to analyze the data separately for various departments. Another function of the Centralised Database is that it can be managed easily than the distributed database systems. Also the Centralised Databases do not have the complexity in the design of the system like the distributed databases.


Centralized Database ensures a higher level of security. There are certain protocols established for the people associated or working with the data. So that the access of the data can be monitored and it reduces the amount of data leakage which in turn increases the data security.

The usage of a Centralised Database increases the integrity of data. The data is present at one physical location which makes the organization of the data easier and this ensures that the data is consistent and correct.

The Centralised Database consumes less power and maintenance, so it is cost effective.

The usage of a Centralised Database also increases the efficiency in the operation. For example, as the data is controlled by a central business unit, the resources used previously for data management can be utilized for other business areas.

Centralized Database becomes very useful in case of projects where people from various functions are involved. In such a case, the centralized database helps in removing any barrier of communication among the various teams as it removes the many layers of data and presents the data of the organization in one place. Also with the presence of a centralized database, the sharing of ideas across the teams becomes even more flexible.

The usage of a Centralised Database increases the efficiency of the organizations as it helps in keeping focus on the goal. Also, it helps the organizations making decisions quickly and implementing their strategies faster as the layers of data are reduced which also reduces the time consumption for the analysis of the data.

Centralized Database may reduce the efficiency of the system in case of multiple users trying to access the data simultaneously as the data is present at one location. There may be the issue of traffic for the accessing of data from the Centralised Database.

Another important issue that might generate from using the Centralised Database is data loss. As the data is present at one location, in case of any failure if the database is lost, and if there is no provision for back up, there is always the risk of losing the data. This might create an outage for a long time and the organization loses access to the data which will impact the business negatively.

If there is any issue with the accuracy of the data, it will affect the organization because in case of any wrong data present in the database, the cost of correcting the data becomes more.

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Learn Why Do We Need Doxygen And How It Works?

Introduction to Doxygen

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Why do we need Doxygen?

As already explained, Doxygen is a tool that helps in writing reference documentation for different programming language such as Java, C, C++, C#, IDL, D etc.

In addition to that, Doxygen can be used for the

creation of template configuration file,

generation of documentation using a conf file which is already available

updating of the old configuration file, etc.

More details on these will be explained under the heading Uses of Doxygen.

How Doxygen works?

Now, we are going to look at the working below:

1. Select the programming language

For working in Doxygen, first, there should be an assurance that the programming language you are about to choose is recognized by this tool. If not, then choose another method. Normally, Doxygen supports different programming languages such as C, C++, IDL, C#, Java, Objective C, VHDL, Python, D, PHP and Fortran.

2. Run Doxygen

Once this command is executed, an html, latex, rtf, xml, docbook or man directories will be created within the output directory. The type of document depends on the settings you have provided in the doxygen. From the type of files themselves, we can understand that these directories consist of the documentation in RTF, HTML, Unix-Man page, LATEX, DocBook and XML format.

In this, the default output directory is the one in which it starts. Moreover,  the root directory where the output will be written to can be changed with the help of OUTPUT_DIRECTORY. Inside this OUTPUT_DIRECTORY, the directory which is format-specific can be chosen using DOCBOOK_OUTPUT, HTML_OUTPUT, MAN_OUTPUT, RTF_OUTPUT, XML_OUTPUT, and LATEX_OUTPUT.tags of the conf file. Similar to the creation of the conf file, if the output directory also does not exist, the doxygen will be creating one.

3. Documentation of the sources

Even though the source documentation is explained as the third step, it should be explained as the first step in the case of a new project. Suppose the option EXTRACT_ALL is marked as No in the conf file; documentation will be generated by doxygen only for the documented entities. In order to document these, there are two methods, especially for classes, members and namespaces. The first one is to set a specific documentation block prior to the member/class/name space declaration or definition for file, class and namespace members; it is also allowed to place the documentation directly after the member.

Advantages are:

Only less overhead is required from the one who writes the documentation.

Documentation of unions, files, templates, namespaces, enums, classes, functions, structs, typedefs, variables, and defines is supported.

Compatible with JavaDoc (1.1), KDOC and Qt-Doc

Class diagrams are generated automatically in HTML

Dependency graphs, graphical class hierarchy graphical graphs etc., can be created using the tool known as a dot which is available in the tool kit known as Graphviz.

Documentation can be kept within the header file, source file or in another file.

A list of class members can be generated with their protection level.

All the sections which are public, private as well as protected can be automatically detected.

Transferring to other machine or location is possible for the documentation as well as a search engine.

Easy to work even in large projects.

Client side searching is limited to symbols only rather than ful text searching.

Server side searching does not behave properly while working locally.

Compilation of the CHM file works on only Windows

Mac OSX and Xcode works only in combination.

