To prove we are in the digital era, let’s recall the numbers.

Did you know that the number of smartphone users worldwide surpasses 3.5 billion? As per the Statista- Smartphone User forecast, it is estimated to further grow by several hundred million in the next few years. 

There are 7.94 billion devices connected worldwide and this number is more than the number of people in the globe! Hence, there is no denying the fact that mobile applications are an integral part of our daily lives. 

Keeping the above data in mind, many entrepreneurs are planning to start a mobile app centred business and investors are funding different types of mobile apps.

If you are amongst those businessmen who are looking forward to creating a mobile app, then the first question to address is – which is better among Android vs iOS development? 

In this article   you will walk through certain factors that will influence your choice, and embibe you with the iOS and Android App Development Platform.

Let’s get right to it.

Apple vs Android: Market Share

According to Statista, in 2020 the market share of Android and iOS was 86.6% and 13.4% respectively and these numbers are expected to reach 87.1% for Android and 12.9% for iOS in 2023.

Looking into the above graph, you can conclude that Android is a clear winner in the mobile operating system market share worldwide and is expected to remain so in the years to come. It is so because the Android startup apps are the most adopted ones for almost every smartphone vendor other than Apple. 

Also, Android is an open source platform that allows and makes it easy for mobile phone manufacturers to add their own look to the operating system. 

Apple vs Android: App Downloads

According to Statista Market Forecast 2016–2021, there will be 196 billion annual downloads from Google Play store by 2021, all thanks to growing smartphone and app adoption worldwide. 

While from the iOS store there will be 42 billion downloads. Android again wins when it comes to app downloads worldwide and it is expected to enormously grow in the years to come.

After reading all the above facts and data  it is highly advisable for you to  make yourself familiar with the process of startup app development. 

We have curated a complete guide on apps for startups, with A to Z information about the process of getting your idea live on a mobile application. In this article, we will answer all the questions that every entrepreneur who is new to the app industry might have.

Which Is Better – iOS Or Android?

Android vs iOS development is a never ending debate between software developers. The quest to- Which platform should startups choose has no one word answer. There are solutions that depend on various factors that an entrepreneur should consider before making a decision. 

Let’s go through all the factors one by one:

1. Demographics 

There is no denying the fact that Android smartphones have a larger demographic than apple users. 

Also, there is no denying the fact that Apple is considered a high end device in which users are willing to purchase apps. So, Apple users are generally found in prosperous parts of the world. 

For example: The USA is considered among the highest revenue making countries, thus you will find a larger number of iOS users there. 

As per the reports by Statista, currently there are more than 113 million iPhone users in the United States, accounting for about 47 percent of all smartphone users in the United States. So if you are targeting the western demographic, I suggest you to hire iphone app developers. 

2. Fragmentation

In simple words, fragmentation refers to when users are running different versions of a mobile operating system and using different mobile hardware models or mobile devices. 

We are aware that iOS devices and their release cycles are controlled by Apple alone. As a result, once a year Apple synchronizes iOS version releases with device releases. Hence, fragmentation issues are less. 

However, when we talk about Android, fragmentation issues occur constantly, further making life of the android application developers difficult at the time of testing and quality control. Thus, Android fragmentation increases development cost and maintenance time.

3. Design and development

In terms of designing, Google Material Design has a greater influence on UI and UX. Mobile app developers feel that when it is about coding mobile apps then Swift is a much easier language to start as compared to Java.

However, design and development is one factor that depends less on the platform and more on the skills of your partnered mobile app development company. When you are linked with a brand that has it specialization in the development of both Apple and Android apps, it is of the least matter about which platform requires less developmental efforts as both are done within equal efforts.

Now that we are aware of the factors involved, let us discuss the reasons to go for android startup apps and iphone startup apps separately. 

Why Choose Android Startup Apps?

You should go with Android first if your audience is not concentrated on any one specific demographic or target audience. Also, if there are a good number of customization elements in your mobile app, go with Android. 

Below are the advantages of using Android. 

1. Greater user base 

Majority of users globally use Android devices as compared to other devices, which gives you a large pool of potential users.Not having an android app for business means losing limitless opportunities and audience for your product or services. 

2. Open source platform 

Android is an open-source platform which means Google doesn’t charge any fee for using this platform. Wherein, Google also provides Android app development tools and technology for free to the developers. Thus Android app cost is comparatively lesser than iOS app cost. 

So, it is a cost effective solution for your startup budget.

Hardware device manufacturers such as Samsung, Oppo, Xiaomi, etc., all use Android as their default OS. 

3. Customizable apps 

Android app development allows the app developers to customize the applications as per the business requirement. This means your business app development will get done with the right requirements and required flexibility which might not have been possible with any other platform. It is always a good idea to hire an android app development company that can help you with your startup app. 

4. High ROI

The moment you publish your app on the store, you get a big pool of potential users that you can tap into. Since you have such a wide market to target, the return of investment on your android app would be instant and always on a higher end considering your Android app development cost. 

5. Compatibility

One of the biggest advantages of choosing an android app over iOS is that there’s no restriction on devices that is to be used for building an android app. You can build an app on any device, be it Windows desktop, Mac, or the Linux system. 

The fact that makes Android a go-to platform for all the sectors with interoperability needs is that it allows you to expand your brand across devices and systems . 

We now understand the advantages of choosing android apps. Let’s dig deep and see what are the reasons to go for iOS app development for your startup. 

Reasons To Choose iPhone Apps

You should place your startup’s first mobile app on iOS by investing in a sound reputed iOS app development company if you wish to come in the sight of the app store’s target demographics. 

Let us discuss the advantages of choosing iOS apps for startups:

1. Security

Security is the utmost requirement for any business because sensitive enterprise data is lodged in apps. Android apps are a big risk when it comes to security while iPhone users are cushioned against hacking and malware. When you compare the iPhone vs Android on the basis of security, iPhone apps protect firmware and software through stringent security measures such as :

• Integrated data handling systems
• Measures to prevent duplication of data
• Measures for loss of security by data encryption

2. Revenue

The ones that have a greater ROI than Android apps are the iPhone apps.. The best revenue generation opportunities that you can get from your iOS application development process, is to keep an eye on the mistakes, tips and tricks, and other related information would be a big advantage. You can look for iOS app development services that can help you with your vision. 

3. Established customer base

The biggest USP of Apple is its established customer base. Apple is a pioneer in technology and applications. Apple has a well-established niche of its customer base that swear by Apple’s quality and performance and are loyal to the brand. That’s why it is said that once a smartphone user experiences the iOS platform, they will never be satisfied by any other OS and will stick to Apple. 

4. Low fragmentation and testing

As discussed earlier, Apple generally develops just one updation on its existing OS every year. Also, the number of Apple devices are lesser than Android-based ones. Thus, Android apps should be tested comprehensively to get its better functioning on all the versions of Android OS. 

On the other hand, iPhone apps just have to meet testing criteria of its previous iOS versions. This constantly reduces testing time and guarantees a rapid time to market for its apps. This also results in saving apple app development cost. 

Wrapping Up

After reading the write up you must have understood that there is no right or wrong answer, it all depends on your requirements. We have seen specific scenarios favouring the iOS platform and others suggesting us to go for Android. 

By keeping all the above information in mind, you can contact a mobile app development company that will help you build your app without worrying about the operating system.

The post Which Mobile Platform Should Startups Choose in 2021: Android Or iOS? appeared first on Appinventiv.

There was a time when cross platform development was considered a synonym for building an app using React Native and Flutter. The business enthusiasts and mobile app development companies were relying upon these two frameworks to make their presence on both Android and iOS in a cost-effective manner.

