Automation – DevopsCurry

An Brief Introduction On Automation Testing

Thu, 08 Aug 2024 07:50:26 +0000

In today’s article, we will be talking about what is automation testing in software development, what are its types and frameworks, and more…

What is Automation Testing? A Beginner’s Guide

Testing is a very crucial phase of software development. It ensures the software or application is ready to enter the market. Different kinds of tests help the development teams find anomalies in various aspects of the software like user interface (UI), security, performance, etc. However, as crucial as it is, testing also demands significant time and energy.

Especially in the times of manual testing, the testing phase involved the following challenges:

Manual testing of each code and feature was time-consuming and tedious.
It mostly involved boring, repetitive tasks.
It required a separate group of manual testers which added to the business’s costs.
Like any other manually performed task, it was prone to human error.

In short, manual testing was inefficient and unproductive. Resources that could be spent on designing and developing new features, were instead spent on testing the old ones. This inefficiency called for automated testing or test automation…

What is automation testing?

Automation testing or test automation simply refers to the use of tools to automate the testing phase of software development.

It involves the following processes:

Firstly, developers create a test script using a programming language. A test script is a set of instructions that an automation tool is supposed to follow to verify an application. In other words, it tells the tool what to do in order to confirm if the application is working or not.
Then the test scripts are run using the automation tools and the data is collected and analyzed.
Failed tests indicate problems in the software. The tests can be re-run after corrections.

Benefits of automation testing

Automation testing requires minimal human intervention, thus allowing the development teams to focus on designing and innovating new features and updates.
It saves time and costs while maintaining a high quality of work.
It helps in performing more number of tests in less time, thus improving test coverage when compared to manual testing.
It helps in the early detection of defects.
It reduces the chances of the human-error which was quite inevitable in the case of manual testing.

Types of test automation

Unit testing: Unit testing is the first phase of testing. It involves testing individual units or components of the software in isolation. It helps in the early detection of bugs and flaws.
Integration testing: After unit testing, comes integration testing which tests the integrity of the software. It makes sure that all the units or components effortlessly function together to make the software work as a whole.
Smoke testing: Also known as ‘build verification testing’, smoke testing is used to check the stability of the application. If the application passes this test, it goes for further testing. Smoke testing is usually done by QA (Quality Assurance) engineers.
Regression testing: Regression testing is used to ensure that the new codes or updates do not negatively affect the existing features. It is run after every time a new code changeis developed.
Performance testing: As the name suggests, performance testing is used to test the performance i.e. the speed and responsiveness of the software under a particular workload. It helps to optimize the software during peak hours and heavier workloads.

Test automation framework & its types

Test automation frameworks are a set of guidelines, tools, and practices that facilitate software testing. They include test scripts, coding standards, libraries, etc. to speed up test execution and aid in continuous testing. Linear, modular-based, and hybrid are some of the most common types of frameworks we will be talking about here:

Linear framework: Linear framework is the most basic and common framework used for smaller applications. In this, the tester interacts with the software in a defined order while the framework records its actions in a script. Now, the tester simply plays back the script to automatically perform the testing. Hence, it is also called a ‘record and playback’ Because of its simplicity, it requires minimal expertise and is suitable for beginners.
Modular-based framework: In this framework, the application is broken into smaller and independent units or modules. A test script is created for testing every module in isolation. Modular-based framework allows independent scaling and easier maintenance of each module. However, creating the framework requires time and good programming knowledge, making it suitable for experienced programmers.
Hybrid framework: Hybrid framework, just as it sounds, is a combination of various kinds of test frameworks. It combines the strengths of the above-mentioned and many other frameworks for maximizing test effectiveness while mitigating their drawbacks. However, as powerful as it is, it demands a high level of expertise and proficiency in programming.

Challenges of automation testing

Shifting from manual to automated testing can require high initial investments in the form of money and time. Choosing the appropriate tools and softwares requires time and experimentation.
Not all kinds of tests can be automated. For example, exploratory testing which mimics the user actions is better performed manually.
Different automation tools require knowledge and expertise in different programming languages.
Automation tests can sometimes wrongly detect flaws in the codes when there’s really no issue.

