akhyansh – Talent500 Blog

Why Progressive Web Applications Are the Next Big Thing

Posted on July 2, 2021July 2, 2021 by akhyansh

In today’s online environment, users are spoilt for choice when it comes to applications and websites. Companies strive to attract customers and maintain their business, and many successfully do so on one platform or another. However, the problem arises when the user has to navigate between these solutions and the experience doesn’t carry over. For instance, the app experience for an online service may be stellar as compared to the website, and the gap between the two can be damaging. This is where progressive web apps (PWA) come into play.

Simply put, progressive web apps are websites that have been designed to resemble an application. There are many benefits to such a technology, with data backing the claim that users prefer mobiles apps to websites. This makes sense, considering that applications are generally easier to navigate, offer better personalisation, and offer the ability to work offline. These are just some of the features that a PWA leverages to provide users with an enhanced experience.

For a deeper dive into why progressive web apps are the future of web design and development, take a look at these pointers.

Heightened security

For any type of web provision, security is among the top priorities. Not only does it protect the user base from malicious attacks but also inhibits the lasting damage that these cyber-attacks could lead to. What’s more, search engines like Google encourage web services to have the right type of security provisions in place. Failing to do so has its drawbacks and users are often notified of any shortcomings in this regard.

Progressive web apps don’t have to worry about such security concerns, so long as the website is using HTTPs. This helps them enjoy the same security benefits and ensures that interactions happen in a controlled environment. The benefit of this heightened security is that it allows for more diversified application. Users can now comfortably enter sensitive personal or financial information into progressive web apps without worrying about it being stolen or exposed.

Offline functionality

Among the key reasons why progressive web applications are regarded as the future of web applications is because it bridges the gap between online and offline access. Thanks to the use of a proxy server known as a ‘ServiceWorker’, developers are able to offer users an offline experience. This is also considered semi-offline in some cases, but the key takeaway is that it works offline.

Unlike a traditional web application or website, a PWA doesn’t rely completely on having an active network to work. It leverages content caching to facilitate offline function. Over to the side of development, this is all possible due to the Java scripts mentioned above. These can respond to connectivity changes and network requests, while operating independently from the application.

Better user experience and conversions

Providing a simple, easy, and hassle-free user experience is the end goal for any application. Progressive web applications particularly excel in this regard because of ServiceWorkers. These unique event-driven scripts allow granular caching and speed up the application greatly. In fact, with proper optimisation, a developer can ensure that the PWA loads almost instantly and is easy to navigate, even without a network connection.

These benefits further translate into conversions for companies that leverage the power of a PWA. A great example is AliExpress, a company which was able to increase its conversions by 104% for new users. Besides this, there was a 74% increase in the time spent per session by users on their PWA. These numbers showcase the power of a PWA, but it isn’t the only success story. Another notable conversion metric PWAs were able to deliver on was user engagement. Saudi retailer eXtra Electronics was able to generate an additional 100% sales through the PWA via web push notifications.

Cost-effective development cycles

There are numerous testimonies to back the claim that PWAs are cost-effective. Nicolas Gallagher, the Engineering Lead for Twitter Lite, claims that it is the least expensive way to use Twitter. Further, developing a PWA benefits the parent company because it is much cheaper to build as compared to a native application.

To offer perspective, on average, a PWA can cost between $3,000 and $9,000, whereas a native application will cost around $25,000 to develop. Aside from the cost, a progressive web application takes less time to develop. This is particularly valuable to startups as it promises a much quicker ROI.

Universality

Progressive web apps are solutions that combine the features of a website and an app. Among the key benefits is that these apps can run on any device or operating system as it utilises the browser. This helps provide a seamless user experience across devices as developers are required to optimise for the browser. Unlike a traditional app, this reduces the need for heavy-duty downloads and saves on platform real estate.

A good example is Pinterest’s PWA, as it’s only around 150KB in size, as compared to the native app, which is around 56MB for iOS and over 9MB for Android. Besides saving on the file size, PWAs are also universal because there’s no need for any type of installation. Think of it as a plug-and-play solution that businesses can fall back on across all devices with a browser.

Personalised interactions

PWA developers have the freedom to personalise user experiences in a more flexible manner than is possible with a traditional app. This improves customer loyalty and engagement by a great deal and one of the best examples of this technology are push notifications.