Usage of Doxygen

As already explained. It can be used to create template configuration files, generate documentation using a conf file, etc. Now, we will see how.

Generation of template conf file: doxygen [-s] -g [confName]

Updation of an old conf file: doxygen [-s] -u [confName]

Generation of documentation using an already available configuration file: doxygen [confName]

Generation of a RTF/HTML/Latex template stylesheet file : doxygen -w rtf styleSheetFile

Generation of extension file of RTF : doxygen -e rtf extensionsFile


It is a tool that helps in writing reference documentation for different programming languages.  In this article, different aspects such as usage, working, pros, and cons are explained in detail.

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Why Do We Use Pandas In Python?

Pandas has been one of the most commonly used tools for Data Science and Machine learning, which is used for data cleaning and analysis.

Here, Pandas is the best tool for handling this real-world messy data. And pandas is one of the open-source python packages built on top of NumPy.

Handling data using pandas is very fast and effective by using pandas Series and data frame, these two pandas data structures will help you to manipulate data in various ways.

Based on the features available in pandas we can say pandas is best for handling data. It can handle missing data, cleaning up the data and it supports multiple file formats. This means it can read or load data in many formats like CSV, Excel, SQL, etc.,

Let’s take an example and see how it’s gonna read CSV data.

Example data = pd.read_csv('world-happiness-report.csv') print(data.shape) data.head() Explanation

In the above code, variable data stores CSV data which is a world happiness report (downloaded from Kaggle datasets) by using the read_csv function available in the pandas package. data.shape is used to give you the columns and row count.

Output       Country name year  Life Ladder   Log GDP per capita Social support 0   Afghanistan    2008    3.724               7.370           0.451 1   Afghanistan    2009    4.402               7.540           0.552 2   Afghanistan    2010    4.758               7.647           0.539 3   Afghanistan    2011    3.832               7.620           0.521 4   Afghanistan    2012    3.783               7.705           0.521 Healthy life expectancy at birth   Freedom to make life choices   Generosity                            50.80                          0.718       0.168                            51.20                          0.679       0.190                            51.60                          0.600       0.121                            51.92                          0.496       0.162                            52.24                          0.531       0.236 Perceptions of corruption   Positive affect   Negative affect                    0.882             0.518             0.258                    0.850             0.584             0.237                    0.707             0.618             0.275                    0.731             0.611             0.267                    0.776           0.710 0.268

The above block has the top 5 rows of data in the world’s happiness report data set that can be displayed by pandas dataframe.head() function.

There are many more features that help us to deal with large data for both machine learning data science operations. Which are merging and joining data sets, Visualization, grouping, masking, and also is very helpful for performing mathematical operations on our data sets.

Let’s take another example and see how to create an output file using pandas.

Example file = data.to_json('output_file.json') Explanation

Data.to_json is a pandas function that is used to create a JSON file based on our pandas dataframe object (data).


The resultant JSON file will be created in our working directory with an extension of .json and the name of the file is output_file (for our above example).

These are some reasons why we need python pandas.

Why Do I Need Underscores In Swift?

In Swift, underscores have many different uses for a different purposes. Here are some examples.

Ignore unnecessary loop variables or return values.

Absence of identifiers for external parameters in function calls.

Even if they were initially specified as constants, making variables changeable.

Ignoring tuple components or using discard values when managing errors.

To Ignore a Value

To ignore a value that a function or method returns in Swift, use an underscore. You could compose something like this, for instance, if you only worry about an operation’s success or failure. This is the most common case you use in your code.

Syntax if _ = someFunction() { } else { } Example

In this example, the greetingMessage() function returns a string value, but we’re ignoring it by using an underscore in the conditional statement. Instead, we’re just checking whether the function succeeded or failed based on whether it returned a value or not.

import Foundation return "Good morning, (fullName)" } if let _ = greetingMessage(fullName: "Alex Murphy") { print("Function successfully executed.") } else { print("Function failed to execute.") } Output Function successfully executed. To Omit External Parameter Names

In Swift, you can use an underscore to omit the external parameter names for a function or method. This concept is widely used in iOS applications. For example, if you have a function like this −

syntax func doSomething(_ value: Int) { } Example

In this example, the greetingMessage() function takes a string parameter named fullName, but we’re omitting the external parameter name by using an underscore in the function declaration. When we call the function, we just provide a string value without specifying the parameter name.

import Foundation return "Good morning, (fullName)" } if let _ = greetingMessage("Alex Murphy") { print("Function successfully executed.") } else { print("Function failed to execute.") } Output Function successfully executed. To Make a Variable Mutable

In Swift, you can use an underscore to make a variable mutable. For example, if you have a constant like this −


In this example, we’re creating a mutable variable called x by adding an underscore before its name. We’re initializing it with the value of x, which is a constant. We’re then adding 10 to x and printing its value. Since _x is mutable, we can modify its value even though it was originally derived from a constant value.

import Foundation let x = 42 var _x = x _x += 10 print(_x) Output 52 Ignoring a Loop Variable

You can ignore the variable while performing anything using a loop.


