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You will have a lot of data when you start a project or a business and you will have some more additional data if/when your business or project grows. Moreover, in the future, you will have to implement Business Intelligence Suite in your project for added data which can be helpful for project data analysis.

But, implementation of Business Intelligence Suite is more about converting the additional data to actionable insights. The amount of data your project or organization can collect today offers the ability to understand which processes are working and help teams prepare for future trends.

However, you will only have some numbers and figures with no context if your project data analysis and comprehension is not proper.

Meanwhile, there isn’t a single right to for project data analysis as it shifts according to your needs and the type of data you collect. There are many different ways to analyze project data, and the approach you take for project data analysis will depend on the type of data you have collected and the specific goals of your project.

Therefore, it is necessary to understand the data type and correlate it with the project data analysis methodology that works the best.

Hence, let us first learn about the types of data that you have to deal with during your project or business.

The difference between Quantitative and Qualitative data

The first and most important step for choosing the best way of project data analysis for your data set is understanding the type of data you have. Basically, you have two types of data – quantitative data and qualitative data.

For the foremost, as the name implies, quantitative data deals with hard numbers and quantities. These numbers or data can include payroll data, revenues, marketing data such as click-through rates, sales numbers and many more similar types of figures that can be quantified.

On the other hand, Qualitative data are comparatively harder to pin down as it pertains to more interpretive and subjective aspects of the project. The qualitative data may include interviews with employees, customer surveys etc.

And, the basic difference in the project data analysis techniques for quantitative and qualitative data is that the project data analysis methods used for qualitative data are less structured than quantitative techniques.

Measuring Quantitative data

Quantitative analysis methods primarily rely on accurate counting and interpretation of data based on facts. Our first three methods for upping your analysis game will focus on quantitative data:

1. Regression analysis

When you have to make predictions and forecast future trends, regression studies are excellent tools. This is so because regressions measure the relationship between a dependent variable (what you want to measure) and an independent variable (the data you use to predict the dependent variable).

Furthermore, you can have a nearly limitless number of independent variables, while having only one dependent variable. Your project can also be optimized with the help of regressions as it can help you uncover the relationship between factors and highlight trends.

Data Analysis has become an integral part of running a smooth and successful business. This is because when the analysis of data is done effectively, it not only gives you a better understanding of your business’s previous performance but, you can make better future decisions too.

There are many different data analysis tools available to help you make sense of your data. Some of these tools are designed for specific types of data, while others are more general purpose. Choosing the right tool for your needs will depend on the type and amount of data you have, as well as your goals for analyzing it.

There are basically four types of data analysis and you can utilize them at all levels of your company’s operations.

The four types of data analysis are:

Descriptive Analysis

Diagnostic Analysis

Predictive Analysis

Prescriptive Analysis

While we are separating these into categories, all of these are related and build upon one another. Likewise, as we move from the simplest type to a comparatively complex one, we will face an increase in the degree of difficulty and resources required.

Fortunately, the level of added insight and value also increases.

Descriptive Analysis

It is the most common and simplest use of data in the industry. In simple terms, Descriptive Data Analysis answers “what happened” by summing up past data.

It simply describes what has happened and doesn’t try to explain why it might have happened. Furthermore, it does not try to establish cause-and-effect relationships and aims to provide just an easily digestible snapshot.

Some good examples of descriptive data analysis in action can be Google Analytics or any other social media analytics.

There are mainly two techniques used in descriptive data analysis:

Data aggregation

Data Mining

Data Aggregation

Simply gathering data and presenting it in a summarized format is data aggregation.

To give you a clear example, e-commerce has a hold of all kinds of data relating to its website or page visitors and customers. The summarized data or aggregate data can provide an overview of the wider data set. For instance, the average number of purchases or the gender of the customer.

Data Mining

This is the analysis part. Data Mining is when the analyst explores the data and tries to uncover any trends or patterns.

The outcome of Descriptive Data Analysis is a visual representation of data as a pie chart or a bar graph.

Diagnostic Analysis

After “what happened” is answered, the succeeding step is delving deeper into “why it happened?”

Diagnostic Data Analysis takes the insights from descriptive data analysis and dives down to find the causes for those outcomes. This type of data analysis is mostly used to create more connections between data as well as identify behavior patterns.