To web developers across the world, progressive web apps check all the boxes. They are fast, cross-platform compatible, reliable, and tightly integrated. What’s more, companies are slowly catching on and making PWA development a priority for their online services. This means that there’s a growing demand for developers and the scope for innovation is massive. With the required skill set, you could very easily find yourself developing the next big thing!

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A guide to executing Linear Regression in Python

Posted on December 4, 2020December 4, 2020 by akhyansh

Regression and classification are two commonly used supervised learning algorithms and while classification is used for discrete values (e.g. email spam detection), regression is employed when the values are continuous (e.g. weather forecasting). Breaking down the term linear regression is important: ‘regression’ means that there is a causal or ‘cause and effect’ relationship between two or more variables and ‘linear’ implies that this correlation is linear, that is, if you plot it in a 2D space, you’d get a straight line.

Linear regression has a wide range of real-life applications from assessing the risks that insurance providers take to vendors adjusting their prices based on income statistics. Modelling such data in Python is fairly easy too. In this guide you’ll learn some basic linear regression theory and how to execute linear regression in Python.

What is simple linear regression?

In linear regression, you map dependent variables with independent variables. The dependent features are also known as outputs or responses and the independent features are called inputs or predictors. Here is some theory based on insights from Mirko Stojiljković’s article on Real Python.

A linear regression model can be defined by the equation:

? = ?₀ + ?₁?₁ + ⋯ + ?_n?_n + ?

? is the dependent feature

x = (?₁, … ?_n) are the independent features

?₀…?_n are the regression coefficients or model parameters

? is the error of estimation, which can be averaged out to be zero

In case of simple linear regression, there is only one independent feature.

So, the regression model narrows down to:

? = ?₀ + ?₁?₁

In carrying out linear regression you are arriving at estimators for ?₀…?_n_,the regression coefficients.

The estimated regression function then is:

?(x) = ?₀ + ?₁?₁ + ⋯ + ?_n?_n

At any given point, the difference between the actual and predicted response would be the residual (R).

For ? = 1, …, m,

R_i = ?_i – ?(?_ᵢ)

To arrive at the best set of coefficients, the goal is to minimize the SSR, sum of squared residuals.

SSR = Σᵢ(?ᵢ – ?(?ᵢ))²

How to execute simple linear regression in Python?

To implement simple linear regression in Python you can use the machine learning package Scikit-Learn. You can try out the code on Jupyter.

Import libraries

Pick the dataset

Consider this simple dataset provided by T McKetterick on Kaggle which provides, from a small sample of American women aged 30-39, average mass as a function of height.

Now, importing the CSV using pandas:

To get a glimpse of the dataset, execute:

The output is:

(15, 2)

And to view some of the data run:

The output is:

To “see” if the data would be good for linear regression, it makes sense to plot it.

The result is:

As you can see, this is very linear in form.

Provide the data

Here, you are to divide the data into independent and dependent variables:

Create a linear regression model:
Now, on to creating a linear regression model with Scikit-Learn and fitting it with the dataset.

To train the model use:

The output is:

LinearRegression()

To predict features run:

Evaluate the model

Root mean square error is a metric we want to minimise. So, let’s see how our model has performed:

View the results:

You get:
Root mean squared error: 0.49937056025884025
R2 score: 0.9891969224457968

Both these are very good values.

Also, take a look at the coefficients.

You get:

Slope: [61.27218654]
Intercept: -39.06195591884392

Having ?₀ = -39 makes sense, as a person shouldn’t have positive weight for zero height (x).

Take a look at the line predicted by the model:

Now that you have a working model, you can use it to predict dependent variables for a new dataset, say, for a larger group of women of a similar age group and demographic.

So, you could get weight values for random heights, such as, 1.3, 1.5, 1.2, 2.4, 1:

Have a look at x and y_pred:

You get:

[[1.3]
[1.5]
[1.2]
[1.4]
[2.4]
[1. ]]

And similarly,

To get:

[ 40.59188659 52.84632389 34.46466793 46.71910524 107.99129178
22.21023062]

How to execute multiple linear regression in Python?

As an example of multiple linear regression consider data on cars from CarDekho, available at Kaggle.

Import Libraries

Provide the dataset

To view the first 5 rows of data:

You get:

Prepare the data
Now, for the purpose of evaluation, consider that the present price depends on only 3 of the above factors: years, selling price, and kilometers driven.