Conclusion:

Automation testing is a key component in the modern software development lifecycle, offering numerous advantages that help teams build better software more efficiently. Automation testing is a powerful tool that helps improve the efficiency, accuracy, and coverage of software testing. By automating repetitive and time-consuming tasks, testers can focus on more complex and creative aspects of testing. This not only speeds up the development process but also ensures that the final product is of high quality and reliable.

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Overall Guide About Dora Metrics

Tue, 06 Aug 2024 03:56:52 +0000

What are DORA Metrics?

We have already discussed about DevOps culture and a few metrics and KPIs before. In today’s article, we will be focusing on 4 standard metrics provided by DORA and how to improve them.

What are DORA Metrics in DevOps & How to Improve Them?

The aim of DevOps is to improve the software development process through better communication and collaboration between the development and operations teams. DevOps culture has four basic principles as per the CAMS model:

Culture
Automation
Measurement
Sharing

The third principle, that is measurement, is what entails the metrics and Key Performance Indicators (KPIs) used to evaluate the DevOps performance of companies. These metrics and KPIs also help companies find loopholes in their development and deployment process. There are several metrics and KPIs, but four of them have been set as standard by DORA.

Let’s see what is DORA and it’s 4 standard metrics…

What are DORA Metrics?

DevOps Research and Assessment (DORA) is a research program launched by Gene Kim, Jez Humble, and Dr. Nicole Forsgren. It is a team at Google Cloud that “seeks to understand the capabilities that drive software delivery and operations performance.” (DORA)With years of research, DORA has identified 4 key metrics that help to measure the DevOps performance of businesses. These key metrics can be categorized based on what they measure.

The first category measures the throughput (or velocity) which refers to how fast changes are being made. It includes:

Deployment frequency
Lead time for changes

The second one measures stability which refers to the quality of the changes and the ability of the team to fix any failures. It includes:

Change failure rate
Mean time to recovery

Based on these metrics, DORA classifies the DevOps performance of companies into four: Elite, High, Medium and Low. After understanding all the metrics, you can also attempt the DORA Quick Check to evaluate these metrics for your company.

So let’s discuss each one of the metrics and how to improve them one by one…

Deployment frequency

Deployment frequency tells how often new codes are deployed to production. It can be simply calculated by counting the number of codes deployed over a period of time. It can range from multiple times a day (for high-performing teams) to once every 6 months or more (for low-performing teams).

How to improve deployment frequency?

Smaller deployments: When you receive a proposal for change, try to break it down into smaller changes. This makes it look less intimidating and more doable. Moreover, it becomes easy to test with smaller changes and rollback in case of failures.
Automation: Automating manual or repetitive tasks like testing or deploying can help reduce time spent on individual deployments. This allows the workforce to focus on more creative and complex tasks instead, thus improving the frequency of updates.

Change lead time

Change lead time or the lead time for changes indicates the efficiency of the CI/CD pipeline based on how much time it takes for a change to get successfully deployed to production. In other words, it is “the difference in hours between the date and time of the author’s commit and the date and time of the deployment containing that commit.” (Pluralsight)

It can range from less than an hour (for high-performing teams) to more than 6 months (for low-performing teams).

How to reduce change lead time?

Smaller deployments and automation: As said before, smaller batches of codes and automated testing and deployment help speed up the deployment process and reduce change lead time.
Optimize communication: Smooth communication and collaboration help in faster decisions and less delays.

Change failure rate

Change failure percentage or change failure rate (CFR) is the percentage of deployments that lead to failures in production. A ‘change failure’ means any negative impact like crashes, low performance, or security vulnerabilities caused by a change or update. It can be calculated using the following formula:

CFR = (number of failed changes/total number of changes)*100

A CFR lower than 15% is considered good while anywhere between 16 to 30% is considered high. A lower CFR indicates a reliable CI/CD pipeline and effective testing.

How to reduce the change failure rate?

Automated testing: Test automation and code reviewing at every step help to ensure the correctness of codes and reduce human errors.

Mean time to restore service (MTTR)

Also known as failed deployment recovery time, MTTR is the time taken to recover from a partial service interruption or a total failure. It includes the time spent diagnosing and repairing the issue and redeploying the new code. It can be calculated using the following formula:

MTTR = (Total downtime/Number of incidents)*100

It ranges from less than an hour (for high-performing teams) to 6 months (for low-performing teams).

How to reduce MTTR?