To give you a clear indication of how diagnostic analysis is performed, here is an example:

Your website or page may have a healthy volume of website visitors and many visitors may have used the add-to-cart action. However, there might be a drop in sales occurred because very few visitors actually proceeded to make a purchase.

In this blog, we will learn how to setup node_exporter in Ubuntu server. Then we will configure Prometheus to scrape data from node_exporter and configure Grafana to display various metrics of mysql database in Grafana for mysql monitoring.

If you read my previous blog we learnt how to setup cAdvisor for container monitoring. Likewise, in case of mysql monitoring, we need a client agent similar to cAdvisor.

We will use mysqld_exporter as a client agent and set it up in Ubuntu server directly. We won’t be setting mysqld_exporter in a container this time. It can get MySQL metrics and export it to Prometheus server.

Then, we can easily perform mysql database monitoring using grafana by visualizing various mysql metrics such as mysql uptime, Query per second (QPS), total number of connections, etc. All these metrics are pulled by grafana from prometheus.

PART 1: CREATING A USER FOR MYSQL MONITORING AGENT:

sudo groupadd --system prometheus

sudo useradd -s /sbin/nologin --system -g prometheus prometheus

Now lets download mysql exporter and install it:

curl -s https://api.github.com/repos/prometheus/mysqld_exporter/releases/latest | grep browser_download_url | grep linux-amd64 | cut -d '"' -f 4 | wget -qi -

tar xvf mysqld_exporter*.tar.gz

sudo mv mysqld_exporter-*.linux-amd64/mysqld_exporter /usr/local/bin/

sudo chmod +x /usr/local/bin/mysqld_exporter

mysqld_exporter --version

PART 2: CREATE PROMETHEUS EXPORTER DATABASE USER:

Login to your mysql as root and create a user with PROCESS, SELECT, REPLICATION CLIENT grants:

CREATE USER 'mysqld_exporter'@'localhost' IDENTIFIED BY 'YourPassword' WITH MAX_USER_CONNECTIONS 2;

CREATE USER 'mysqld_exporter'@'%' IDENTIFIED BY 'YourPassword' WITH MAX_USER_CONNECTIONS 2;

GRANT PROCESS, REPLICATION CLIENT, SELECT ON *.* TO 'mysqld_exporter'@'localhost';

GRANT PROCESS, REPLICATION CLIENT, SELECT ON *.* TO 'mysqld_exporter'@'%';

FLUSH PRIVILEGES;

EXIT

Here, WITH MAX_USER_CONNECTIONS 2 is used to set a max connection limit for the user to avoid overloading the server with monitoring scrapes under heavy load.

Cloud security architecture is a strategy for safeguarding data and applications in the cloud for a company. It’s a crucial component of business security.

In addition, it necessitates an architecture to link it to a broader security strategy. The cloud provider and the consumer share responsibilities for security in cloud architecture.

Matter of fact, the need of having a security architecture in place to safeguard data grows as more enterprises transfer and share their data on the cloud.

The cloud may be accessed in a variety of ways. As a result, cloud security designs are built to function in a variety of contexts, including software as a service (SaaS), platform as a service (PaaS), infrastructure as a service (IaaS), etc.

The foundation of cloud security architecture is shared accountability between an enterprise and a cloud provider. However, this does not imply that an organization has less duty.

A cloud security architecture should, in general, adhere to cloud security best practices. The roles and obligations of each partner may vary depending on the cloud provider’s services and how they are delivered.

The security layers, design, and structure of the platform, tools, software, infrastructure, and best practices that exist inside a cloud security solution comprise a cloud security architecture.

Cloud security architecture covers all the following things:

Identity and access management.

Methods and controls to protect applications and data.

Approaches to gain and maintain visibility into compliance, threat posture, and overall security.

Processes for instilling security principles into cloud service development and operations policies and governance.

So, one question might raise after knowing about cloud security architecture. And that is how to secure data in the cloud. For that we enlisted followings:

Recognize the benefits and drawbacks of Cloud Computing.

Determine which data is sensitive or controlled. The loss or theft of data, which might result in regulatory penalties or the loss of intellectual property, is your most significant risk.