But now, Kotlin has evolved and entered the competition, making everyone wonder who will be the ultimate ruler of the cross platform application market. Since, we have already looked into the Flutter and React Native comparison earlier, here, in this article, we will talk descriptively about the Kotlin vs Flutter comparison and help you find the right option for your cross platform programming and development needs.

While we’ll be discussing the cross-platform functionality of Kotlin here, you can learn about how Swift can be used for developing Android apps in this blog.

But let’s learn the ropes of both first.

What is Kotlin Multiplatform?

Kotlin multiplatform, is an additional feature of the language that expand beyond android app development. Meaning, it enables developers to share codes, data, and business logic across multiple platforms such as iOS, Web, Linux, MacOS, Android, and Java Virtual Machine (JVM).

Since we’ll be focusing on the multiplatform functionality here, you can get acquainted with Kotlin’s native platform characteristics in this blog:- Kotlin for Android App Development – The Whys and Hows and Bonus Tips.

The concept of investing in Kotlin for cross platform app development is still new in the market, but it is surprisingly embraced by various popular brands.

What is Flutter UI Framework?

Introduced back in 2015 with an intent to prepare a market for Google Fuchsia, Flutter is an open-source SDK that enables to craft beautiful, native-looking mobile, web, and desktop using a single codebase.

Google Flutter targeted numerous prevailing technological and market challenges and provided effective solutions. A result of which is that Flutter became an ideal cross platform app development framework in no-time. Not only this, it entered into the list of technologies that top brands work with.

Now, as we have touched base on both, let’s see which is better– Kotlin or Flutter – for cross platform mobile development.

Factors to Consider for Comparing Kotlin Multiplatform and Flutter

1. Market Popularity

Both Kotlin and Flutter are open-source and free to use tools. Because of this, developers are showing an interest in working with both.

If you check Google Trends, Flutter is showing a steep rise in its popularity when compared to Kotlin. Likewise, if you compare Kotlin and Flutter based on their GitHub presence, the former has 28.3K stars and 3.29K forks while the latter has 69.5K stars and 8.11 forks.

This indicates that Flutter is getting a huge momentum in the market, compared to Kotlin.

2. Learning Curve

If you consider the learning curve as a factor of comparison in the Flutter vs Kotlin native battle, the latter undeniably wins. A reason behind is that it is interoperable with Java and Google offered various Kotlin learning courses a few months back.

But, when talking about Kotlin and Flutter, the scenario is somewhat different. The Kotlin Multiplatform is a new ecosystem and has limited resources available in the market, in comparison to Flutter. 

Because of this, Flutter wins the competition.

3. Performance

Flutter language lets developers use the same language for both layout and backend needs, enjoy higher animation speed, and much more. 

But, in comparison to Kotlin, it is still lagging much behind in the market. The prime reasoning behind which is that Kotlin code compiles exactly in the format as that of the targeted platform.

So, if you ask which is better kotlin or flutter? Then the winner of this flutter vs kotlin performance is the latter.

4. Integration of Third-Party Libraries and Tools

Kotlin multiplatform operates within the native platform ecosystems rather than building one. So, app experts can employ the same tools and libraries they have been using during native development, including Jetpack Compose and SwiftUI. Which ultimately means they need not look for third-party libraries and tools to bridge a connection with native environment.

However, it is required in the case of the Flutter cross-platform SDK, provided it is basically a UI development tool.

5. Scope in Backend Development

When it comes to considering backend software development services to know the difference between Kotlin and Flutter, the former again has an edge over the latter.

Whilst Flutter language goes with Firebase, which is an effective BaaS (Backend-as-a-Service) platform, to ensure effective backend opportunities, Kotlin multiplatform enables developers to write backend code.

6. Focus on Multiple Platforms

Currently, applications designed for targeting mobile (Android and iOS) and web come under the Flutter use cases. One cannot use it for making their presence on tvOS, Android Auto, CarOS, and WatchOS.

However, there is no such constraint in the case of Kotlin multiplatform. It is designed to develop app projects for devices and platforms beyond mobile as well.

So, preferring Kotlin multiplatform is a good choice for mobile application development services companies for launching their app idea on every imaginable platform and device.

7. Job and Salary Scope

Another important factor to consider while looking into Kotlin multiplatform vs Flutter language comparison is job and salary scope.

As per the StackShare community, Kotlin programming language is mentioned among the tech stacks by 268 companies and 210 developers, whereas Flutter cross platform is listed as a technology stack by 42 companies and 146 developers.

Now, since Kotlin multiplatform is not an independent platform. It is an experimental extension of the native language and one can use it within Kotlin native environment, it gives an impression that it has more job options in the market.

8. Documentation and Community Support

Compared to Flutter, Kotlin multiplatform is still new and in fact, in an experimental state. Because of this, the documentation and community support for the latter is somewhat lagging behind the former. But, it is expected that the documentation as well as community strength will improve once the multiplatform becomes stable; further encouraging all to use Kotlin for cross-platform development.

Now, we have seen how Flutter and Kotlin differ from various perspectives. It is important to know the advantages and disadvantages of both to make the selection easy.

Pros of Kotlin Multiplatform

1. It’s a SDK, not framework

The biggest benefit of opting for Kotlin is that you won’t have to  shift the complete application. You can begin with a single module or feature, determine its working, and then progressively migrate other parts.  

2. Easy to Learn

Since Kotlin has a syntax quite similar to other top programming languages like Swift, Java, Groovy, and Scala, it is quite easy to learn the language and invest in Kotlin multi platform development.

3. Reuse of Business Logic

It’s true that it is not the first tool that enables one to separate business logic and UI. Various cross platform frameworks like Xamarin and React Native have been offering this service. 

However, the fact that makes it different from others is that it employs shared logic and libraries below the UI layer. This makes mobile application developers interact with the native environment – Swift and XCode for iOS, JavaScript for web, and Kotlin and Android Studio for Android -without using any bridge and deliver codes in a more native way.

4. Native UI Experience

Kotlin Multiplatform does not demand developers to follow any instruction for UI creation. It enables UI app developers to work on the appearance, behavior, and performance of the application just like they would in the case of native.

5. Higher Performance

One of the advantages of considering Kotlin cross platform framework for mobile app development is that multiplatform compiles code to the explicit same format as the targeted platform. This makes it as good at performance as its native counterparts.

6. Enhanced Flexibility and Scalability

It lets developers scale up their app project and operate without using any VM. This makes it a perfect choice for crafting a mobile app while focusing on flexibility and scalability factors.

However, despite such impressive advantages, the multiplatform extension of Kotlin language is still struggling to become the favorite of all because of certain disadvantages.

Cons of Preferring Kotlin Multiplatform

1. Still in Experimental State

Though introduced in Kotlin 1.2, the SDK has not yet reached a stable 1.0 version. It is still in an experimental state; making developers hesitant in picking it for their next big project.

2. Takes Time to Grab Knowledge

Despite the fact that Java and Kotlin have a similar code base or functional platforms, there is a requirement for the organization to prepare the developers on Kotlin. It is on the grounds that the shifting of platforms probably won’t be simpler without appropriate information on Kotlin. This may require an organization to deal with costs in preparing the team for kotlin cross platform.

3. Finite Available Libraries

Currently, only a few basic libraries like data serialization libraries and HTTP client libraries are available in the development environment. Although many more libraries like DateTime library enter the market with the advent of Kotlin 1.4, the successor of Kotlin 1.3.60, it is tough to build applications seamlessly and effortlessly till date.

4. Need Familiarity with Other Tech Stack

Since Kotlin multiplatform is not designed to be a replacement for every application programming interface (API) that a platform focuses on, it is required for app development companies to have basic knowledge of each platform and API. This is again a drawback of picking Kotlin over Flutter and other cross-platform development options , which leads to people opting for flutter without considering the location, be it flutter mobile development in Texas to help their business for good.

