The concept of data visualization plays a very essential role when it comes to assisting the users in question to comprehend the critical ideas quite easily. For instance, identifying the data patterns and trends quickly, and getting the maximum output from the presented data as well.

As a matter of fact, with the increase of data escalation in terms of generating data, finding a way to extract, processing and visualize those data to enhance data interpretation is growing rapidly. In addition to that, if interactive capabilities and data visualization are combined together then it will be easy for the users to dive into the finest details of graphs, charts, maps, dashboard, etc, and helps them to preserve the important data analyses and insights.

On the contrary, with the help of JavaScript programming, the developers can come up with interactive and attractive charts quite easily, directly from the chart libraries. No matter what, be it the requirement for an open source library, a paid-for, or any other types. You can surely find the required one to uplift your visualization skills. There are a number of charting libraries, that includes, Google chart, AnyChart, D3, and Highcharts are the one that worth mentioning. The creating process for all of these is in most of the cases quite similar. Hence, mastering one of then give you the privilege to use the other libraries. Certainly, when you want to add any specific characteristics for any of them.

For the purpose of JavaScript tutorial, let us take the examples from the AnyChart library. This is easy to use and quite flexible as well. It has exhaustive documentation, a wide range of supported chart types verity, and also a code playground, that allows you to test the codes, etc.

Therefore, let’s get into the process of how AnyChart helps in meeting the need for data visualization.

Here we would be addressing the three easy steps that can be used to create a basic bar chart in JavaScript. You can later integrate the same into your website or even in application. These are:
– Data Preparation
– Go to the JavaScript charts library
– Create or enter codes

Now, let’s talk about each one of them in detail.

Data Preparation

In case the data type is unstructured, then you need to prepare the same for the purpose of easy loading into the chart library. According to the format of the library you choose, you need to process the data in the format that the library accepts. However, AnyCharts supports a verity of formats for data including the other ones mentioned above.

For almost all of the charts you create, all you need to do, just put the values for both X and Y axes. Again, for the purpose of making Bar Charts, you will be required to put the values for Y-axis followed by an index number or any item number. This number will be considered as the value for the X-axis.

For instance, let us consider the data in array format, then X will be the item number and data values will be Y axis.

Again, when the data is on JSON format, it will look like this.

Connect the Chart Library

In order to create a connection to the preferred JavaScript chart library, you need to download the relevant package and install the same locally. You can also use the CDN service. In most of the cases, CDN service is the most preferred one. As it will give you the privilege to load data from server Library’s files to the users directly. That in turn as well increase the page load time and enhance the experiences.

Furthermore, AnyChart has a module-based system which allows you to connect to the specific chart type and the features that are essential for your project. It will also help you shrink the code sizes that are running on the application.

Such as, when creating a bar chart using the AnyChart JavaScript, you will be required to add below-mentioned core and basic Cartesian modules.

Writing the Codes

To create a JavaScript Bar Chart, you need to follow the steps mentioned below

To start with let us start with creating a container on the HTML page, it will refer the bar chart.
Then put all the previously processed data on the above-mentioned step.
Let’s create the type of charts that we are going to create from the applicable chart constructor function.
Create a title for the chart and axes
Now at this point create a bar series and put the data
Point the chart to the container the created earlier and start outlining the bar chart
Hence, you can easily understand that creating a bar chart using AnyChart is pretty much straight forward and simple.

Now let’s concentrate on the factors to enhance the looks and feel of the chart, by using some advanced level steps.

Create Advanced JavaScript Bar Charts

From the above discussion, the process of making the bar chart using AnyChart is clear. Thus, dive into other possibilities, when we have to perform a bit complex data visualization job to do. Of course, JavaScript chat creating is not at all complicated for smart web developers.

Lets, go to the advanced method that you can use from the same library.

Create Multi-Series Bar Chart

Besides the single series bar chart, AnyChart JS will allow you to create a multi-series bar chart, that enables to show the multiple sets of data on charts plot. Series referred to the single data set in AnyChat that will also display in chart’s plot. Now with the help of multiple series chart, it is easy to visualize the detailed information with the insights to the audience.

To build a Multi serious bar chart using AnyChart, it is essential to add data adopter module. It will help you load the HTML data in the work environment.