Data categorization engines can help you categorize your information so you can analyze the risk adequately. Gain a better understanding of how sensitive data is accessed and shared.

Sensitive information can be safely stored on the cloud, but you must keep track of who has access to it and where it travels. Examine file and folder permissions in your cloud environment, as well as access contexts such as user roles, user location, and device type.

Find out about shadow IT (unknown cloud use). Before signing up for a cloud storage account or converting a PDF online, most customers do not consult their IT department.

According to the research, it shows the popularity of data analysis is constantly growing. In 2015, only 17% of organizations were using big data analysis. Now, the number is rapidly increasing and making it to 59% of organizations which are using it.

Python for data analysis is a programming language that is designed to perform statistical computations and data analytics.

In the same way, Python is a popular programming language. It is used for web development, mobile applications, operating systems, data science, machine learning, artificial intelligence, etc.

It is considered one of the easiest programming languages worldwide. Most beginners start from this language.

Since, Python is an easy and versatile programming language, the job opportunities for Python are also increasing day by day.

Nowadays, technology plays a vital role in different kinds of organizations. It is a key factor for an organization to get success.

One such ingredient that, if used properly, will define a company’s success. And, it is data analysis.

What is Data Analysis?

Talking about different advanced technologies, data analysis comes into play. Data analysis is important in business to understand the problems and to explore data in order to solve those problems.

Data analysis is the methodology of gathering data and processing the data to make it useful. Data analysis helps to obtain accurate data for the future planning of the company. As a result, it helps to narrow down the main thing to focus on for uplifting the company.

Python and Data Analysis

So, the question might arise about what is the connection between Python and data analysis. Now, we will be discussing it in detail.

We know data analysis is important for the organization. Meanwhile, what are the best options for data analysis?

There are lots of programming languages for data analysis. Such as Java. JavaScript, Python, C#, C++, etc. Python is considered to be the best programming language for data analysis.

If you’re involved in marketing or are a business owner, you’ve probably heard about SEO (Search Engine Optimization).

That’s because it’s now a vital component of every effective marketing plan.

SEO will promote your website in a variety of ways by assisting you in achieving the following goals:

Improved visibility and ranking

Long term growth

Providing a better user experience on every platform

Improves creditability

It Gives your business a competitive advantage over your competitors

SEO will provide a long term return and is a low-cost marketing method

SEO’s aims are straightforward: to assist your business’s expansion through an internet search.

Search Engine Optimization may help your organization build an online presence and expand organically if done correctly.

However, determining how SEO adds to business success can be difficult. That is why, in this blog, we will clarify why SEO is so crucial, as well as the impacts of SEO on your website.

Advantages of SEO for Your Website

The advantages of SEO for businesses are virtually limitless, and making use of them may increase the success of your brand in the industry.

Let’s take a look at some important advantages of SEO for your website.

SEO Reaches the Target Audience

Search engine optimization (SEO) is a powerful inbound marketing tactic.

Using a well-executed SEO ensures that the clients you’ve targeted will discover what they’re searching for once they arrive on your site.

In contrast to typical outbound promotional methods that entail reaching out to customers if they would like to hear from you or not, inbound strategies focus on making it simple for your audience to discover you whenever they need information.

Inbound marketing is considerably more focused on the consumer. It is because the costumers can find about your products on their own.

SEO Promotes Better Cost Management

You don’t need to advertise your website or pay per click (PPC) if you currently have a high position in online search engines.

It implies that if you’ve built a website that the search engine thinks it deserves to refer visitors to, it may persist in driving traffic to your web page for years after it’s been published.

SEO does require an initial investment for high-quality content writing if you hire a professional. However, after that, there are no further ongoing costs to attract visitors to your content.

It Gives You a Competitive Advantage

Brands that invest substantially in SEO frequently outrank their competitors and win the market share.

When you know that the top page of Google search results receives over 80% of the customer traffic, you can’t risk not being there, particularly if your rivals are.

Begin by identifying your rivals, studying their strengths and shortcomings, and establishing and maintaining an online presence.

Next, make sure your site loads quickly and is mobile-friendly, provide the greatest content you can, and focus on giving your users the best experience possible.