5. Higher Development Cost

Last but not least, the Kotlin SDK provides several semi-native functionalities. Because of this, it demands higher cost to build mobile apps, which eventually makes it not a good fit for creating MVPs and app prototypes.

6. Fluctuations in Compilation Speed

In different instances of leading incredible developments, Kotlin in general works faster than Java. Thus, Java in general remains as a clear winner in this situation to develop clear applications. The fast speed of Kotlin may not give a clean functional application.

With this covered, let’s take a recap of Flutter before diving deeper into the market to know the Kotlin and Flutter multiplatform relationship.

Advantages of Considering Flutter

1.   Open-Source and Free to Use

Both Flutter and the underlying programming language Dart are open-source and free to use. This empowers developers to view the documentation, post their queries in the open developer forum, and eventually, learn and implement the code. App development with flutter across the globe got recognition and people are making use of this development by opting for flutter app development company in USA, Florida, New York, etc., to achieve their business goals.

2.  Hot Reload Functionality

One of the biggest reasons why Google Flutter is preferred by mobile application development agencies is its hot reloading functionality.

The functionality makes it easier for anyone to make any change at the backend and view the difference at the front-end simultaneously. And this way, easily decide whether the change looks perfect with other elements of the application or not.

3.  Highly Customisable

By investing in Flutter app development services, developers get an opportunity to use different widgets in the development environment. This makes it easier for them to create mobility solutions that are faster and have customized look & feel.

4.  Lower Development Timeline and Cost

One of the prime benefits of focusing on flutter mobile app development is that it renders opportunities to create native-like apps for both iOS and Android using a single codebase. Something that makes the cost to create a Flutter mobile app considerably low.

5.  Support for Google Firebase

With the facility of Firebase integration, apps built with Flutter enable developers to add backend facilitiesy into the development environment. 

6.  Suitable for Creating MVPs and App Prototypes.

Flutter programming language offers the functionality of building an application for both Android and iOS platforms in less time, cost, and effort. This makes Flutter a perfect option for MVP development and creating prototypes that can be used to test the water or raise fundings.

Disadvantages of Using Flutter SDK

1. Bigger App Size

The foremost drawback of selecting Flutter for your application development plan is that the app size is too big. Compared to React Native and Xamarin, even the release file size of ‘Hello World’ app in this framework is around 6.7MB.

2. Coding Done in Dart

There’s no denying the fact that Dart is easy to learn and faster to implement. However, it is not yet as popular as its peers – Java, C#, JavaScript, and C. This prevents flutter app builder from enrolling the best dart programming courses and lands into the Flutter industry.

3. Lack of Third-party Libraries

Third-party libraries and packages have a critical effect on software development as it empowers a few highlights for designers. These outsider libraries are regularly free, open-source, pre-tested, and easily accessible. You may not track down each and every component you need for the development for the time being.

Since Flutter programming language is new for mobile application development, it is difficult to track down free packages and libraries. The device is still at the developing stage and is improving. Thus, you should either wait for some more time or pick an alternative for the long term development.

4. Issues with iOS

This is the reason why flutter app developers are stressed over its implementation for iOS. Since Google is directly keen on fixing bugs in the short measure of time, building Android applications on Flutter is quick and pleasant.

Probably the most recent update in Flutter programming language is a pixel-perfect iOS appearance. iPhone settings were developed on the framework to empower the Cupertino widgets. But based on iOS 10 and iOS 11, features were refreshed later and delivered for some time.

5. Lack of Password Manager Support

Utilizing your own, non-native widgets has its disadvantages. For instance, there are issues utilizing certain Android and iOS features. Apps built with Flutter right now have issues with autocomplete input fields. This is particularly observable in the event that you need to implement security (password) extraction from the built-in or third party administrators.

Who’s the Winner of the Cross-Platform Development Battle?

Both Kotlin multiplatform and Flutter are reliable, aim to reduce time to make a presence on both Android and iOS platforms, and are supported by Google. They are giving cut-throat competition to each other and are becoming more competitive with every release. 

In such a scenario, it is advisable to consult with mobility experts and analyze your cross-platform app requirements and priorities to determine which one is better – Kotlin vs Flutter.

The post Kotlin Vs Flutter: Who Will Rule the Cross-platform App Market? appeared first on Appinventiv.

As quoted by Francis Bacon, meaning, ‘One man’s misery or misfortune is the fortune of another.’

This saying has become true for the JVM world. With the fall of Java – the programming language that entered the market in 1995 and changed the complete story, various other languages are gaining huge momentum. Developers have not only started using those JVM programming languages, but have also proposed them as the right Java’s alternative for app development.

Three of such programming languages that are enjoying limelight in the market are Kotlin, Clojure, and Scala. 

While we will talk about Clojure some other day, let’s focus on the ‘Scala vs Kotlin comparison’ battle today. 

Here, in this article, you will get to know the scenario of Kotlin vs Scala 2021 to help you make a smart and profitable decision of switching from Java to another JVM language on your own.

TABLE OF CONTENT

1. What is Kotlin Programming Language?
2. What is Scala Programming Language
3. Kotlin versus Scala: A Detailed Comparison of Java Alternatives
4. Pros and Cons of Kotlin and Scala 
5. What Is The Best JVM Language?
6. Is Kotlin Better Than Scala?

So, Let’s dive into the Kotlin world first.

What is Kotlin Programming Language?

Kotlin is an open-source, cross-platform, statically-typed programming language developed by JetBrains in 2011. It was recognized as an official Android programming language by Google back in 2019 and has reached the latest version, Kotlin 1.4. 

[As we have mentioned about cross-platform nature of Kotlin above, you can learn about it in detail here –  Kotlin Vs Flutter: Who Will Rule the Cross-platform App Market? ]

What is Scala Programming Language?

Scala is an abbreviation of “Scalable”: a language i.e. scalable as per the user requirement.

Scala is a modern compiler based programming language that is designed to express normal patterns of programming in a brief, sophisticated, and type-safe way. It flawlessly supports both the features of object-oriented and functional languages.

With this covered, let’s gear up for the detailed battle of Supremacy between Kotlin and Scala and know how mobile app development companies are benefitting from these two languages.

Kotlin versus Scala: A Detailed Comparison of Java Alternatives

1. Market Popularity

Kotlin, with support of Google, has achieved success in making its presence dominant  in the market much faster than expected. As per Google trends, there’s a surge in searches related to Kotlin when compared to Scala. This gives a clear indication that Kotlin is more in trend than Scala.

But, if you focus entirely on the job posting platform, you will come across the fact that Kotlin is behind Scala with significant numbers.

Likewise, if you check for the developers’ salary for distinguishing Kotlin or Scala, you will realize that the values are much uniform. 

This, as a whole, concludes that both the languages are far popular and liked by the developer community in one way or the other.

2. Ease of Learning

Scala is a powerful language with highly-advantageous features and flexible syntax. So, it is quite difficult for newbies to get their hands on this JVM programming language.

But, it is not in the case of Kotlin.

As reported by Java developers in various organizations, one can easily understand basic Kotlin syntax in just a few hours and start coding within 1-3 days. Also, Google introduced free Kotlin courses for Android developers, which again streamlines their journey in the Kotlin market.

So, the winner of Kotlin and Scala differences war is the former, if the entire focus is on ‘learning curve’.

3. Functional Programming

It’s true that both languages are known for their functional programming paradigm. But, when it comes to looking into Scala vs Kotlin comparison in terms of functional programming, the former wins. 

Scala is more swayed by functional programming languages like Haskell than Kotlin. It encourages the use of functional coding along with some additional features like pattern matching and currying. Besides, the attempt of possible functional programming is more considerable in the Scala environment. 