Real-time monitoring and alerting systems: Continuous monitoring helps to instantly detect anomalies and resolve them quickly.
Automation: Use automation tools for detecting and resolving faults speedily.
Improve communication: Improve communication between teams to reduce delays and keep everyone updated.
Infrastructure as Code (IaC): IaC improves collaboration and allows easy rollback mechanisms in case the new code does not work.

The above metrics can be summarized using the table given below:

Image credits:https://www.google.com/url?q=https://cloud.google.com/blog/products/devops-sre/using-the-four-keys-to-measure-your-devops-performance&sa=D&source=docs&ust=1722875353836286&usg=AOvVaw1d9OPlfMgEQ1Uj_NfLPpWQ

Other Important Metrics & KPIs

Change volume: Change volume measures the amount of changed code against the static (old) code. It helps to determine how impactful the deployed codes are.
Defect escape rate: It measures how frequently defects go unnoticed and escape into production. It indicates the quality of the development and testing processes.
Time to detection: Time to detection, as the name suggests, is the time taken to detect failures. A lower time to detection means effective monitoring and alerting mechanisms.
Deployment success & failure rate: Deployment success rate is the percentage of deployments that do not require rollbacks or hotfixes while deployment failure rate includes those that result in errors.
Customer ticket volume: It is the total number of tickets or alerts raised by the customers.

Conclusion

Measuring your performance is the key to improving it. The four DORA metrics – deployment frequency, change lead time, change failure rate, and mean time to recover – help to quantitatively measure the current performance of your DevOps team. These metrics can be improved mostly through automation and smooth collaboration between teams. Continuous assessment and improvement of these metrics ultimately help to improve customer satisfaction – the goal of all businesses and companies.

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A Complete Guide To Automation And AI

Wed, 17 Jul 2024 05:33:00 +0000

AI Automation: Definition, Benefits, Applications, & More

Let’s see why automation and AI seem so much alike.

Both perform tasks faster and save time.
Both operate independently without human intervention.
Both can replace and are replacing human roles.

Because of these similarities, one might think that AI and automation are essentially the same. But they are not. If you want to understand the fundamental difference between AI and automation and how they can be combined to achieve better results, first understand the term “automation”…

What is Automation?

What’s the first thing that comes to your mind when you hear the word “automation”? Something like “automatic”? That’s exactly what it is.

Automation is the use of technology to perform tasks without human intervention. It involves creating systems or processes that can operate independently to complete repetitive or complex activities, often leading to increased efficiency and accuracy.

According to Spiceworks, “Automation is the use of machines or technology to perform tasks without much human intervention.”

In simple terms, when a process traditionally performed by humans is carried out by technology, it is referred to as automation. Businesses use automation technology to reduce their expenses on manual labor (such as salaries), minimize human errors, and improve efficiency. Robotic arms assembling a car or online stores reminding you of your unordered shopping cart via emails are a few examples of automation.

Another common term related to automation is RPA, or Robotic Process Automation.

Robotic Process Automation (RPA)

Image Credit: https://www.softwebsolutions.com/resources/implementing-intelligent-automation.html

RPA is a specific form of automation that utilizes software robots (known as ‘bots’) to perform repetitive or routine tasks. Businesses employ these bots to save costs and allow their workforce to focus on more creative and complex tasks.

Is AI and Automation the same thing?

AI and automation can seem similar because both appear to replace humans in some capacity. However, there are key differences between them in terms of capabilities. Automation follows predetermined rules to conduct low-level tasks and involves no decision-making, primarily replacing unskilled labor by performing tasks faster and with fewer errors. On the other hand, AI possesses intelligence, enabling it to understand data and make decisions based on it. It is closer to human capabilities and can, to some extent, replace skilled labor.

To understand this better, let’s take the example of the healthcare sector: Automation handles manual tasks such as data entry, billing, patient monitoring, and sample processing. In contrast, AI performs tasks that require evaluation and decision-making, such as detecting fractures via X-ray images, suggesting treatment plans for patients based on their medical history, and verifying diagnoses and prescriptions provided by health professionals.

Intelligent Automation (IA): Combining AI with Automation

Intelligent automation, or AI automation, is an integrated version of automation (specifically RPA) and AI, along with Business Process Management (BPM). These three components work together as follows:

AI acts as the brain of IA, making decisions based on its data.
RPA performs specific, rule-based tasks within a business process using bots.
BPM automates and optimizes the entire business process from end to end.