Parse data from the HTML table

Now create the data and specify the data source

Add a legend if you want your audience to understand and read the meaning of the values

Upon running the codes, it will create the multi-series bar chart displaying the composition of the values and understanding of the data as well.

Stacked Bar Chart

It is quite easy, without making any huge changes to the multi-series bar chart you can create a stacked bar chart. There are two ways, value stacking, and percent stacking. You can choose any one by setting the scale of the stack mode method to the “value” and “percent”.

Heres how to set:

This is how to set the mode back to the 100% stacked bar chart

Set Interactivity to Charts

All the charts prepared by using AnyChart are interactive by default. Some of the default chart behaviors are highlighting and points when hovered over, directing to hovered tooltips point, etc. In addition, you can tailor the chart’s interactivity to match the specified requirements.

Final Thoughts

Considering all the point it is clear that, creating the interactive JavaScript (HTML5) charts is quite easy with the appropriate JS library. So, here in this guide, we have just touched the surface. You can also visit the AnyChart’s documents and learn other factors.

For any queries, or specific instructions do let us know in the comment section below.

Artificial Intelligence to diagnose Alzheimer’s disease.

Alzheimer’s disease. The diagnosis and treatment. Artificial Intelligence. The first two phrases are directly associated with each other. Artificial Intelligence or AI, however, is closing the gap of difference pretty quickly to emerge as the technology that can detect Alzheimer’s well before its diagnosis.

Before we delve into how AI is helping out, it is essential to first know about this disease itself.  We will help you do just that.

UNDERSTANDING ALZHEIMER’S

Alzheimer’s is a dominant reason for the occurrence of dementia, which is a layman term for memory loss and various other cognitive issues severe enough to hinder a routine life. The disease is not a standard parcel that comes with aging but has a high risk of developing at an old age. A considerable part of people with Alzheimer’s belongs to a very young age group too.

The fact that Alzheimer’s is a progressive disease that does not make matters easier. The dementia symptoms gradually take a turn for the worse with time. Initially, the memory loss is pretty mild, but as more time passes, people even lose the ability to have a simple conversation and react to their surroundings.

Currently, the disease has no cure. The present treatments can only help in slowing down the progress of Alzheimer’s, but it is only temporary as the situation worsens over time.

The earliest symptom that an individual has Alzheimer’s is when there are problems faced to remember the recently learned information. It is because the disease initially affects that part of the brain which governs learning. As it grows, the symptoms become more severe like disorientation, mood swings, increasing confusion about time and events, and trouble in talking too.

Alzheimer’s disease is a focal point in today’s biomedical research. The researchers are making constant efforts to detect as many facets of Alzheimer’s and other types of dementia as possible. The most fantastic progress has provided information as to how it affects the brain cells.

ARTIFICIAL INTELLIGENCE AND ALZHEIMER’S

Some recent studies have conclusively demonstrated how AI can lead to an improvement in brain imaging to predict the earliest stages of Alzheimer’s disease. According to them, AI will be able to detect the disease in patients about six years before a confirmed diagnosis comes to the fore. It will lead to incorporating the changes in lifestyle, preparation, and methods of treatment well in advance.

Thanks to more and more innovations which help out in the early stages of Alzheimer’s, early diagnosis will mean that the patients will have more time in financially, personally and legally prepare themselves for their treatment.

With more research conducted every day, newer ways for diagnosing Alzheimer’s and dementia forms are getting tested. From brain imaging to blood tests, the hunt to find some of the most affordable ways to diagnose is on- much before even the symptoms start to show.

By the use of a usual form of brain scan, researchers were able to programme a machine learning algorithm for diagnosing early stages of Alzheimer’s as much as six years in advance before a clinical diagnosis, which will give the doctors a possible chance, to begin with, the treatment.

While no permanent cure for Alzheimer’s disease is available quite yet, some promising drugs have come into existence since the past few years which can help to stem the progress of this condition. These treatments, however, need to be administered early in the course of the disease to do some good. The race against the flow of time has motivated scientists to search for ways to help diagnose this condition much earlier.