4. Pattern Matching

As already unveiled above, Scala offers better opportunities for pattern matching than Kotlin. Its match statement enables developers to match any type of data, including its own. Also, it shows full support for higher-end and macros types.

A result of which is that Scala is termed as “Java’s switch on steroids” and is considered as a good fit for Big Data processing tasks.

5. App Performance

Though app performance depends highly on the nature of the app, let’s consider an Android application for comparison of JVM languages.

Android development with Scala is a good option for employing highly scalable Android app development software services. Whereas, a Kotlin-based application will be easy to code and debug. As a consequence between Kotlin vs Scala performance, app performance will be higher in the case of Kotlin when compared to the other JVM language.

6. Inline

In the case of Scala, you are required to request the compiler to inline a method by annotating it with @inline.  For example:-

@inline final def f1(x: Int) = x

Here, the compiler has the choice to see if the function is actually inlined or not.

But, in the case of Kotlin, the compiler will automatically consider functions with ‘inline’ keyword as inline. It is the developers’ responsibility to be careful with manual inlining to prevent the risk of inlining duplicates. Something that makes developers favour Scala over Kotlin for using inlines.

7. Null Safety Management Efficiency

One of the prime reasons why developers are showing an interest in other JVM languages is that they offer a better facility to handle hate-bound NPEs (Null Pointer Exceptions). So, it is again the main factor to consider while deciding the right Java alternative.

When comparing Kotlin with Scala, the latter’s approach is quite incompetent. One has to introduce null values with the option in Scala programming, which further adds to code complexity. 

So, clearly, the winner of the battle here is Kotlin.

8. Operator Overloading

Though Java does not support Operator overloading, both Scala and Kotlin come loaded with this functionality. So, it is again important to consider this factor to know whether to choose Scala or Kotlin for JVM-based development needs.

Kotlin focuses on making a balance between power and readability. It empowers developers to overload the basic operators, but refrain them from defining new ones. This makes it easier for one to understand Kotlin operators (even to novice developers), but reduces the flexibility.

Scala, in contrast to Kotlin, goes with quite a lenient approach to operator overloading. This makes it easier for developers to write more human-readable codes, and thus, makes it a good fit while creating internal domain-specific languages. But, at the same time, it can result in confusion if used incorrectly.

9. Java Interoperability

Though both Scala and Kotlin are interoperable with Java, Kotlin leads the show if you wish to maintain full compatibility with existing Java-based projects and technologies.

Kotlin is designed to be 100% interoperable with Java. So, you can easily call Kotlin code from Java and vice-versa effortlessly. Likewise, you can employ existing Java-based frameworks like Java Server Faces (JSF),  and ‘Vert.x’ Kotlin framework while considering Kotlin for server-side development.

Whereas, when it comes to Scala use cases then, accessing Java classes in the development environment is easy. But, it is not possible to call Scala classes in the Java environment, especially when working with advanced features of Scala as they might lack Java equivalent.

10. Libraries and Frameworks

When talking about libraries and frameworks, both JVM languages are doing wonders in the market. 

On one hand, Scala – taking advantage of its long span in the market – facilitates developers with a wide range of libraries and scala frameworks to work effortlessly with APIs and applications. Scala libraries include Cats, Slick, Shapeless, Akka, and Play Framework.

While, on the other side, Kotlin has also come up with a lot of offerings in terms of community libraries & frameworks. A few of the Kotlin libraries are Ktor, Exposed, and Arrow.

11. Documentation

When talking about Scala, its API documentation is simple, straightforward, and well-structured. It offers search functionality and an option to restrict the number of displayed elements with a filter. However, sometimes you might find it  tough to search for pages on a particular topic. 

Whereas, using the Kotlin API documentation is a bit tiresome. There’s no search function or filter option. However, it comes with a reference which explains all the concepts in a detailed manner (with examples). This helps Kotlin remain in the competition to Scala in terms of documentation. 

12. Use Cases

Though both Scala and Kotlin prove to be good alternatives to Java in the JVM world, they come with different unique use cases.

Besides Android development with Kotlin, web development and server-side development is also a few of the Kotlin use cases. Scala is a good fit for projects requiring a blend of OOPs and functional programming concepts, for Big Data-based solutions, or for operating with complex Machine learning patterns.

13. Community Support

Being in the market for the past 15 years, Scala has a bigger community than that of Kotlin. This makes it easier for app developers to find tutorials, solutions, and other resources related to Scala over Kotlin.

In fact, if you check in Q&A site Stack Overflow, you’ll find more than 4K questions related to Kotlin and 70K tagged with Scala. 

So, when focusing on community support, Scala beats Kotlin in the JVM war.

The language offers developers with ample benefits as an answer to Is Kotlin better than Java. With kotlin pros and cons, you will understand the above points in a better way. A few of which are as follows:-

Pros and Cons of Kotlin and Scala 

Benefits of Kotlin Programming Language

1. More Reliable

Unlike other conventional languages like Swift, Kotlin has undergone various Alpha and Beta stages before entering into the market. Because of this, the latest version of Kotlin is highly mature, compatible and reliable to operate with.

2. Higher Development Speed

Kotlin encourages developers to perform any functionality by writing a less number of code lines. This not only enhances productivity but also speeds up the app development process.

3. Fail-fast System

Another advantage of going with Kotlin language is that it provides a built-in fail-fast system which reduces the risk of errors in coding. 

4. Easy to Maintain

The Kotlin benefits offer extensive support for various IDEs including Android Studio. This makes it possible for developers to build solutions on different platforms and maintain them timely.

Because of such advantages, Kotlin programming language has received support from various recognised brands and organizations are adopting android app development companies. A list of which is as follows:

Limitations of Kotlin Language

1. Less Kotlin Developer Experts Available

Despite the sky-high popularity of Kotlin, there are only a few programmers with expertise in Kotlin development in the market. This is the prime limitation of considering Kotlin for JVM based application development.

Now, before you find yourself inclined towards Kotlin language, let’s jump to Scala.

2. An Overview of Scala Programming Language

Designed by Martin Odersky back in 2004, Scala is an open-source language that facilitates a perfect blend of object-oriented and functional coding in a statically typed programming environment. The language was devised to address the cons of Java programming language in terms of general-purpose and thus, can be an ideal Java alternative.

Scala programming language, whose latest version is 2.13.1, is highly appreciated by various reputed brands, including:-

And the prime reason behind this is the following set of benefits it provides. Let’s look into scala pros and cons to understand it.

Scala Advantages

1. Statically Typed

Scala comes with a highly expressive typing system that demands application of statistical abstraction in a particularly consistent and secure manner. Primarily, the type system supports:

• General Classes
• Upper and Lower typing limit
• Clearly written self-references
• Polymorphic methods
• As a member of internal class and abstract type items
• Variable Annotations

2. Concise Coding

One of the prime reasons why Scala is gaining a huge momentum these days is because it is highly concise. The language, when compared to other programming languages like Java, requires less number of code lines to perform the same activity. 

3. Highly Scalable

Another advantage of Scala-based development is that it is highly scalable. Developers can merge different paradigms and introduce a vast range of features into the development environment while working with this JVM language.

4. Typesafed Language

Scala emerges as a good resource in terms of type safety and this is another Scala advantage. Besides, it enables you to create your own data types on the top of primitive ones and write a DSL for your project.

5. Enhanced Quality

Since a minimum number of code lines are written in the Scala environment, it becomes easier for developers to deliver a bug-free experience, a.k.a, get better results from the Quality assurance process.

6. Multi-Platform Presence

Though Scala primarily targets JVM platforms, it can be a good option for creating software that runs on multiple platforms. 

Scala Disadvantages

1. Difficult to Understand

Since Scala codes are an amalgamation of functional and object-oriented concepts, the information sometimes gets a bit hard to comprehend.