This time around, try dividing the dataset itself, into training and testing sets. You could keep a ratio of 80:20.

You can view the dimensions of the matrices:

You get:
(240, 3)
(61, 3)
(240,)
(61,)

Train and test

Once again, use Scikit-Learn library to train and test:

The output is:

LinearRegression()

In multiple linear regression, the model will try to arrive at optimal values for all coefficients.

You can view these now:

You get:

That is, if the present price increases by ‘1 unit’, the selling price should increase by 1.87 units. (e^-1 ~ 3.68). Or if the kilometers driven increase by 1 unit, the selling price drops by 3.14 units.

Prediction and evaluation
You can now make predictions on the dataset:

To view actual vs precited values,

This is what you get:

You can now evaluate RMSE and R2 score for training and test data:

You get,

RMSE = 2.040579021493682
R2 score = 0.8384238519780597

And for the test data:

You get:

RMSE = 1.6867312004140655
R2 score = 0.887446046362064

So, what do you think?

Are these metrics a good starting point?

Or would it help to carry out some exploratory analysis first, and have some other variables play a factor in deciding the selling price?

In the field of data science, being inquisitive helps arrive at the right answers.

It also helps to work on projects with some of the best minds in the industry, and for that, you can simply sign up with Talent500. Our dynamic skill assessment algorithms align your skills to premium job openings at Fortune 500 companies. This way you’ll put what you learn in data science to practice and get hands-on experience with top real-world problems!

*You can gain complimentary insight from the following blogs, which also served as resource material for this post:

https://realpython.com/linear-regression-in-python/#polynomial-regression

https://stackabuse.com/linear-regression-in-python-with-scikit-learn/

https://towardsdatascience.com/linear-regression-on-boston-housing-dataset-f409b7e4a155

A Visual Guide to Version Control – SCM & VCS

Posted on December 4, 2020December 4, 2020 by akhyansh

What is Version Control?

Version control is a system that records changes to a file or a set of files over time so that specific versions can later be retrieved. In this article, we will use source software code as version-controlled files for the context, but you can actually do this with almost any type of file on a device.

A Version Control System (VCS) is a very smart thing to do if you are a graphic or web designer and want to manage every version of an image or layout (which you would most likely want to). This allows you to revert selected files back to an earlier state, revert the whole project back to a previous state, compare changes over time, see who last changed something that might trigger a problem, who implemented a problem, and when and more. Utilising VCS also usually ensures that you can recover files quickly if you mess stuff up. Additionally, with very little overhead, you get all this.

Git (distributed), Mercurial (distributed), and Subversion (centralised) are some common version control systems.

Software that offers versioning features (Git, Subversion, TFS Version Control) all fall into this group. Version Control Systems are just that.

Caution, SCM can refer to different meanings about Versioning:

Software Configuration Management is a wider approach that integrates all the software building, packaging, and deployment processes required, including Version Control Systems. It does not apply per se to apps.
Source Control Management is same as Version Control and Source Control and VCS.

Moreover, people may use SCM to refer to other naming:

Source Code Management, as in Source Code Control System
Software Code Management, but this is a deformation of Software Configuration Management
Source Configuration Management, same meaning as Software Configuration Management but maybe more focused on source code than on the whole software (settings, command line arguments, host parameters, etc)

Therefore it is misleading to only use the acronym SCM: some individuals may understand the same concept as VCS, some others may understand the whole process where VSC is just an element.

There are two general version control varieties: centralised and distributed. Distributed version control is more modern, runs smoother, has more functionality, is less prone to bugs, and is somewhat more difficult to comprehend. You will need to determine if it is worth the extra difficulty for you.

The number of repositories is the key difference between centralised and distributed version control. There is only one repository in centralised version control and there are several repositories in distributed version control. Here are pictures of the typical arrangements:

Each user gets his or her own working copy inside centralised version control, but there is only one central repository. As soon as you commit, it is possible for your co-workers to update and to see your changes. For others to see your changes, 2 things must happen:

You commit
They update

Each user obtains his or her own repository and working copy in distributed version control. After you commit, others have no access to your changes until you push your changes to the central repository. When you update, you do not get others’ changes unless you have first pulled those changes into your repository. For others to see your changes, 4 things must happen:

You commit
You push
They pull
They update

Note that commands to commit and update only transfer changes between the working copy and the local repository, without affecting any other repository. The push and pull commands, on the other hand, transfer modifications between the local repository and the central repository without affecting your working copy.