Positron emission tomography (PET) scans, which can measure the level of particular molecules like glucose in a brain, have been analyzed as a useful tool for diagnosing Alzheimer’s disease even before the symptoms tend to get severe. And, this is a revolution in Healthcare Industry.

Glucose is like a driving fuel for the brain cells, and the more active a brain is, the more glucose gets used up. As these brain cells die out, they use less and finally, no glucose at all. Other kinds of PET scans look out for proteins that are mainly related to Alzheimer’s disease, but the glucose PET scans are cheaper and more common. This is mostly true for smaller health care facilities and also developing countries, as they also help for the process of cancer staging.

Radiologists have used the PET scans to try and detect Alzheimer’s disease by having a look at the glucose levels through the brain, particularly in the areas of frontal and parietal lobes of the brain. But because it is slow and progressive, the changes in glucose level are pretty subtle, making it difficult to spot with a naked eye.

To sort out this issue, the machine learning algorithm was applied to the PET scans to help with the diagnosis of early-stage Alzheimer’s disease with more accuracy.

For training the algorithm, images from Alzheimer’s Disease Neuroimaging Initiative (ADNI) served as the input. ADNI is a substantial public dataset of numerous PET scans from the patients who were diagnosed either with Alzheimer’s, a mild cognitive problem or no kind of disorder.

After a point of time, the algorithm started to learn on its own about the features which were considered necessary for prediction of the diagnosis of Alzheimer’s and which were not.

Once the algorithm became trained on a vast number of scans, the scientists tested in on two kinds of datasets for an evaluation of its performance. It passed the assessment very successfully, with an estimated 92% of patients who had developed Alzheimer’s identified correctly.

At such impressive statistics, this algorithm has a lot of potential to be clinically relevant. If it can perform well in such kind of tests, the algorithm can then be of use when a neurosurgeon looks at a patient in a clinic as a diagnostic and predictive tool for Alzheimer’s, proving very integral to get the patients a treatment which they require much sooner.

**We at GoodWorkLabs help Healthcare Companies develop essential Apps, Web, and Software Solutions using AI!

Want help in building a technology for your healthcare business needs? Work with GoodWorkLabs who understand and have expertise in this space. Contact us to get a free quote for your project.

Dating apps are increasingly taking the help of AI!

It is apparent that you will have used a dating app at least once, even if you never dared to admit it openly in your social circle. The premise of most dating apps is the same; take a look at the picture visible with a little information and then decide to take a swipe left or right. These swipes determine your rejection or interest to the profile of a particular person respectively.

During development stages, these dating apps were a little cluttered and confusing to move through. Today, however, you can just bid a farewell to hours of mindless swiping through numerous profiles. Thanks to Artificial Intelligence.

Dating apps are increasingly taking the help of AI to help users suggest places to go for a first date, indicating the initial remarks that can be said to the person at the other end. To make the matter all the more intriguing, these apps even assist you in finding a partner who resembles your favorite celebrity.

Until very recently, smartphone dating apps like Tinder left the task of asking someone out and making a date go well to people who were using the app. Gradually, this led to fatigue in the users who had to keep searching through a lot of profiles without too much success.

This is why the online dating sector turned over to take the help of Artificial Intelligence and get people to arrange dates in their real lives, acting more like a dating coach of sorts.

These newly found utilities of Artificial Intelligence, where the computers are programmed to develop human processes like thinking or decision-making have been highlighted time and again, signifying its importance.

Uses of Artificial Intelligence for Dating Apps

If anything, dating websites and applications have established themselves as the new benchmarks when it boils down to getting the first date for yourself. This is why as we mentioned above, many websites and app owners are trying to use something different on the lines of AI to ensure and provide the users with a fantastic overall experience.

Here we look at how AI is improving the dating lives of users along with the user experience of a dating app or website as a whole-

1. Help find better matches

Being the most obvious use, of course, AI for dating apps helps to improve the matching of people with their potential dates. There are two pretty remarkable methods through which this is happening. The dating app Hinge has recently been observed testing a feature which they call Most Compatible that takes the help of machine learning in finding better matches.

The feature monitors how people behave on the app. This behavior involves the kind of content a user has previously liked. The function aspires to serve as a matchmaker to find you, people, with whom you matched with on the platform prior.