You can think of RPA and BPM as workers who perform physical labor, while AI acts as the engineer who instructs them.

Benefits of AI Automation

Productivity: AI automation bots help save time as they can work faster and more efficiently than humans. They do not require breaks and can work non-stop.

Cost Reduction: Traditionally, a business process would require multiple employees who needed to be paid a monthly salary. However, investing in AI automation can save those expenses in the long run.

Error Reduction: Humans are bound to make mistakes, but that’s not the case with technology. AI automation can help reduce errors and improve the quality of work.

Reduce Occupational Risks: AI automation can facilitate risky jobs and lower occupational hazards. For example, in the mining industry, miners face major health risks during excavation. Automated machinery can conduct risky excavations from a secure location while also monitoring air quality.

Customer Experience: In almost all industries, AI automation is used to improve customer experience. AI chatbots, available 24/7, can easily solve general queries. If the customer is not satisfied with the bot’s response, it can direct them to a human representative from the suitable department.

Applications of AI Automation Across Various Industries

Healthcare

Automation technology helps people book appointments, allowing them to schedule at their convenience with real-time availability updates and reminders to reduce cancellations.
It speeds up diagnostic processes with higher accuracy. For example, Arterys, a cloud-based medical imaging software, detects heart-related abnormalities through MRI and CT scans.
Patient monitoring systems like health watches detect abnormalities in heart rate, respiratory rate, oxygen levels, etc., and alert healthcare providers, aiding early disease detection.
Robotic or robot-assisted surgery is known for its precision, performed through tiny incisions, causing less pain, blood loss, and resulting in less conspicuous scars.

Finance

Banks and lenders use AI software to determine a person’s eligibility for a loan based on their financial history.
AI chatbots act as first-level customer service providers, helping customers check their bank balance, view transaction history, schedule payments, and solve general queries quickly. Available 24/7, these chatbots can also provide personalized banking advice and offers.
AI technology aids in fraud detection by learning from past fraudulent activities to detect future fraud and analyzing a person’s buying behavior to alert them about abnormal spending patterns.

Marketing and Advertising

Email automation tools (like Convert Kit and Mailchimp) schedule emails to be sent at specific times, sequence emails for crash courses, and trigger emails based on customer behavior, such as transactional emails after a purchase or welcome emails after subscribing to a blog.
AI automation assists in competitor analysis by collecting data about competitors’ ad strategies and generating insights.
It analyzes customer behavior and recommends products to individuals who are likely to buy them.

Conclusion

Now you know that AI and automation are not the same and differ in their scope of abilities. However, they can be combined in the form of Intelligent Automation to gain the benefits of both. As the use of AI increases across businesses, many job roles will come to an end, while many new jobs will be created. Ultimately, the future will be determined by how well businesses and the general public adapt to the rise in AI and automation technology.

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An Overview Of Chaos Engineering

Shiwani Sharma — Wed, 17 Apr 2024 08:06:27 +0000

What is Chaos Engineering?

Chaos Engineering is a technique used to assess the dependability of software systems by introducing controlled disorder into them. Organizations can leverage chaos engineering by establishing backup elements or procedures to ensure that the software functions smoothly during unexpected issues. The primary goal of chaos engineering is to identify vulnerabilities and weaknesses in an architectural system, enabling the entire team to evaluate performance in a production environment. Chaos engineering is derived from a concept called chaos theory and focuses on the immediate behavior of systems. It is straightforward to use, offers automation, and supports various configurations.

Definition of Chaos Engineering as per Wikipedia:

Chaos engineering is the discipline of experimenting on a system to build confidence in the system’s ability to withstand turbulent conditions in production.

According to Robert L. Devaney, to classify a dynamical system as chaotic, it must have these properties:

It must be sensitive to initial conditions.
It must be topologically transitive.
It must have dense periodic orbits.

Image Credit: https://www.dynatrace.com/news/blog/what-is-chaos-engineering/

Principles of Chaos Engineering:

Automation: It helps ensure that experiments are repeatable and well-managed. Chaos engineers consider automation essential for conducting experiments regularly. Automation principles play an important role in chaos engineering, as it permits the repeatable, predictable, safe, and controlled injection of chaos into the system. Many chaos engineering tools, such as Chaos Toolkit and Chaos Monkey, are crucial for this purpose.