2. Slow Compilation Speed

Another disadvantage of Scala is that it takes a longer time to compile complex codes. This results in slow compilation speed when compared to what is observed in the case of Java or Kotlin.

3. Issues with Binary Compilation

It is not binary compatible with a few versions. For example, a code that is compiled with Scala 2.1 might not compile with Scala 2.11. This is yet another limitation of picking Scala language.

With this, you have gained all-inclusive information about the two JVM titans. Which implies you might be curious to hop over to the differences between Kotlin and Scala to see which one wins the JVM war.

But, wait.

Before we move on to the section where we compare Scala and Kotlin, let’s have a quick look at how Kotlin’s an improvement over Scala, and vice-versa.

What Is The Best JVM Language? 

With the benefits of JVM, there is not just one but many best JVM languages. Some popular ones are:

• Scala
• Kotin
• Clojure
• Ceylon
• Fantom

Is Kotlin Better Than Scala?

As noted from the aforementioned factors, Kotlin and Scala are somewhat in a tug of war. While Kotlin is an improvement over Scala in some ways, it lags behind the latter in others. This implies, there’s no end to the ‘Kotlin versus Scala’ debate. 

Now, in such a scenario, the optimal way to decide what to choose between Kotlin & Scala instead of Java is to consult with a trusted app development agency. So, connect with experienced android app developers in USA, brainstorm on your app idea, and determine what’s an ideal choice for you. And ultimately, start coding using that JVM programming language or hire a skilled android app development company in USA.

The post Kotlin vs Scala: Choose Right JVM Language for App Developers appeared first on Appinventiv.

In this article, we will be taking an in-depth look into two such popular systems and look into how they stack up against each other: HBase vs Cassandra.

We will be exploring the essentials, architecture, performance, amongst other things. 

• What is HBase?
• What is Apache Cassandra?
• The Similarities Between HBase and Cassandra
• HBase vs Cassandra: The Differentiating Factors
• When to Use Which Database

Let us start with the overviews first. 

What is HBase?

HBase is a distributed, scalable, column-based database with dynamic diagram for structured data. It enables efficient and reliable management of large data sets which are distributed among multiple servers. 

HBase Architecture & Structure 

It is a one of a kind database which works on multiple physical servers simultaneously, which ensures a smooth operation even though the servers are not operating together. HBase data model uses two primary processes for ensuring ongoing operations:

A.  Region Server – It can support various regions. The region here stands for record array that corresponds to a specific range of consecutive RowKey. Every RowKey contains these elements –

• Persistent Storage – It is a permanent storage data location in HBase. The files are used in the HDFS storage in HFile format. The RowKey sorts this data type and divides them in pairs, where each pair aligns with one HFile. 
• MemStore – It is a write buffer in which anything written to the HBase gets stored. When the MemStore reaches a specific size, the data gets written in a new HFile. 
• BlockCache – It is a read cache which enables you to save time on the data which is frequently read. 
• WAL – When the data is written into memstore, there is always a risk of losing it. WAL (Write Ahead Log) saves all the operations prior to its implementation. This way, the data can be recovered if something happens.

B.  Master Server – It is the primary server of Apahe HBase. It manages regions distribution across Region Server, monitors regions, manages running of the ongoing tasks and performs a series of other necessary tasks.

To coordinate the action between services, it uses Apache ZooKeeper – a service for configuration and service sync management. 

What is Apache Cassandra?

Cassandra belongs to the NoSQL-system class and is designed for creating reliable, scalable repositories of data arrays which are represented as hash. It works with key space, which aligns with the concept of database scheme in relational model. There can also be multiple column families that relate to the concept of relational table.

To know more about the top databases and how they are popular among web developers you can also check here. 

Apache Cassandra Architecture 

The idea behind the Cassandra architecture is to have a P2P distributed system which is made of nodes cluster in which a node can accept the read or write requests. Every node in the cluster communicates the state information about itself and the other nodes through P2P gossip communication protocol. This together forms the basis of Cassandra data modeling and analysis. 

At the center of the Apache Cassandra data model lies a Log Structured Merge storage engine. It comes with key elements like:

• Memtable
• Commit log
• SSTables
• Compaction 

The overview of both HBase database management system and Cassandra must have given you an idea of how similar the features of HBase and cassandra can be.  

The Similarities Between HBase and Cassandra

1.  Database 

Both HBase and Cassandra are NoSQL open-source databases (like Aerospike database). Both of them can handle large data sets and non-relational data, which includes images, audio, videos, etc.

2.  Scalability 

Both HBase and Cassandra have a high linear scalability feature. Under the feature, users who want to handle more data only need to increase the nodes number in cluster. This makes them both equally good choices for handling huge data.

3.  Replication 

In case of both HBase and Cassandra, there is a safeguard which prevents the loss of data even after it fails. This is done through the mode of replication. The data which is written on one node gets replicated on multiple nodes in a cluster. Because of this, if a node fails, a redundant node is always present for accessing data. 

4.  Coding

Both the databases are column-oriented which implement similar write paths. Columns are mainly the center storage unit in a database. Users can add columns according to their requirements. Additionally, the right path starts with logging a write operation to log file. It is basically done for ensuring durability. 

Now that we have looked into what makes them similar, let us shift our attention to the difference between HBase and Cassandra. 

HBase vs Cassandra: The Differentiating Factors

1.  Data Models

While the terms of both the databases are more or less, there are some fundamental difference between HBase and Cassandra. 

The column in Cassandra is like HBase’s cell. Its column family is also more like HBase table. On the other hand, HBase column qualifier is a lot like Cassandra’s super column. 

One of the Cassandra key characteristics is that it only allows for a primary key to have multiple columns and HBase only comes with 1 column row keys and puts the responsibility of the row key design on the developers. Also, Cassandra’s primary key contains partition key and the clustering columns in which the partition key might contain different columns. 

2.  Architecture

HBase has a master-based architecture while Cassandra has a masterless one. It means that HBase comes with a single failure point, while Cassandra does not. The HBase client communicates directly with slave-server without contacting master, this gives a working time once the master is down. 

Moreover, in the Cassandra and HBase comparison, the former supports both data storage and management, while in case of the latter, the architecture is only designed for data management while it relies on other systems/technologies for storage, server status management, and metadata. 

3.  Performance – Read & Write Capability

When the comparison is drawn between Apache Cassandra performance and Apache HBase performance, it is done on the front of read and write capability. 

Write: Both HBase and Cassandra’s on-server write paths are fairly alike. There are some differences though which makes Cassandra better, like the difference in names for the data structure and the fact that HBase does not write to log and then cache simultaneously. 

Read: If you are looking for consistent and fast reads, you should go with HBase. Since it writes on only one server, there is never the need of comparison between the various nodes’ data versions. 

Even though Cassandra can handle over 129,000 reads in one second, the reads are targeted and there are high probability of them being inconsistent. 

4.  Security 

Both HBase and Cassandra offer not only database-wide access control but also granualty of a certain level. Cassandra allows row-level access while HBase goes a step ahead and offers cell-level access. Cassandra sets the users roles and their condition, while HBase comes with an inverse move in which the administrators assign visibility label to the data sets and then informs user groups which labels they can view. 

5.  Infrastructure 

HBase makes use of the Hadoop infrastructure which consists of moving parts such as HBase master, Zookeeper, Name and Data nodes. 

Cassandra comes with several different operations and infrastructure. It also uses different DBMS in addition to the infrastructure. A number of Cassandra apps also use Storm or Hadoop. Additionally, its infrastructure is based on a single node type structure. 

6.  Support 

The support specific Cassandra and HBase comparison looks like this – HBase doesn’t support the ordered partitioning, while Cassandra does. Ordered partitioning leads to making the row size in Cassandra to 10s of megabytes.   