Conflicts

The version control system lets multiple users edit their own copies of a project simultaneously. Usually, the version control system is able to merge simultaneous changes by two different users: for each line, the final version is the original version if neither user edited it, or is the edited version if one of the users edited it.

A conflict occurs when two different users make different changes to the same line of a file simultaneously. In this case, the version control system cannot automatically decide which of the two edits to use (or a combination of them, or neither!). To resolve the conflict, manual intervention is needed.

“Simultaneous” changes do not necessarily happen at the exact same moment of time. Change 1 and Change 2 are considered simultaneous if:

User A makes Change 1 before User A does an update that brings Change 2 into User A’s working copy, and
User B makes Change 2 before User B does an update that brings Change 1 into User B’s working copy.

There is an explicit process called merge, in a distributed version control system that incorporates concurrent editing by two separate users. Sometimes merge completes automatically, but if there is a conflict, merge requests help from the user by running a merge tool. In centralised version control, any time you update, merging occurs implicitly.

It is better to avoid a conflict than to resolve it later. Conflicts, notwithstanding the best efforts, are bound to occur.

Git

Git is a distributed version control system created by Linus Torvalds in 2005 to monitor changes in any set of files, originally designed to manage work between source code programmers during the development of software. Speed, data integrity and support for distributed and non-linear workflows are its objectives.

Git vs GitHub

Git is a version control system that allows you to maintain your source code history and keep track of it while GitHub is a cloud-based storage service that allows you to manage Git repositories. If you have open-source projects using Git, GitHub is intended to help you manage them better.

Many open source enthusiasts are probably weary of GitHub acquisition by Microsoft, knowing very well that Microsoft is a for-profit corporation, and who knows, terms and conditions are bound to change (as is often the case for such deals) concerning the world’s leading software development platform.

If you are one of those already thinking of alternatives to Github for hosting your open source project(s), then check out the list below.

Version control best practices

Use a descriptive commit message
Make each commit a logical unit
Avoid indiscriminate commits
Incorporate others’ changes frequently
Share your changes frequently
Coordinate with your co-workers
Remember that the tools are line-based
Don’t commit generated files
Understand your merge tool

Tips

Cache your password so that you don’t have to type it every time
Setup email notification whenever someone pushes or commits to the central server

References

https://git-scm.com

https://en.wikipedia.org/wiki/Git

https://en.wikipedia.org/wiki/Version_control

https://stackoverflow.com

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Python vs. Node.js: Choosing The Right Technology for Backend Development

Posted on October 30, 2020 by akhyansh

Choosing the right programming language when taking up a new project is perhaps one of the toughest decisions for the programmers. The reason is simple. Every project has some unique specifications and problems, and there exists no such software in the programming world that can be used for everything.

Different programming languages have different strengths and weaknesses, and, therefore, specific use cases. Thus, it becomes imperative to identify the right programming language for any project at the outset to minimise any hiccups in the later stages. In this write-up, we will compare two popular programming languages, Python and Node.js, to identify which works better in what situation.

Understanding Python and Node.js

Python is one of the most popular languages for machine learning. It is a general-purpose programming language that is object-oriented, dynamically typed, and supports multiple programming use cases. You can use Python for developing robust applications for web, mobile, and desktop with seamless options for back-end development that makes it extremely popular with programmers across the world.

Node.js is also mainly used as a back-end framework. It is a JavaScript runtime environment that has been created on Google Chrome’s V8 JavaScript engine. Node.js can be used for both front-end and back-end development and is useful for building efficient and scalable JavaScript-driven web applications. Node.js is mainly used for web-based applications, but the use cases for Python are much more versatile.

The convenient nature of Python certainly gives it an edge over Node.js, but picking a clear winner isn’t that simple. Below, we have shared a comparison between Python and Node.js on some pre-defined parameters to help you make the right choice for your next development project.