The dating sites presently are as good as the data they have. Keeping that in mind, the dating sites are increasingly making use of technology and suitable data to filter out the matches for their users. There are many cues like emotion in communication, revert times and the size of profiles too.

2. Keeps things in moderation

Keeping things moderate on dating apps is very important for two essential reasons. It is evident that you wish that people have an overall positive user experience. If people have to continuously swipe with the fear of accidentally getting a fake account, they will ultimately switch over to some other app.

Moderation has also become essential to protect the app company itself. Many authorities are taking down any web platform which is not severe for sex trafficking and related crimes.

This has left with moderation not being an option anymore for brands, effectively going them with two options- manual moderation or automation enabled by computer vision (CV) moderation. Only one method out of the two helps a dating app scale and moderate more content at lower costs, and that method is computer vision.

3. Prevents security concerns

For any user of dating apps, security is one of the prime concerns. One negative experience is more than enough to turn people away from a specific app permanently. It is essential that dating apps take this very seriously and invest in measures to make their platforms secure to the maximum possible extent.

Getting every individual with enough help for a date is going to be impossible, and this is why companies will have to depend on AI to take care of this issue. An app called Hily gives the users a “risk score” that provides a user with passing ID verification, past complaints, the extent of conversation with other users and time spent on the app.

An individual with a high-risk score can be blocked on the app by the other users from sending their private information to the particular profile. The app can also detect when a photo has been tampered and then blocks such users too.

4. Provides great & useful user content

The final use of dating apps for the dating scene in 2019. Many factors make a dating app interactive and user-friendly where they can move to have a good time. Selfie images and information related to the profile of an individual are part of the content which is available on such apps.

AI can be used to provide better advice to users as to what they could do to improve their dating profile and visibility. For instance, online dating coach Greg Schwartz used face recognition model Clarifai to create an app which could recognize the standard errors that people tend to make in the photos they use on certain dating apps like using the images of fancy cars and bikes to get an impressive looking profile.

While not everyone has the same opinion that Artificial Intelligence is going to help them out in finding the love of their lives, the trend is currently on the rise, and it will be fascinating to see how things further unfold within this year.  

To know more about how AI can help your business, reach out to us:

Tips to improve Automation Testing

As people of the digital age, you realize that the devices you use for everyday functions, including your business run on software. If you are one of those with good enough technical awareness, automation testing will not be a new term for you. However, if you have not heard of it, we will help you out!

Automation testing is a process where an application implements the entire life cycle of software in very less time, providing the testing software with a lot of effectiveness and efficiency.

In this automatic technique, a tester writes a script on its own and then takes the help of appropriate software to test the software. The main intention behind automation testing is to boost the test efficiency and develop the value of software.

Automation testing helps in uncovering those parts of a code which are not getting tested. A low automation code coverage surely has an impact on the product quality, putting irrelevant pressure on testers to manually check it.

There are a lot of hurdles for Quality Analysts that result in low automation coverage-

• Long-running projects also come with a long list of deliverables which leads to heavy pressure. In a bid to fulfill the delivery deadlines, the automation tests get avoided.
• A legacy app which is not suitable for unit testing. It is an issue where the entire logic of the app is in the database queries as it stacks some tests but with very few unit tests.

7 ways to improve Automation Testing

Here are seven tips which will help you out with automation testing and improve the automation coverage:

1. Capture tests at the moment of story creation

You can include your test cases in story cards, along with the acceptance criteria highlighted by Business Analysts. It motivates developers to adapt to a tester’s perspective, helping them understand what tests should go in which layer of the test pyramid. If the testers write test cases in advance, they can plan out easily for story testing.

2. Estimation of Automation Testing efforts

Make a point to include Quality Analysts in estimation sessions and let them be accountable enough to explain the roadblocks like additional data setup requirements or even a change in approach to testing. For instance, a change in code might look small but can have a pretty massive impact on the entire application. It will require more tests which means a 1 pointer story might not be so.

3. Check and run tests in Devbox

Quality Analysts can be instructed to check automation tests which constitute a part of the story, on DevBox. It helps to ensure that all the functionalities are in working order and no existing tests have broken. It is a phase where testers and developers discuss if the written tests are in the appropriate layers of the test pyramid and the data setup is right.