7.  Nodes 

In the case of Cassandra, the users have to identify nodes as seed nodes. These serve as the points for inter-cluster communications. In the case of HBase there are several master nodes. They monitor and coordinate actions of region servers. 

8.  Internode Communication 

Both HBase and Cassandra have internode communication. While Cassandra uses the Gossip Protocol, HBase uses Zookeeper Protocol where a single node acts as boss through with the other nodes gets the necessary data. 

9.  Transactions 

When it comes to HBase vs Cassandra comparison in terms of transactions, Cassandra comes with the feature of lightweight transactions. The mechanisms used here are Row-Level Write Isolation and Compare and Set. While, on the other hand, HBase works with two different mechanisms known as Check and Put and Read Check Delete. 

10.  Documentation 

Cassandra’s documentation is a lot better than HBase’s documentation. Because of this, working on and learning Cassandra also becomes easier. 

11.  Query Language 

Both HBase and Cassandra shell are based on the JRuby shell. Cassandra query language, is very specific. It is CQL (which is modeled in the line of SQL). Compared to HBase query language, the functions and features of CQL are far more rich. 

The differences between HBase and Cassandra shows that there is no concrete answer to which database is better of the two. It all boils down to when to use which. 

When to Use Which Database 

The Cassandra and HBase use cases can be differentiated on the grounds of application type they are used in and the outcome expectation that an app development company has. 

Use HBase if you need consistency in the large scale reads and if you work with a lot of batch processing and MapReduce for it has a direct relation with the HDFS. 

HBase’s use cases consist of online log analytics, write-heavy applications, and apps that need a large volume, such as Facebook posts, Tweets, etc. Additionally, there is a large set of use cases related to Cassandra Hadoop integration.

Use Cassandra if high availability of large scale reads are needed. Also, since it requires a very minimum setup with less administration overhead it is a lot easier to get the process started in. It also offers greater flexibility in CAP theorem tradeoffs. 

Some examples of what is Cassandra used for can be seen in the development of messaging systems, e-commerce websites, and real-time sensor data.

In short, use HBase data model and implementations when you have to analyze for big data or have to perform aggregations. Use Cassandra if you have to emphasize on interactive data and real-time transaction processing.

The post HBase vs Cassandra: Which is Better of the Two NoSQL Databases? appeared first on Appinventiv.

And, if you had to sit down and think of the programming languages which were best aligned with the motive to develop secure, microservice favoring frameworks and custom web application development services, you would again find yourself staring at the two languages.  

Even after being similar in some prominent ways like maturity, being open source, and being designed for microservice oriented, modern, parallel computing environments, there is a lot of confusion around Go vs Rust and which of the two languages are good for the developer community to enter into. 

The intent of this article today is to look into the difference between Go and Rust in much detail. The differences that go beyond Rust use cases and Golang use cases and answers questions related to- What is Golang good for? How to get rust to run better? Why use Golang? How to get rust to load faster? Why use go programming language? When to use Rust language? When to use Go programming language? And much more.

Let us answer Which is better?

Which Is Better Go Or Rust?

 Go vs Rust: Performance 

When asked how to run rust better or how to get rust to run better, the one factor that weighs up Rust in the Golang vs Rust debate is performance. The programs have been designed to run at similar or near similar speed of C++ and C. 

Go, on the other hand, trade runtime speed for convenience, by making tasks automatic. But in terms of Rust vs Golang Performance comparison, development speed of Go falls several steps behind the high performance that Rust offers. 

Go vs Rust: Memory Management 

In Rust vs Go performance, Rust performance make use of compile time ownership strategy for memory management through zero cost abstractions. If a Rust code is not memory safe, it won’t be able to cross the compilation stage. 

Like Rust, Go is also memory safe. But in the case of Go, it is handled automatically during the runtime. Meaning, developers won’t have to think of releasing or allocating memory when writing rust code. 

Go vs Rust: Development Speed

At times, the development speed becomes more important than the program speed. A working example of this can be seen in the case of Python, which is not one of the fastest languages to run but fastest to write a software in. 

In Rust vs Go speed, Go comes with the same appeal. The simplicity and directness that it offers makes it faster than a number of languages in the market. 

Rust, on the other hand, comes with more language features and thus takes longer to master. Its compile speed is also longer than Go’s. So, if the intent is of faster full cycle app development services, you would be better off with Go than Rust. 

Golang vs Rust: Concurrency

Modern day apps are distributed and networked. The languages that haven’t planned for these realities are far off behind the curve. The solution to the query how to run rust better and go faster is on the developers. The developers have to be able to run the tasks independently and share state between the tasks minus the risk of data corruption. 

Now while concurrency was built into Golang’s syntax from beginning, Rust recently gained the native syntax in the form of async/.await. But even though the concurrency of Rust lacks the developer experience that went behind Go, it makes up for it through Rust’s memory safety. 

After getting to know the part where we look into how they stack up against each other, let us now refresh the basics of both the programming languages. 

Rust Programming Language

Rust language came into existence back in 2010. Deemed as one of the ML languages’ extensions, the aim that the language was expected to fulfill was better security, rust performance, improved parallelism, and greater modularity. 

The Rust experience system and feature set that Rust comes with has brought it to a stage where there are very few developers who haven’t heard of it or as showing an unwillingness to work in it. 

Advantage of Rust language:

• Amazing run speed 
• Rust dramatically increase performance
• Enables fine grain features 
• Interoperable with C, and FFI 
• Zero-cost abstraction
• Predictable runtime behaviour 
• Saves crashing, debugging time. 

Disadvantage of Rust language:

• Learning Rust is steeper than Go 
• Between Go vs Rust, it is slow to compile 
• It is slower than the C and C++ language in the analogous conditions. 

What Is Rust Best Used For?

Rust is the sort of programming language that one can utilize almost everywhere. In any case, a few spots are better qualified for it than others.

For instance, in light of its high quality Rust performance and zero-cost deliberations, framework programming is one field where rust dramatically increase performance and flourishes. Frameworks programmers are a type of engineer who works intently on the hardware side of improvement.

Since Rust programming language takes some motivation from C++, it’s not astounding that the language would have some center level abilities. Middle level languages are the deft and opportunistic trade-off between low-level and high-level languages.

They’re frequently used to make working frameworks or computer applications as they can deal with the degree of intricacy it takes to oversee memory on the equipment side of things. But, regardless of the absence of thought, they’re as yet readable by the human eye, which is uncommon to discover in machine code.

In spite of the fact that there is some dispute in the developer community regarding what considers as a center level language or not, Rust is perceived for being an advanced language with machine-oriented possibilities.

Go Programming Language Review

Go coding language was introduced back in 2007 by Google. It was looked at as a language that had emerged to solve the issues that organizations face when developing software infrastructure. The Go language specification was devised to introduce garbage collection, dependency management, built-in concurrency, robustness across multiple boundaries between the components, etc. 

To answer the question, why use golang, let’s read further. The result of the well thought of features of Go has gotten it a market position which is enough to bring it several points ahead in the Go vs Rust debate. 

Advantage of Go Language:

• The best feature of Go language is the incredible speed at which it compiles to machine code. 
• The simplicity of code
• Concise, pragmatic, and efficient
• Flexible and highly concurrency favoring 
• Strong interoperability with C language. 

Disadvantage of Go Language:

• Keeps simplicity over Golang performance 
• Lacks the generics to an extent
• Not enough immutability 
• Not a system language. 

Why Should You Use Go And When To Use Golang?

Comprehensive 

If you are thinking about what is golang good for, then read further as the Go language is fully comprehensive and ready to rock with its tools to its libraries to its documentation. But jokes apart, Go is equipped with broad documentation, convenient tooling, and minimal dependencies. 