	Python	Node.js
Use cases	Used for developing data science apps, voice and face recognition software and 3D modelling software. Generally, it is one of the best options for developing larger projects.	Preferred for asynchronous programming and developing real-time web applications like chatbots and streaming applications. It is preferred for smaller projects with lesser memory.
Universality	Web, desktop, mobile back-end and front-end.	Web, desktop and mobile back-end only.
Speed	Slow speed due to slower due to its synchronous nature.	Much of the Node.js core API is built around an idiomatic asynchronous event–driven architecture which makes it faster.
Syntax	Simple and compact syntax that lets you achieve more with fewer lines of code.	Mostly similar to the browser’s JavaScript syntax. Prior experience with JavaScript will make it easier to work with Node.js.
Scalability	Python lacks proper scalability support for several reasons. It is a slower programming language that doesn’t support multithreading, which prevents it from running multiple tasks, simultaneously. However, developers often use Python implementations like CPython and load balancing mechanisms to overcome this issue.	Node.js is built into the runtime environment and comes with a cluster module to handle multi-tasking. Node.js also allows for both vertical and horizontal scaling of web applications, clearly scoring over Python in this regard.
Extensibility	Python is highly extensible through frameworks like Django and Flask for both web-only and full-stack development. There’s also Jython, the implementation of Python in Java, which simplifies scripting and allows rapid application development.	Node.js comes with a pool of frameworks like DerbyJS ad Koa.js to extend its features, making it a more versatile framework for development.
Libraries	Python Packages and libraries are handled by pip, which is the default package installer for Python. Talking about the number of packages, Python offers more than 220 thousand packages across numerous categories like image processing, data science, etc.	The packages in Node.js are handled by node.js npm, which is also called the Node Package Manager. In numbers, it contains over 1.3 million packages, beating pip hollow in terms of extensibility with the right package.
Community	Python is an old and popular language, which also means it has a large and helpful community of users to find support and resources online. Developers are free to contribute to Python and its packages through the communities.	The Node.js community is also beaming with talented developers, eager to help and contribute to the development of the Node.js framework.
Popular Framework	Django, Flask, Falcon & Bottle	Express.js., Hapi.js, Sail.js & Koa.js

Conclusion

Both Python and Node.js are potent options for programmers to develop web and other applications. However, to choose between the two, you need to consider two factors – the purpose of the project and your skillset as a developer. While both the languages have significant advantages over the other, it is the purpose of the project and your comfort level, as well as that of your team, with either of the technologies, that would direct your choice.

Overall, both the languages are equally preferred, and software engineers well-versed with Python and Node.js are in high demand across the globe. If you are planning a job change or looking at climbing the next rung in your career, you may sign up on Talent500 to empower your job search for key roles in Fortune500 companies.

Essential Interview Questions For Backend Developers With 1-3 Years Of Experience

Posted on October 23, 2020October 23, 2020 by akhyansh

With currently over 1.8 billion websites registered and counting, the ticker moves faster than a clockʼs seconds hand. Of these, it is estimated that less than 25% are currently active, leading us to a still eye-catching figure of about 400 million active websites. To keep these websites up and running, while looking pretty, is the job of the frontend, backend, or full-stack developer. The programming and technology that refers to the server-side of an application, is called backend.

The backend of any application, also known its brain, involves communication between servers, databases, and applications (or browsers). It is the backend developerʼs responsibility to ensure that these three components communicate seamlessly with each other to facilitate the user-facing side of the websites.

A backend developer needs to be proficient with several tools and skillsets to be great at their job, as seen in highly skilled developers. When it comes to highly functional mobile application or website application with heavy payload such as Netflix or Amazon, concepts of new generations are brought in to ease the workflow and enhance the availability of content provided by any service. Such concepts include publisher-subscriber model, queuing, messaging, sockets, etc. As development team size grows, involved developers goals include speed, data integrity, and support for distributed, non-linear workflows.

A basic understanding is required for how application will communicate to its backend. REST, GraphQL, gRPC, sockets, etc are some widely used communication practices. Security of data communication between systems is essential part of backend. Without it, data breaches are common making any application vulnerable.

Below are some typical skills a backend developer must possess:

Server-Side Languages & Frameworks: Ruby, Perl, Python, PHP, Node.JS, Angular, .Net, ASP.Net
Database Skills: MySQL, Oracle, NoSQL, MongoDB or PostgreSQL
Others: Apache, Nginx, Docker, Kubernetes, Git, OAuth, etc.

Given that there is such a large scope for discussion in an interview, candidates need to prepare for and be ready to answer certain questions with flair and confidence. Below is a list of 9 questions, which backend developers should be prepared to answer.