4. Attention to Gits Commits has a dual advantage 

If quality analysts keep an attentive eye on the Github commits of all features and sub-features which have been picked up for testing, a better scenario of API and pending tests can be created. It helps the testers make a note of all impact areas.

For example, if a change of code at a particular area impacts a functionality, automated tests for the affected functionality can be written.

Comparison of the release branch with the master branch is advantageous when automation test coverage inclines a little negatively with the release along with a large manual regression phase. Such a situation does not let the efforts of testers go in vain on the unaffected areas but stays focused on the functionalities affected.

5. Introduction of code coverage tools 

Publishing the code coverage reports of test suites for the full team will put a lot more emphasis on writing tests for automation. Also, if the tool becomes a part of the build pipeline, the pipeline can be failed on a drop which goes below a pre-decided threshold.

For example, an existing coverage of code can be 45% while the benchmark is at 40%. If the development of a new story gets concluded without any tests, the coverage can then come down to 35%, failing the build as it could not meet the criteria. It is a practice which is helpful to all the stakeholders be responsible for automation on equal terms.

Code covering metrics can also be helpful for teams to identify automated areas and restrict the use of manual regression cycles, thus bringing a stop to manual testing.

6. Make an automation backlog board

Once a coverage report is all set, it is an excellent option to analyze functionalities which correspond to a code. The next step is to create test cards for these functionalities. Getting an automation backlog board ready in the project management tool with test cards lends visibility to the work which is to be completed and ensure product quality.

The backlog will require attention and time for improvements on code coverage.

7. Automation of Backlogs

The moment an automation backlog is ready, these operations can begin:

Quality Analysts and Business Analysts can examine and prioritize on the backlog cards and to automate per iteration and minimize the backlog. Try to include Quality Analysts when it comes to the writing of automation tests, which can be between the iterations or at the time of regression.

Allot the functionalities among the Quality Analysts to inculcate a feeling of ownership and responsibility for closing backlogged cards. Keeping track of ROI in terms of time saved can prove to be an essential record to seek approvals to clear an existing backlog.

Conclusion

Thus, making sure that a developed product is of high quality is a collective priority of a project team. It is important that the entire team has a good understanding of the test pyramid so that the right kind of tests slot into the correct layers of the test pyramid.

All the ways mentioned above if brought at the correct time will help in easing the automation backlog in a project. Quality Analysts can not only a dream but also get to their goal of great automation code coverage.

If you have any queries and are looking for help with automation testing, then let us help you. Contact us here and we will get back to you shortly.

Agile Methodology Tools in Business

Timely delivering projects under a defined deadline and a set budget is a priority for those companies who wish to maintain their credibility, reputation, and prestige. Projects that get delayed give a hard time to the enterprises throughout their hierarchy because late project delivery has a significant impact on the morale, level of productivity and focus as well. To make matters worse, incorrect implementation of agile methodology might force the employees to leave the company due to excessive stress.

In such a stressful situation, the single best thing an employer can do is to take a step in the right direction at agile methodology, which is where the real tools of project management enter to play. The tools help in the identification of the actual status of a project, the expected tenure of a project and all its practical applications.

Entering into the world of project management, one is a witness of the importance of flexible working methods while also ensuring the implementation of futuristic and latest techniques for gathering the results quickly. There are some project management tools which come in handy to assist in the implementation of Agile project methodology of management.

The eight best tools which we have for you to choose from are:

1. Trello

One of the most widely utilized tools of project management, Trello is renowned for its straightforward user interface (UI) and easy usability. The functioning of Trello can be figured out even by a beginner who does not have too much knowledge in the field of project management.

Trello gives you cards along with dragged columns. The primary columns are three of them that include To Do, Doing, Done. Pulling the map to the appropriate box involves the rest of the tool to plot and create new columns, a rapid and simple procedure.

The cards are objects which can be assigned to resources that are relevant and include the estimation, completion process as well as delivery dates of the projects underway. The reputation of Trello is evident from the fact that even Twitter makes the use of Trello.