The system development life cycle (“SDLC” for short) allows users to migrate newly developed projects to operational projects. The System Development Life Cycle phase is a systematically structured, multi-step iterative process.

This process is used to model technical and non-technical activities, to provide a framework for providing quality systems that meet or exceed organizational expectations, or to guide decision-making progress. increase.

Traditionally, the system development life cycle consisted of 5 to 7 phases. By increasing the number of steps, systems analysts were able to define clearer actions to achieve specific goals. Similar to the Project Lifecycle (PLC), Software Development Life Cycle uses a systematic approach to describe the process.

Often used and tracked when an IT or IS project is under development. The Software Development Life Cycle highlights various stages (phases or steps) of the development process.

The Software Development Life Cycle approach is used to help users see and understand the activities involved in a particular step. It is also used to let you know that you can repeat a step at any time or modify a previous step if you need to change or improve your system.

The seven phases of the SDLCPlanning

This is the first phase of the system development process. Before you start the planning phase, it’s a good idea to take the time to get a good understanding of your app development lifecycle.

The planning phase (also known as the feasibility phase) sounds exactly like that. In other words, it’s the phase where developers plan their next project. Helps define problems and the scope of existing systems and determine goals for new systems.

By creating an effective blueprint for the next development cycle, you can theoretically identify the problem before it affects development. And it helps to secure the funds and resources needed to realize the plan.

Perhaps most importantly, the project timeline is set during the planning phase. This can be important when developing commercial products that need to be put on the market by a specific date. It identifies whether a new system is needed to achieve a company’s strategic goals.

This is a preliminary plan (or feasibility study) of a company’s business initiative to acquire resources to build an infrastructure to change or improve services. Companies may also try to meet or exceed the expectations of their employees, customers, and stakeholders.

The purpose of this step is to find the scope of the problem and determine the solution. In this phase, you need to consider resources, costs, time, benefits, and other factors.

Systems Analysis and Requirements

In the second phase, the organization addresses the root cause of the problem or the need for change. If you encounter a problem, present and analyze possible solutions to determine the best solution for your project’s ultimate goals.

Here, the team considers the functional requirements of the project or solution. This is also where system analysis takes place. Or an analysis of the end-user needs to ensure that the new system meets expectations.

Systems analysis is important in determining what a company’s needs are, how they can be met, who is responsible for the individual parts of the project, and what type of time frame is expected in system development life cycle phases.

There are several tools that companies can use especially in the second phase of system development life cycle phases. These include:

CASE (Computer Aided Systems / Software Engineering)

Collection of requirements

Structured analysis

Systems Design

Phase 3 details the specifications, features, and operations required to meet the functional requirements of the proposed system development life cycle phases. This is a step for the end-user to discuss and determine the specific business information requirements of the proposed system.

This phase explores the components (hardware and/or software), structure (network capabilities), processes, and procedures that are essential for a system to achieve its goals.

Development

In the fourth phase of the system development life cycle phase, the actual work begins. Especially when programmers, network engineers, and database developers are brought in to do the main work of the project.

Matter of fact, this task uses flowcharts to ensure that the processes in your system are well organized. The development phase marks the end of the first phase of the process. This phase also marks the start of production.

The development stage is also characterized by infusions and changes. Focusing on training can be of great benefit at this stage of system development life cycle phases.

Integration and Testing

Creating software is not the end. Now we need to test to make sure there are no bugs and no negative impact on the end-user experience.

During the testing phase of the system development life cycle phases, developers examine the software with a fine-toothed comb to note bugs and defects that need to be tracked, fixed, and retested later. It is important that the entire software meets the quality standards previously defined in the SRS documentation.

According to the research, it shows the popularity of data analysis is constantly growing. In 2015, only 17% of organizations were using big data analysis. Now, the number is rapidly increasing and making it to 59% of organizations which are using it.

Python for data analysis is a programming language that is designed to perform statistical computations and data analytics.

In the same way, Python is a popular programming language. It is used for web development, mobile applications, operating systems, data science, machine learning, artificial intelligence, etc.

It is considered one of the easiest programming languages worldwide. Most beginners start from this language.

94% of the business enterprises use the cloud. It leads to huge threats in security to companies using the cloud.