In this example, we’re using an underscore to ignore the loop variable in a for loop. We’re simply iterating over the numbers array and incrementing the sum variable for each element in the array. Since we don’t need to use the loop variable (which would normally be an integer index), we can use an underscore to indicate that we’re ignoring it.

import Foundation let numbers = [1, 2, 3, 4, 5] var sum = 0 for _ in numbers { sum += 1 } print("Number of elements: (sum)") Output Number of elements: 5 Ignoring Part of a Tuple

A tuple contains multiple values together. In order to use the specific values, you can ignore the part of a tuple.


In this example, we’re using an underscore to ignore the y component of a tuple. We’re then using pattern matching to extract the x and z components of the tuple and assign them to local variables. Since we don’t need the y component, we can use an underscore to ignore it.

import Foundation let point = (x: 10, y: 20, z: 30) let (x, _, z) = point print("All values: (point)") print("x: (x), z: (z)") Output All values: (x: 10, y: 20, z: 30) x: 10, z: 30 Ignoring a Throw Value

While you use the do-catch statement to manage the errors in your code, you can ignore the thrown value.


In this example, we’re using an underscore to ignore the error that might be thrown by the doSomething() function. We’re calling the function inside a do block and using a catch block to handle any errors that might be thrown. Since we don’t care about the specific error value, we can use an underscore to ignore it and just print a generic failure message.

import Foundation return 42 } do { let result = try doSomething() print("Success: (result)") } catch _ { print("Failure") } Output Success: 42 When Using Underscores in Swift, Keep the Following Extra Considerations in Mind

Underscoring is not required. Code can frequently be written without underlining and still produce the same results. However, using underscores can help you write more legible and succinct code.

If you use underscores overly or improperly, your code may become more difficult to comprehend. For instance, using an underscore to hide a crucial mistake number in a function call may result in errors or strange behavior.

Underscores can have different meanings based on the situation in which they are used. For instance, an underscore used in a switch expression as a wildcard pattern and one used to ignore a loop variable have distinct meanings.

Underscores can be combined with other language features to create powerful abstractions. For example, combining underscores with closure syntax can create very concise and readable code.

Underscores are not specific to Swift and are used in many other programming languages for similar purposes.


It is possible to make your code more concise and easier to read by using underscores, especially when you don’t need to use a specific value or parameter name. It’s important to use underscores judiciously and avoid using them in a way that might complicate your code. Using underscores in your code should make the code clearer and easier to maintain, as is the case with any language feature.

Do You Need A 24Gb Ram Phone?

Moreover, the upcoming device from OnePlus, the OnePlus ACE 2 Pro, is expected to feature up to 24GB of RAM. This might raise the question, do you really need that amount of RAM on your phone? I mean, 16GB of RAM is typically considered excellent for computers., even for gaming. So, do 24GB RAM phone models make sense? Let’s delve deeper into the subject for a comprehensive understanding.

Why Does Your Phone Need RAM?

Before anything else, you need to have a proper understanding of what RAM actually is. At the core, it is one of the fastest memory in your phone. It is faster than the onboard storage but slower than the cache that remains embedded with the phone’s processor. But why do phones need RAM for?

Thanks to being exceptionally faster than onboard storage, operating systems use it to load up and run apps. If you were to make the operating system or apps run on regular storage, it would take you hours just to send a text message. Yes, RAM in phones is that crucial.

How Much RAM Do You Actually Need?

To understand how much RAM your phone needs, consider your requirements and the demand for the device. Let’s break things down to make it easier for you:

You Need At Least 2GB of RAM to Run Android

If you are to use the phone for basic tasks such as making a call, receiving a call, listening to local music, and texting, 2GB of RAM is more than enough. Before Android 13, the minimum RAM requirement was 1GB. But with the release of the new Android version, Google has increased the minimum requirement.

Essential Android Apps Needs At Least 4GB of RAM to Run Smoothly

Get At Least 8GB RAM Phone If You Want to Play Games

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However, if you are someone who tends to keep a lot of tabs open in Google Chrome, it would be a good idea to get a 12GB RAM phone model. After all, Google Chrome itself requires a good amount of RAM to operate optimally. Most Chinese manufacturers offer a minimum of 12GB in their flagships for this specific reason.

Another Important Consideration: More RAM Does Not Always Mean Better Experience

As we are talking about phone RAMs, it is better to look into the topic fully. So, a higher amount of RAM will not always mean a high-end experience. The processor must be able to utilize the amount of RAM the phone comes with.