2. Visual Studio Team Services (VSTS) 

If you love using Microsoft Stack, VSTS is the perfect tool for your needs. The device facilitates easy integration with Visual Studio, helping manage a technical project with maximum ease. Until five users, the option of using VSTS is free and some premium features that can be purchased. The best feature of VSTS is the mechanism to trace any changes in the code which is the best thing a developer can ever hope for.

3. JIRA 

When you talk about authenticity, Jira is that tool which lives up to your expectation in project management and is known for being the best tool for tracking the records of jobs done through Agile management. Be it small businesses, enterprises or big organizations, Jira is ideal for business of all sizes.

Just like Trello, columns and tickets are there for you to display the different phases of your work. These tickets can be made and then be attached to a resource. When you complete a sprint, the performance of each can be measured through pie charts and graphics representations too.

4. AXOSOFT 

It is a software for an Agile project which is helpful in the identification of bugs in the project and then taking up an accurate Scrum framework to plan these projects. Axosoft has many tools which make the developers work conveniently and create features which are under the budget, on the right schedule and free of bugs.  

Agile followers are in love with Axosoft because of the way this software helps business through the creation of an Agile workflow. The progress report of each is very transparent, and Axosoft also keeps it centralized which ultimately results in practicing Agile methodology to a maximum extent in any team.

5. ASANA 

Asana is one among the best task managing software, and facilitates a team for planning, sharing and tracking the advancement of a project with the mapping of every resource’s performance within the organization.

The interface is pretty easy. You need to create a workplace, add the projects required for completion. It is easy to allot, track and organize the tasks thereafter. You can also add up notes, comments, and tags to be clear and expressive with the motto.

6. Zoho Sprints 

Zoho Sprints assigns you the authority for creating backlogs through a drag and drop feature. You can also stretch the stories of individual users with priorities, which is an added feature other than allotting tasks to a team.

Every work item can be noted duly in a time sheet that has budget control measures like the billable and non-billable hours for a particular piece of the project.

7. WRIKE 

Wrike tool has dashboards, customizable workloads, and charts which boost a project in flowing freely. There are a lot of updating options where all kinds of scattered information that rests on your mail, images, and documents can be easily accessed. Simply put, WRIKE helps in streamlining the workflow that is relevant to the timely completion of a project.

WRIKE also features the collection of necessary information from the cloud, sending of emails and also seamlessly merges with applications like JIRA and Salesforce.

8. Velocity Chart 

This tool helps to have an idea about the value that is generated in every single sprint, helping you to estimate the amount of work which will be completed in subsequent runs. In other words, you can easily measure the velocity of your team’s work.

The Velocity Chart adds up the estimates for every complete and incomplete story. These estimates can be on factors like hours, business value and any other factors that can be assigned to a numerical value.

If you wish to include Agile methodology in your project management practice, the eight tools that we listed above can prove to be crucial to have quick and efficient project management. If you would like to implement agile methodologies to your project, you can contact us here.

The importance of choosing the right technology stack

The use of the right technology stack is the essence of a successful digital product. But choosing the right blend of technology is always tricky. 

At GoodWorkLabs, we offer an expert tech consultation that is unique for every digital product in question. In this post, we have given a more generalized road to help you choose the right tech stack for your application. We are laying down all the possible options for your reference so that you can manifest the right blend for your brand.

Technology stack: Definition & Popular Technology

In layman’s language, web app development requires a database, a server, HTML+CSS, and programming language. All these layers put together, form a tech stack for web development.

Technically, a technology stack is a combination of components which satisfies all the layers of mobile or web application and can directly affect the app functionality. The anatomy here is very simple with two major layers:

• the client-side (frontend; the presentation, what the user sees)
• server-side (backend; the website’s functionality, processes)

Frontend frameworks and libraries:

1) Bootstrap:

• Customizable, saves time, easy to use with a bunch of other helpful components.
• Recommended when you are opting for a ‘mobile first’ application.