Monitoring and Observability: This principle is crucial as it collects data during chaos experiments and aids in assessing the impact of failures. These principles help organizations learn and improve the performance and reliability of their system. First, let’s discuss the monitoring principle, the process of analyzing and collecting data from a system in real-time to gain insights into its behavior, health, and performance. Monitoring plays an important role in chaos engineering as it allows for quick detection of experiment execution, among other things. Now, let’s discuss observability, a way to debug and understand a system by checking out its behavior and internal state, even when there is no clear monitoring.

Safety: Chaos engineering should be carried out with safety considerations in mind. In case of failure or catastrophic losses, chaos engineers have the capability to roll back any changes. Safety is one of the important principles of chaos engineering, required to manage experiments in a safe, controlled manner, so that the organization can identify weaknesses in their system, upgrade the overall system, and manage any losses and interference effectively.

Building a Hypothesis: Chaos engineers start by constructing hypotheses, which guide the chaos experiment. These hypotheses revolve around how a system should perform under adverse and typical conditions. When building a hypothesis for a chaos engineering experiment, it is important to define the goals, income, and variables to test. When introducing chaos, it is important to write a clear statement that narrates what you expect.

Controlled Experiments: Controlled chaos experiments involve introducing specific forms of chaos to measure various factors, such as server failures and network latency. It is a scientific investigation where researchers have to handle many variables to ensure that they carefully control many dependent variables. The main aim of the principle of controlled experiments is to establish the relationship between many variables and some irregular variables. In this principle of controlled experiments, researchers utilize a control group and other experimental groups to identify differences and draw correlations about the effect of manipulated variables.

Post-experiment Analysis: After a successful chaos experiment, the entire team’s task is to examine the data collected and determine how the system will react when subjected to injected failures. This is an important and challenging phase where you can assess deep learning and results from organized chaos experiments.

Benefits of Chaos Engineering:

Organizations derive several benefits from chaos engineering, including:

Enhanced User Experience: Chaos engineering ensures that users have the best experience, even when encountering errors, ultimately improving the overall system’s performance. Chaos engineering helps identify weaknesses in your system, resulting in less downtime, fewer outages, and an enhanced user experience. It also helps identify potential problems during deployments and software updates, which, when addressed by the team, enhances the user experience.

Cost Savings: System failures and outages can be expensive in terms of operational expenses, lost revenue, and customer churn. Chaos engineering helps mitigate these costs by decreasing financial losses stemming from customer dissatisfaction and lost revenue. It helps organizations optimize resources and identify under- or over-equipped resources, reducing irrelevant expenses.

Stimulates Innovation: Chaos engineering identifies structural flaws and designs improvements in software systems, fostering innovation. It supports the team in finding weaknesses and losses in the organization and helps uncover areas that need improvement and innovation to make the system more resilient and robust.

Cultural Shift: Chaos engineering promotes continuous learning, regular development, and experimentation between operations and development teams. The cultural shift emphasizes self-service, automation, and the reduction of manual mistakes, enabling faster and more valid testing. Chaos engineering serves as a vehicle for the team to share experiences, best practices, and knowledge, promoting a culture of knowledge sharing and continuous learning.

Improved Resilience: Chaos engineering identifies system weaknesses and enables teams to strengthen the system against unexpected issues.

Efficient Problem Detection: Chaos engineering helps identify and address problems quickly, reducing downtime. This helps organizations find and address problems that arise early, improve system resilience, and build a more robust and reliable system.

Conclusion: Chaos Engineering is a technique used to assess the dependability of software systems by introducing controlled disorder into them. The primary goal of chaos engineering is to identify vulnerabilities and weaknesses in an architectural system, enabling the entire team to evaluate performance in a production environment. In this blog article, you will be able to learn in-depth about the principles and benefits that occur through chaos engineering.

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Edge Computing

Shiwani Sharma — Mon, 15 Apr 2024 04:38:55 +0000

What is Edge Computing?

Before introducing the term and the concept of Edge computing, we must first understand the meaning of “edge.” The term “edge” is both ambiguous and all-encompassing. This word “edge” generates a buzz similar to that of the cloud and IoT. Edge computing helps allocate a computing framework that facilitates enterprise applications that are situated close to data sources, such as local edge servers and IoT devices. These data sources offer various benefits to organizations, including improved response times, ample bandwidth availability, and faster insights.