Cloud security is a cybersecurity field that specializes in protecting the security of cloud computing systems. This includes data confidentiality and security across online-based infrastructure, applications, and platforms.

Protecting these systems requires the efforts of cloud providers and their customers, whether used by individuals, small businesses, or businesses. The cloud provider hosts the service on the server over an always-on internet connection.

Because their business relies on customer trust, they use cloud security techniques to keep their customer data private and secure. However, cloud security is also partially in the hands of the customer. Understanding both aspects is important for a healthy cloud security solution.

At its core, cloud security is composed of the following categories:

Data security

Identity and access management (IAM)

Governance (policies on threat prevention, detection, and mitigation)

Data retention (DR) and business continuity (BC) planning

Legal compliance

Cloud security may look like legacy IT security, but this framework actually requires a different approach. Before we dive deeper, let’s first look at what cloud security is.

What is Cloud Security?

Cloud security is a set of technologies, protocols, and best practices that protect cloud computing environments, applications running in the cloud, and data stored in the cloud. Protecting cloud services begins with understanding what exactly you need to protect and the aspects of your system that you need to manage.

In summary, backend development to protect against vulnerabilities is largely in the hands of cloud service providers. Beyond choosing a security-conscious provider, the most important thing customers need to focus on is proper service configuration and secure usage habits. In addition, customers must ensure that all end-user hardware and networks are properly protected.

The main objective of cloud security is to secure the data, regardless of your responsibilities:

Physical networks — routers, electrical power, cabling, climate controls, etc.

Data storage — hard drives, etc.

Data servers — core network computing hardware and software

Computer virtualization frameworks — virtual machine software, host machines, and guest machines

Operating systems (OS) — software that houses

Middleware — application programming interface (API) management,

Runtime environments — execution and upkeep of a running program

Data — all the information stored, modified, and accessed

Applications — traditional software services (email, tax software, productivity suites, etc.)

End-user hardware — computers, mobile devices, etc.

Also, Read, Secure your Cloud and On-premise Infrastructure with Open-source tools>>

How is Cloud Security different from others?

Traditional IT security has evolved significantly with the move to cloud-based computing. The cloud model is more convenient, but always-on connections require a new way of thinking to stay secure. Cloud security as a modern cybersecurity solution differs from older IT models in many ways.

According to the research, 1.01 billion dollars in credit card transactions occur every day around the world in 2018. Now, the numbers are increasing day by day after the covid-19 wave.

Transaction Processing System helps to work in the real-time-based system. It is used in railway reservation systems, accounting systems, banking systems, etc.

There are different types of Transaction Processing Systems such as payroll, order entry, inventory control, accounts payable, etc.

Matter of fact, the Transaction Processing System is the major factor for the rise in the global economy.

What is a Transaction Processing System?

A Transaction Processing System is an information processing system that collects and stores data about business transactions and sometimes controls decisions made as part of a transaction. The transaction is the process of exchanging goods, services in terms of money.

Advantages of Transaction Processing System

1. Real-time processing

This system is based on real-time. It helps in the execution of data in a short period, providing near-instantaneous output.

Good examples of real-time data processing systems are bank ATMs, traffic control systems, and modern computer systems such as the PC and mobile devices.

2. Increase in productivity and customer satisfaction

If the system is fast, then it leads to an increase in productivity and customer satisfaction. And, the Transaction Processing System is also one the fastest systems.

3. Security

Security is one of the main key factors. Similarly, it helps to make good security of the users while doing multiple transactions.

4. Systematic

Simply, a part or unit of the system cannot work solely. It needs whole parts to work successfully.

Meanwhile, working successfully, it provides a systematic facility to the users.

What are the components of the Transaction Processing System?

1. Input

Source documents such as customer orders, invoices, purchase orders, etc. serve as Inputs to the Transaction Processing System.

2. Processing

Once the inputs are provided, they are further processed to get an output.

3. Storage

Ledgers serve as a source of storage. Examples of ledger accounts are cash, account receivable, inventory, fixed assets, accounts payable accrued expenses, debt, stockholders’ equity, revenue, cost of goods sold, salaries and wages, offices expenses, depreciation, and income tax expense.

4. Output

Any document generated is termed output.