GoDoc – a Go package that obtains and produces documentation while you code – merits an exceptional shoutout.

Quick

A significant number of the features add to Golang performance that explains our doubts on why use Golang? Specifically speed that is conceded through garbage assortment, quick compilation, and simultaneousness in addition to other things is likely the Go’s characterizing feature.

Benchmarks show that Go can perform as much as multiple times quicker than Python in certain spaces. Go is regularly quicker than Java also.

Portable

Go programming language review is that it can be arranged pleasantly and flawlessly on multiple platforms. No more catching over what working framework to run due to compatibility issues.

FAQs About Go vs Rust

1. Why use Golang and not Rust?

The answer to this would be in when and not why. You should go with Golang when – you have to write code faster when simplicity is a bit more important than performance, and when readability is a must have necessity.

2. What does Go and Rust have in common?

There are some prevalent similarities between Go web development and Rust in Go vs Rust web development, mostly in terms of the maturity and the fact that both: the programming features of Rust and Go are open source and have been designed for microservice oriented, modern, parallel computing environments.

3. Is Go faster than Rust?

Yes. Speed is one of the biggest differences between Rust vs Golang – a difference which is won by Go.

Conclusion

We could keep digging into both the languages, dissecting both sides of Go vs Rust web development, but the ultimate answer to the Rust and Go comparison would be the fact that it depends. There are instances where Rust is better than Go due to the rust experience system and vice versa. 

Ultimately, this is what we recommend as a web application development company, who works on both why use golang and rust – 

Choose Rust when:

1. If rust web server performance is a necessity 
2. If you want it to interoperate with the C language. 
3. When your developers need control on every detail of how the threads behave with the rest of the system, how the errors must be handled, it would be better to look into Rust web server performance use cases.  

Choose Go when:

1. If readability is a must have requirement 
2. If you wish to write codes faster
3. If the focus is on uniformity and simplicity. 

If your doubts are clear regarding the Rust vs Go performance then you might need assistance  for your web app development and for understanding the future of Rust programming language or rust experience system you might need to consider Appinventiv as a reliable and trustworthy web development company in USA. We at appinventiv have expertise in building world class Android apps and have developed a number of apps that have created new milestones.

FAQs About Go vs Rust

Q. Why Go and not Rust?

The answer to this would be in when and not why. You should go with Golang when – you have to write code faster, when simplicity is a bit more important than performance, and when readability is a must have necessity. 

Q. What does Go and Rust have in common?

There are some prevalent similarities between Go web development and Rust in Go vs Rust web development, mostly in terms of the maturity and the fact that both: the programming features of Rust and Go are open source and have been designed for microservice oriented, modern, parallel computing environments.  

Q. Is Go faster than Rust?

Yes. Speed is one of the biggest differences between Go and Rust – a difference which is won by Go.

The post Go vs Rust: Which is Better and Why? appeared first on Appinventiv.

Sometimes good things are an accident – Electricity, Discovery of America, Gravity, and PHP.

Wait, what?

Actually, PHP was never intentionally designed. Rasmus Lerdorf, a Danish software engineer attempted to design an easy common gateway interface by using the C language that would help him track views on his CV. Little did he know what he had created for such a menial task is going to change the face of web applications.

As for Python, the language has its own fan following spread worldwide.

What is there to see is how these two fare when put under juxtaposition. Yes! We are talking about the popular battle of PHP vs Python.

So, why don’t we begin the first round by discussing what they are, individually?

What is Python?

Well, of course, it’s not a snake (pun intended). 

For real, Python is one of the few best web programming languages for web development that is winning the hearts of many around the world. It is a non-complex object-oriented, high-level, interpreted language, possessing amazing built-in data structures connected with dynamic typing and binding. It is features such as these that make developers choose this language for efficient and effective app development.

What is more, it is a scripting language that is proficient in binding and combining numerous components together. To add another jewel to its crown, it favors cross-platform development, making it ideal for web and mobile app development.

In fact, Python had recently won second place as the most popular language on Github, beating Java to dust and is one of the most loved languages.

Features of Python programming language

Fast in nature

There is no compiling in Python, for the programs are added to the interpreter which runs them directly. This is something that happens for the rest of the programming languages. It is very easy and quick to get access to the feedback on your Python code like recognizing errors, etc. Due to reasons like this, Python has the ability to execute and finish the programs speedily in comparison to other languages.

Supports test-driven development

With Python app development, coding and testing go hand-in-hand, something that is a tried and tested way to the efficacious development process. Developers are able to develop prototypes of the applications as Python allows them to refactor the code.

Amazing standard library

Another probable feature of Python programming language that enabled it to beat Java on Github, is its collection of impeccable libraries. Python’s robust standard library supports the programmers in choosing modules as per the requirements demand. Here every module allows developers to add other multiple functionalities even when the process is about to complete and that too without any additional coding. 

{Bonus- Java vs Python: Who is Winning the Coding Battle?}

Support for Big Data

If you have been looking forward to working on the Big Data technology but aren’t sure what should be in your tool set, then trust me, Python will be all you need to start with. This programming language is being used extensively in Big Data development; simply because it is faster and has a lot of libraries in store, that are in harmony with Big Data.

Highly compatible

Python is the first choice of developers who want to do away with the recompilation process. It is because along with supporting numerous OS like iOS, Android, Windows, etc., it allows developers to use Python interpreters to run the same code on all platforms and even modify it without performing recompilation. And on top of it, it allows programmers to check the code almost instantly when the changes are made.

Advantages of Python 

• Object-oriented and cross-platform programming language
• Portable in nature due to WORA functionality
• Highly readable, making it easy for beginners
• It is open-source, a feature which makes it easily accessible
• It is a constantly evolving language

Disadvantages of Python 

• Slightly in-effective in mobile computing and browsers
• Design restrictions: Duck-typing may cause run-time errors
• Delayed testing of web applications
• A little too simple for certain tasks

Popular apps made with Python

Being a trusted programming language, there are many popular use cases of Python you must be knowing already.

What is PHP?

Speaking of the name itself, it has a little kick to it. The acronym does not fully describes its full form which is Hypertext Preprocessor. It is also an open-source scripting language (means a script-based program) that is server-side in nature facilitating development for every web app development company.

Additionally, it is possible to embed PHP in HTML to create dynamic web pages for web applications, database apps, and E-Commerce apps. In fact, developers consider it to be a pretty friendly language as it can connect with databases like Oracle, MySQL, etc. 

Features of PHP

Highly flexible and scalable

To be successful, a language should be flexible and scalable enough to give a wiggle room to programmers to have fun, something which PHP clearly offers. It can be easily integrated with other programming languages, namely, Java and others. The components built with this language are reusable.

There are many extensions available to enable different functionalities, that help in defining many kinds of project requirements. 

Fast loading speed

Speed plays a crucial role in making any programming language a favorite of developers. PHP is considered to be a high-speed rendering language as it easily establishes a connection with the database to fetch the requested data in less time as compared to many languages out there in the market.

Supports Cross-platform development

PHP is no way behind Python in catering multiple platforms, i.e, it is also a cross-platform language that helps developers in creating web applications for operating systems such as UNIX, LINUX, Windows, and more. What is more, it also supports Apache and MySQL database. 

Loosely typed language

PHP is denoted as a loosely typed language as it supports variable usage without even declaring its data type. It will be taken at the time of the execution based on the kind of data it has on its value.

Error Reporting

One of the amazing features of PHP is that it has a function that generates a warning notice when something is wrong, allowing the developers to address the issues in real-time.