In other words, it entails computing that occurs either at or near the source of data. It means that the cloud is either closer to you or coming to you, rather than being distant and inaccessible.

Edge devices consist of three layers: the edge layer, the fog layer, and the cloud layer. Initially, edge devices communicate with the edge gateway in the edge layer. Subsequently, the signal is relayed from the edge gateway to the fog nodes in the fog layer. Finally, this data is transferred to the cloud or data center in the cloud layer. Mobile edge or edge computing takes place on advanced networks like 5G, enabling extensive data analysis and faster, enhanced customer experiences, quicker response times, and deeper insights.

The Benefits of Edge Computing

There are five significant benefits of edge computing: security, speed, cost savings, scalability, and bandwidth efficiency. Let’s discuss these advantages in more detail.

Security

Having less data traversing the entire network enhances security. When all data is stored in one location, the main focus is on security, safeguarding the local data of smart product manufacturers. With more distributed data on the network, the risk of a breach increases, even if an appliance is compromised. This also assists organizations in addressing local compliance and data autonomy issues.

Speed

Edge computing reduces the burden on an organization’s network traffic, which, in turn, enhances the performance of its services and applications. Speed is crucial for essential business operations, especially for companies offering data-driven services. Slow speeds can dissatisfy customers and negatively impact a company’s reputation.

Cost Savings

Not all data within an organization holds the same value, and categorizing data for management purposes can result in cost savings. Edge computing allows data to be categorized, reducing the need for costly bandwidth and eliminating redundant storage costs.

Bandwidth Efficiency

Many organizations use significant bandwidth for less important data. Edge computing helps optimize bandwidth usage by prioritizing critical data transmission.

Versatility

Edge computing’s scalability is vital for versatility. It empowers IoT devices to collect vast amounts of data continuously, ensuring data is available for further analysis, eliminating the need for constant interaction with cloud servers.

Offline Operation

This benefit is one of the best benefit of edge computing, it allows the application to manage even when there is no internet connection or central data center. User can utilize it in any places either having internet services or not. In the other words we can say it provide the continuity of services in remote or isolated location.

Uses Cases Of Edge Computing

Some important uses cases of Edge computing are as follow:

Industrial Automation: In the industrial and in manufacturing line, edge computing is utilized for the actual time monitoring and processes and control of machines. It allow predictive maintenance, optimization of manufacturing operations and quality control.

Healthcare: Edge computing is utilized in healthcare to monitoring the patient for remote basis, telemedicine’s and using health devices and it also provide the correct data analysis and some quick alert to the tough condition arise from medical.

Agriculture: Edge computing also plays an important role in agriculture to monitor crop health, soil conditions and whether in actual time. It also help in allotment of resources as well as doing farming practices. It helps in different type of agriculture such as in Precision Agriculture and in this type soil and crop health monitoring etc. are included and it is helpful in every types of agriculture.

Though there are many use cases for several industries, we have mentioned few ones to you and these are industrial automation, healthcare, agriculture

Image Credit:https://innovationatwork.ieee.org/real-life-edge-computing-use-cases/

In the above image you have seen the real-life use cases for Edge Computing. There are many loT devices that is manage by cloud and data center.

Edge Computing vs. Cloud Computing

Image Credit:https://www.orientsoftware.com/blog/edge-computing-vs-cloud-computing/

As you have seen the differences in the above images, now you will see the differences as mentioned below :

S.No.	Edge Computing	Cloud Computing
1	Its platform is its own devices.	It operates in a cloud platform, such as Amazon EC2, Google Cloud, etc.
2	Normal reaction time is in milliseconds.	Normal reaction time is also quick, but not as fast as edge computing, typically in minutes or days.
3	Concerned with networking operations and deploying data handling near the network’s edge.	Provides computing resources on demand, including storage, databases, servers, etc.
4	Requires less bandwidth compared to the cloud.	Cloud computing demands more bandwidth.
5	Access occurs at the end of the appliance, such as the smart sensor.	Access occurs on the main server and headquarters.
6	Emphasizes robust security.	Does not emphasize robust security and safety.