1. How would you manage Web Services API versioning?

Versioning is a critical part of API design, as it gives developers the ability to improve their API without stopping the clientʼs applications whenever new updates are rolled out. The three types of API versioning are:

URL Versioning or Route Versioning: This solution uses URI routing to point to a specific version of the API.
Versioning using a custom header: REST APIs are versioned by providing custom headers with the version number included as an attribute.
Query String Parameter: Considered to be the worst method, the version number is included as a query parameter.

2. How would you find the most expensive queries in an application?

Expensive queries are database queries that execute very slowly or utilize large amounts of CPU or memory resources. These queries are the most common cause of performance issues in an application.
SQL Activity Monitor is an easy and rich UI tool available in the SQL Server Management Studio. You can also use SQL Dynamic Management View (DMV) to view the most expensive queries.

3. What is CAP Theorem?

CAP stands for the attributes of Consistency, Availability, and Partition Tolerance. The CAP Theorem is a concept according to which, a distributed database system can have only two of the three mentioned attributes. It is useful to determine the choice of data manipulation tools used in Big Data, based on each unique use-case.
CAP Theorem is like the old joke about software projects: you can have it on TIME, in BUDGET, or CORRECT. Pick any two.

4. When would you apply asynchronous communication between two systems?

In asynchronous communications, the client sends a request to the server (typically requiring lengthy processing), while receiving a delivery acknowledgment immediately.
After the client receives the acknowledgment, it carries on with other tasks and will be notified eventually when the server finishes processing the request. The main benefit of asynchronous communications is improved performance.
Asynchronous communications can be applied in situations where the response is not required immediately, and the current process can continue without the response. Real-world examples can include email, Slack, and other messaging platforms.

5. In what situation would you choose RDBMS and where would you choose NoSQL?

An RDBMS is used when you need ACID (Atomicity, Consistency, Isolation, Durability) compliance to reduce anomalies and protect data integrity and when the data is structured and unchanging.
On the other hand, NoSQL is recommended for high volume environments, cloud computing & storage, and when using unstructured data. Examples of NoSQL databases are MongoDB, Cassandra, HBase, and CouchDB.

6. What is an MVC framework?

A Model-View-Controller is a software design pattern that separates an application into three logical interconnected components – the model, the view, and the controller. It is used to organize the code into simple organized components. MVC helps in letting your code interact with another developerʼs code, based on their functions.

7. Which sorting algorithm to use and when?

Quick Sort: is one of the most efficient sorting algorithms. It is based on the splitting of an array or list into smaller ones and swapping values based on the comparison with the ‘pivot’ element selected. It is more effective for data that can fit in memory. Otherwise merge sort is preferred.
Bubble Sort: The simplest but most inefficient sorting algorithm, it repeatedly cycles through a list, compares adjacent values, and swaps them if they are in the wrong order. It is mostly used when array is small or if large data which is nearly sorted.
Selection Sort: It is a fast and simple comparison-based sorting algorithm. It sorts by finding the minimum element repeatedly in an array. It is mostly used when an array is small as its time complexity makes it inefficient for larger arrays.
Merge Sort: One of the most efficient algorithms, it uses the principle of divide and conquer. It iteratively breaks down lists into sub-lists consisting of single elements and then merges these elements as per the requirements. Widely used in case of linked list or where the known data is similar.

8. What are the qualities any good backend developer must possess?

This is a great question with which to impress the interviewer, as you can tell them about your competencies and understanding of the position. However, many candidates make the mistake of only talking about a couple of their strengths. The ideal answer should include at least some of the following points:

In-depth knowledge of server programming languages like Python, Ruby, Java, Perl
Great acquaintance with NoSQL and RDBMS
Good understanding of front-end technologies (easy to work with frontend developers)
Basic understanding of cloud deployments
Ability to develop business logic within any app
Ability to easily create functional APIs
Design of service architecture
Ability to optimize web applications

9. What do you find hardest about coding?

This question is designed for the interviewer to find out about your weaknesses or technical deficiencies. It is best to give an honest answer (without beating yourself up too much) about a specific area that might not be your strong point. What is more important is to let the interviewer know that you are improving yourself by learning, reading, or researching to upgrade your technical skills.

Backend development is an immense topic, and the above list of interview questions is only the tip of the iceberg. Use this list as a reference, after doing your research on the basic questions asked in an interview for backend developers.

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