For example, pairing a Snapdragon 600 series CPU with 16GB of RAM is a waste. 8GB or 12GB is more than enough for that SoC. Wondering why? Well, low to mid-range processors can not utilize higher amounts of resources.

Therefore, even if you pair them up with 24GB RAM, you will not get smoother performance in demanding tasks.

So, Should You Not Purchase a 24GB RAM Phone?

You should purchase a 24GB RAM phone if you want to stay highly productive and if the phone comes with high-end specs. The just-announced Red Magic 8S Pro+ is a prime example of such a phone. It is currently the best Android phone you can get in terms of hardware.

Most importantly, the phone comes with Snapdragon 8+ Gen 2, which is ideal for 24GB of RAM. It is a flagship processor that can utilize all the resources that it gets.

When it comes to productivity, a properly kitted phone with 24GB of RAM will let you keep multiple apps open. That enables you to multitask with ease. And the upcoming Android 14 has multiple multitasking features. So, with a 24GB RAM phone now, you will stay a bit futureproofed too.

Moreover, the current 24GB RAM phones are primarily targeting gamers. And they come with gaming-centric features to let the users get the best possible gaming experience. If you are a gamer, a 24GB RAM phone with good overall specs can surely be worth it for you.

Chatgpt Professional: What Is It & Why Do You Need It?

ChatGPT Professional: What Is It & Why Do You Need It? You will need to pay a subscription to use the ChatGPT Professional






To fix Windows PC system issues, you will need a dedicated tool

Fortect is a tool that does not simply cleans up your PC, but has a repository with several millions of Windows System files stored in their initial version. When your PC encounters a problem, Fortect will fix it for you, by replacing bad files with fresh versions. To fix your current PC issue, here are the steps you need to take:

Download Fortect and install it on your PC.

Start the tool’s scanning process to look for corrupt files that are the source of your problem

Fortect has been downloaded by


readers this month.

ChatGPT is an AI-powered chatbot built on OpenAI’s GPT-3 with large question-answering models that help it to understand and respond to human conversations. It has been free for users since it is in the trial stage.

However, OpenAI is planning to release the premium version of ChatGPT Professional or Plus. Keep reading to find out all about this new tool.

What is ChatGPT Professional or Plus?

Since the release of OpenAI’s ChatGPT, it has been free to use because it was in its trials and user-feedback stage. Moving forward, Microsoft’s OpenAI has launched a paid version of the chatbot and says it is due to the need to ensure the tool’s long-term viability.

However, ChatGPT Professional is a monetized version. It requires users to pay a monthly fee to access the exclusive features of the Professional. The ChatGPT Professional introduces priority access to new features, increases functionality with no limits, and quicker reaction times.

In fact, the newer ChatGPT version available for paying customers is known as Turbo and it’s optimized for speed.

Furthermore, the ChatGPT Pro version doesn’t take away the rights of users now willing to pay for the tool. The free plan allows you to use it how it’s been since its first release.

Our readers can check how to bypass the ChatGPT not available for your country issue on their PC.

What are the differences between ChatGPT and ChatGPT Plus?

Larger dataset – OpenAI says that the paid version has a larger training set. That translates into better, more precise answers to queries.

Better support and resources – ChatGPT Plus users will have access to tutorials, documentation, and will be able to benefit from customer support for guidance to maximize the tool’s potential.

Ready-made API – Right now, you can’t access the OpenAI API but with a ChatGPT Plus account, you will be able to access the ChatGPT API, and incorporate it into your own software and infrastructure.

Turbo responses – The paying customers will access a much faster, Turbo version of the tool, with lightning-fast responses.

High volume of requests – Right now, in the free version, a lot of users exceed their quota of requests. With ChatGPT Plus, you will have a much larger quota fitted for organization usage.

How much ChatGPT Plus costs?

Expert tip:

The Professional version of OpenAI’s ChatGPT Plus is available on a $20 monthly plan with many features. However, there may be other plans to make a variety for users to choose from in the future.

Unfortunately, not! Even for a paid subscription to ChatGPT Plus there is a waiting list.

Although there was an official release of this service, OpenAI officials didn’t provide a launch date for public availability.

Why do I need ChatGPT Plus?

The free version of ChatGPT has worked okay since its release. Interestingly, the ChatGPT Professional has premium features and tools only for paid users. Some of the premium features that you will enjoy are:

It’ll be available at all times, even during high demand. It guarantees no blackout periods.

Another reason you need the ChatGPT Pro version is because it guarantees faster response speed, breaking every impediment that may draw back its performance.

Premium users have priority access to new features on the platform. It offers more quota and resources than the free plan.

According to OpenAI, many other features will be added to the Professional ChatGPT version to make it more valuable.

In addition, you may be interested in our guide on ChatGPT’s too many redirects error and how to fix this issue.

Likewise, you can read about the best ChatGPT alternatives that compete with the original app.

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