Conclusion:

Edge computing allows the deployment of computing resources closer to data sources, such as local edge servers and IoT devices, resulting in advantages like improved response times, ample bandwidth availability, and faster insights. Throughout the article, we’ve explored the fundamental concept of edge computing, its benefits, and its comparison with cloud computing. We’ve also examined its diverse applications across various industries, including healthcare and autonomous vehicles.

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Ultimate Key Reasons to Become a DevOps Engineer in 2024

Shiwani Sharma — Fri, 15 Sep 2023 09:02:29 +0000

Firstly, to understand why pursuing a career as a DevOps Engineer is crucial, it’s essential to have a solid grasp of what a DevOps Engineer is.

Is a Devops profile even relevant and in demand in 2023?

Sharing some data, as mentioned by Quadralogics, about the demand for Devops professionals in 2023:

There are around 1 Lakh open demands where DevOps is listed as a primary skill Out of which 10 persentage of jobs are listed in India There is a steep increase in DevOps demand in Mid- Senior level primarily attributed to the need for more DevOps Coaches or Consultants.

The demand for Devops roles and hence Devops engineer is on rise since last few years. The image below shows the median market salary for Devops eningeer in the market.

Who is a DevOps Engineer?

A DevOps Engineer is a professional who has mastered DevOps, with the primary goal of enhancing collaboration and communication within the DevOps team, a fusion of Development and Operations. The primary objective of a DevOps engineer is to streamline the software development process, ensuring that both the development and operations teams meet all requirements. DevOps engineers also help bridge the gap between the tasks needed to make an application instantly changeable and those required to make it reliable. In the latest iteration of software development, DevOps engineers play a pivotal role due to their rapid software delivery system, with a focus on security, quality, and collaboration among all team members.

In simple terms, a DevOps engineer possesses knowledge of the software development lifecycle and is well-versed in various automation tools used to facilitate the growth of Continuous Integration/Continuous Deployment (CI/CD) pipelines.

Characteristics and Responsibilities of DevOps Engineering

Collaboration: One of the primary responsibilities of DevOps engineers is to foster a culture of collaboration and distribute responsibilities between the development and operations teams. They also promote cooperation and communication among individuals and groups.

Automation: DevOps engineers are tasked with maintaining, implementing, and designing automation pipelines for deployment, code integration, and testing, reducing manual errors and ensuring a smooth process.

Additional responsibilities include:

Regulating IT infrastructure in line with business code requirements, necessitating continuous monitoring. A strong grasp of development tools is crucial for writing new code.
Understanding customer needs and project Key Performance Indicators (KPIs), planning project activities, team structures, and collaboration.
Troubleshooting code bugs and resolving technical issues.
Creating and implementing automated processes as needed within the organization.
Coordinating and communicating with customers and teams.

Key Reasons to Become a DevOps Engineer in 2023

When considering a career as a DevOps engineer, you might wonder about the reasons behind it. Here are some compelling reasons:

Job Security: DevOps engineers enjoy job security because their skills remain relevant and valuable. Job security is very important for any type of job and if any job profile provide you the security then this is the best way to pursue as your career.

High Demand: DevOps engineering has gained immense popularity, with most organizations seeking top-notch DevOps engineers to achieve optimal results in software development and operational efficiency.

Good Salary: These specialized skills are highly valued in the software development lifecycle, resulting in competitive salaries. Every person has the dream to work with having good salary and this dream is fulfilled by the job od DevOps engineer.

Continuous Learning: This field offers numerous opportunities for skill development and continuous learning. Staying updated with new tools and technologies is essential. If your job profile has the power to learn you more and on a daily basis you are learning some new tools and things then this is the best reason you have to make your career as DevOps engineer.

Flexibility: DevOps skills are highly transferable across various industries, allowing you to work in diverse sectors such as e-commerce, healthcare, finance, and more.

Infrastructure as Code (IaC): DevOps engineer have to work with the tool infrastructure as code such as Ansible,Terraform etc to handle infrastructure resources in a continuous and repeated manner.

Work From Home Opportunities: Following the COVID-19 pandemic, remote work opportunities have become crucial. DevOps engineers have the flexibility to work from anywhere. This is the best way to handle your personal and professional life together and if your job profile provides you this facilities then what you need other thing. The best reason to work as devops engineer.