2) Angular:

• JS-based framework, good for projects with easy code integration
• New Angular 5 makes it easy to reduce the runtime with the built-in code optimizer
• It is recommended for developing single-page web applications, cross-platform mobile apps, landing pages, and common websites.
• Already used by Google, PayPal, and Upwork

3) Vue.js:

• JS framework which easily integrates with JS libraries
• It is recommended for large-scale and single-page projects.
• Already used by Alibaba, WizzAir, Grammarly

4) React:

• JS library for making user interfaces (UI)
• Active community with numerous ready-made components
• Quick development
• It is recommended for web applications or platforms which require a very responsive UI.
• Already used by Facebook, Reddit, Netflix

5) JQuery:

• JS library that is used for code optimization

Programming Languages:

• PHP:

PHP is particularly designed for web development and creating dynamic web pages. Though it had certain vulnerabilities, it is considered to be the most popular language. Also, as PHP based apps are easy to code, it means that you can cut greatly on expenses by saving time.

• JavaScript (JS):

JavaScript is a convenient, versatile and effective high-level programming language which can be used for both server-side and client-side code. It is recommended for dynamic, agile and modern websites.

• Java:

Java is well-documented and supports numerous libraries. It is used widely for both complex website and dynamic mobile apps. The popular frameworks are Hibernate, Grails, Spring, Dropwizard, and Apache Wicket.

• C#:

With the capability of processing heavy data flow and the flexibility to create all kinds of application, C# is a popular cross-platform technology among developers.

Backend frameworks:

1) Ruby on Rails:

• One of the popular tech stack among startups, ROR is perfect for all kinds of apps from basic web pages to high-traffic web portals. 
• For developers, ROR is very easy to learn and use
• It is fast and scalable
• It uses DRY (“don’t repeat yourself”) design pattern and MVC concept (“model-view-controller”)
• RoR is already used by Airbnb, Basecamp, Twitch, Shopify,  and Zendesk.

2) Django:

• Django is versatile and can be used for startups, medium-sized projects, and high-loaded websites. It is a clean, secure, fast, and scalable framework for rapid development. Along with being well-documented,  it comes with its own lightweight server.
• Already used by Discus, Mozilla, Instagram, and National Geographic.

3) .NET:

• .NET allows developing any type of web app faster and making it scalable. It is very easy to add APIs and live communication features. It has an active community and is extensively documented.
• Already used by Xbox.com, Microsoft, Stack Overflow

4) Node.js:

• Node.js allows optimizing code on complex, high-performance, and data-intensive real-time apps. It is simple, fast, and expressive. It is recommended for apps that involve real-time streaming, collaboration tools, and chatting.

5) Express.js:

• As the name suggests Express.js is a minimalist, flexible, and resource-efficient framework which uses templates and requires minimum efforts. It is recommended for APIs and simple web and mobile services.

6) Flask:

• Flask is another well-documented framework with a highly active community. It is recommended when the client requires to build a service on a resource-constrained system. Also, it is good for serious websites and RESTful APIs.

Databases

1) MongoDB:

• MongoDB is a NoSQL, document-based database which can be used for storing large volumes of unstructured data. It can also be used in a cloud-based environment.

2) PostgreSQL:

• PostgreSQL has multi-version control and supports custom data types. Basically, it is an object-relational database with NoSQL features and is used for storing a gigantic volume of data (up to 32 TB per table).

3) MySql:

• The plus points of this most popular relational database are that MySql is highly scalable, easy to set up, cloud-reafy and is platform independent.

Popular technology stacks

You can also pick from already designed popular web stack. They have a solid foundation and you can easily customize them as per your requirements. The major tech stacks that have been used are LAMP(Linux-Apache-MySql-PHP), MEAN (MongoDB-Express.js-Angular-Node.js) and .NET.

LAMP alternatives:

• WAMP: Windows, Apache, MySql, PHP
• LAPP: Linux, Apache, PostgreSQL, PHP
• WISA: Windows, IIS, SQL, ASP.NET
• XAMPP: Linux, Mac OS X, Windows, Apache, MySql, PHP, Perl
• MAMP: Mac OS X, Apache, MySql, PHP

MEAN alternative:

• MEEN: MongoDB, Ember.js, Express.js, Node.js

Conclusions

The success of your project majorly depends on the tech stack that you choose in the beginning. With so many fishes in the pond, it is difficult to say which one will work best for you. But GoodWorkLabs can help!

Let’s discuss your requirements and compile the perfect tech stack for your next project. Drop us a quick message with your requirements and we will have our tech expert get in touch with you soon