Conclusion:

In 2023, DevOps engineer stands out as an excellent career choice. The reasons outlined above make it a compelling option for those seeking a bright career path as DevOps engineering.

If you have not read our previous posts on Devops Career & Skills and how to transform into a Devops professionals, sharing them again for your reference. Hope this was a useful post for you, do not forget to like, comment & share.

Roles and Responsibilities of a DevOps Professional
Key Soft Skills needed to become a Devops Pro in 2023
A Complete Guide To Transition From a SysAdmin to DevOps Role

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Key DevOps Principles to focus in 2021

Mon, 12 Oct 2020 13:53:36 +0000

DevOps Key Principles

Changes call for innovation, and innovation leads to progress. This is the tried and tested method and the key to building a successful organization. Time and again the software industry is hit by a wave of challenging demands to match the market standards. And to keep up with the changing trends, organizations have started adopting new processes. One such methodology is DevOps.

What is DevOps?

The principal idea behind DevOps is to bring the two loosely connected ends i.e. the Development and Operations team together. The methodology focuses on adopting novel practices, philosophies, and tools over the conventional process of software development. This results in faster delivery, better end product, and great user satisfaction.

The benefits of DevOps are innumerable in comparison to ongoing practices in the market. However, the main challenge is to make the transition from the existing process to adopting a DevOps approach.

Here is a list of key points that the organization needs to keep in mind in order to make a smooth and effective shift.

1. Customer First

Customer always comes first is the motto of DevOps and the organizations need to take the necessary measures to ensure the same. The organizations need to innovate and transform continuously if they go wrong with a plan of action.The team should not only rely on data and market parameters but also need to keep a keen eye on user requirements. The team must strive to achieve the right balance between the changing market trends and customer demands.

2. Inculcate the `We` factor

Prior to onset of DevOps, the Dev team and QA team used to work in silos, and the fully working code would then be handed over to the Operations. The perfectly running code of developers on the dev server would mostly go haywire as soon as pushed on the real-time server. The blame game would then start as the respective teams try to be defensive and prove that there is no blunder from their side. This tug of war would ultimately result in the poor quality product and in turn, adversely affects the organizations reputation.The teams in the traditional sense are considered two ends which hardly meet. But the practice of DevOps works in collaboration right from the start and focuses on bringing the team to work as a unit.

3. Cultivate the concept of `Cradle to Grave`

The second most important factor of DevOps methodology is to take ownership of the product as a team. All the teams are involved from the pilot to deployment and post that providing the services and maintenance. The involvement of the teams from the beginning brings the sense of working in the same unit.

4. Effective Decision making

The team members should not only have sound technical expertise but also try to make the decisions in their purview. The team management takes the business-related decisions, but a good DevOps practice highly encourages the team to take localized decisions. This method avoids the congestion in the whole process.

5. Learn and Evolve

One of the most important pillars of DevOps is continuous improvement. The software market is quite dynamic and to keep up with the pace, the organizations need to learn and evolve. Just by changing the culture or adopting the process won’t help, but embracing the new market trends, adapting the technology and customer needs is the need of the hour.

6. End-to-end automation

Conventionally, the automation process would come in the picture only during the testing phase. The QA team would automate the test cases to check the code functionality and the Ops team would manually configure the server requirements. That was pretty much the case, but DevOps employs automation right from the time the code gets checked in. The code builds, running the test cases, and finally releasing the code for release happens automatically. That is how the entire lifecycle of DevOps is automated.

7. Right Set of Tools

One of the fundamental pillars of DevOps is automation. To enable automation and get the maximum benefit out of it one requires the right toolchain. Let us take look at the most popular DevOps tools:

Jenkins: It is an open-source CI/CD automation server. Helps to build, test, and deploy in a single tool.
Git: The version control system repository makes code readily available across the team.
Selenium: Enables QAs to test the web application and write test scripts.
Docker: Open source container technology. Packages the application in such a way that it can be used on any platform.
Nagios: It is an open-source monitoring tool.
Kubernetes: currently the most in-demand Devops tool in the market. Kubernetes is open-source cluster-orchestrator to manage the real-time container workloads.

8.Continuous Feedback

DevOps is all about taking a customer-centric approach and to enable the same, feedback from end-users is essential. The only way to solve this issue is to concentrate on user requirements and bridge the gap between end-users and DevOps.

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