Advantages of PHP

• Has an enormous ecosystem
• Object-oriented and open-source language with an active community
• Interoperability- has numerous pluggable frameworks
• Supports first-class debugging

Disadvantages of PHP

• Threaded and slower execution
• No Internet of Things community support
• Limited visibility and control
• Externally dependent 

Popular apps made with PHP

Python vs PHP: Battle begins

Now, let’s see how Python Compares to PHP and vice versa.

1. Performance and speed

Speed is often synonymous with great performance in web applications. And in the case of PHP vs Python performance for web applications, it is no different.  It is crucial for web apps to perform error-free while processing millions of requests at a time. PHP is considered to be much more effective at speed. Though PHP 5.x was fast, PHP 7.x broke its own records, leaving average Python programs to dust. It completes the tasks almost 3 times faster.

2. Syntax and code readability

Syntax plays a prominent part as a point of difference between PHP and Python. To be brief, the syntax of PHP kind of resembles that of the C language. The programmers have to be careful and need to use curly brackets, operators, and additional characters. What is more, developers can also ignore the white spaces while the compilation is on-going, bringing the additional usability that will assist in structuring the code into logical components.

As of Python, the syntax is made on the separation of codes with spaces and tabs, expediting the process of coding to a great extent. Moreover, Python’s syntax is readable which makes it easy for beginners to learn it effectively.

3. Price

It is a good thing that both languages are open-source which means nothing but that they are free to use. In fact, developers have advocated that these even though free in nature, maybe better than some of the paid frameworks.

4. Library Support

What gives Python an edge in the battle of PHP vs Python 2020 is that it has exceptionally extensive and well-developed library support for all types of applications developed using Python. Some of the most preferred libraries that developers use in Machine learning development along with others are TensorFlow, Theano, Scikit, etc. 

Though PHP is lagging behind, it also has packages repository “Packagist” that is pretty popular among the developers. 

5. Web frameworks

Both PHP and Python have loads of responsive and incredible web frameworks to start with. For Python-based web frameworks, some that stand out the most are Django, Bottle, CherryPy, Pyramid, and more. 

As for PHP-based web frameworks, we have Laravel, Codeigniter, Zend, Symphony, and so on. As both Python and PHP programming languages provide a pretty good variety of choices to the developers, beginners are still a little bit more inclined towards Python’s Django framework because it is mighty fast.

{Bonus Read: Django vs Ruby on Rails}

6. Debugging

To give you the verdict on this point of comparison of PHP and Python before even diving deep into it, we have to say that it is a draw. 

You ask why? Well, if Python has “Python Debugger” (PDB), a well-documented and facile debugger, then PHP has its “XDebug” package; both offering the most basic debug features – stacks, path mapping, breakpoints, and more. 

7. Front-end feasibility

Web programmers also look forward to developing the layout or the front-end of their websites and portals using numerous effective tools along with languages like Bootstrap, CS, Angular, etc. Python here provides numerous opportunities for the developers to adopt the same techniques and later combine it with Python code by employing frameworks.

This way, designers can easily design websites, whereas the backend developers find it easy to use via a much subtle and understandable language like Python. So, definitely, Python here wins over the PHP. 

8. Usability

It is prominent to consider the architecture of the languages before we conjecture anything in favor of either language. 

The fact that both Python and PHP are object-oriented translate into that they compress code modules containing some functions and data in objects. So, any developer who is even a little bit familiar with object-oriented programming can use either of the languages.

9. Maturity

On the grounds of maturity, Python is inevitably the winner since it was first introduced in 1989 by Guido van Rossum and released in 1991. Its latest update 3.8.0 was released recently on 14 October 2019. It is the most mature and secure among the technologies which make the fundamental building blocks of the intricate mosaic that is the IT industry.

As for PHP, it was created in 1994 by Rasmus Lerdorf. Though not as mature as Python, it still has created a still in the market with its features. The very first manifestation of PHP was a simplistic set of Common Gateway Interface (CGI) binaries that were written in the C language. The latest version of PHP is 7.2.7. 

10. Package Management

Speaking of Python, its package managers are effective in organizing the code and keeping the backups along with the version numbers. It helps the developers track their progress while helping businesses perform regular updates on the activities.

As of PHP, though is package management, however, it doesn’t match up to the PIP, a tool to manage and install Python packages. It allows developers to install, upgrade, and even uninstall while using a wide range of sources for external as well as internal libraries.

11. Market Popularity

If you want to judge or compare two things, market popularity tops the list of points of comparison, because it indicates why developers and programmers love something. 

Looking at this graph above we can surmise that Python is the most searched on the web in the US as compared to PHP.

It has been observed that 7,212,664 websites have been developed using PHP, and as for Python, the number amounts to be 146,702. What is more, PHP is leading in countries like the US, Japan, Germany, and other 150+ countries. In a survey by w3techs.com, as you can see in the graph, it is evident that as of May 29, 2019, PHP was the most popular language based on traffic.

12. Versatility

Python in comparison to PHP is evidently versatile in nature as it is an ideal choice for developing websites integrated with Machine Learning and AI. It comes with an abundance of libraries that support these technologies and facilitate their integration into the web apps.

13. Well-thought-out design

While comparing PHP vs Python for backend, we found that Python, as compared to PHP, is fairly easy to grasp. Python has a well-thought-out-design (refers to the method used to insert code) than PHP. This means that developers find it easy to program for backend with Python than they do with PHP. It is simply because Python is a flexible yet robust programming language, something that PHP lacks.

14. Documentation

Well, it is a draw. Both languages have an abundance of documentation spread across the internet. Any beginner can easily find resources and documents related to any aspect of these languages and can get answers to even the most complicated queries- All thanks to their communities. 

15. Environment Management

Speaking of environment managers, Python is extremely lucky in this regard. There are some terrific breed applications to manage the environment. One such example is Virtualenv – a system that is used to install and use numerous versions of Python simultaneously and switch them as often as needed with ease.

Though PHP also has an analog for Virtualenv known as VirtPHP.  However, it is advised to use containers.

16. Learning Curve

Starting with PHP, being a straightforward language, it has a fairly low learning curve. It is ideal to learn PHP for short-term projects, but if you want to learn something that gives long-term benefits then Python is your match. 

Though on its own PHP is easy to learn, however in comparison to Python, PHP has a steep learning curve for Python is the most readable and easily comprehensible language. It is because the language uses a much simpler syntax.

17. Community Support

Both languages enjoy the support of active communities. It is a crucial aspect for a beginner to consider before committing oneself to a particular language, simply because the learning process is full of doubts and queries and having strong support resolves half the issues.

However, we have to admit that community support of PHP is outstanding, given how actively they have been deprecating the language’s outdated aspects.

18. Salary and Job opportunities

While considering the payscale of both languages, we found that Python developers, on average, make somewhere around $120,024 every year in the United States. Whereas, PHP developers make an average of $86,017 per year.

In fact, you can see in the image above that Python developers, globally make nearly $63k, whereas PHP did not even make it to the list accumulated by Stack Flow.

Final Thoughts

To end this discussion, we would state the most obvious – both languages are differently capable in all aspects. One may surpass the other in one aspect and may lose in others. They both have their own set of features and functions to perform. 

They are a paradox in themselves; there are many similarities between PHP and Python,  and at the same time are dissimilar as well in many aspects.

Nevertheless, Python has emerged to be the winner in most of the comparison points such as documentation, job opportunities, salary, etc. So, it is up to an individual which one they choose based on their requirements.

Frequently Asked Questions

Q. Is Python used for web development

Yes, Python is one of the most preferred programming languages for web development.

Q. Why to prefer python over PHP?

Python has been gaining popularity by the day and has managed to become one of the most in-demand languages. There are many features of Python that have impressed and helped developers and programmers across the globe, which might be the reason why they prefer it over PHP.

Q. Which is best Python or PHP?

Both of the programming languages are used by the developers and programmers for web development. They both have their pain points and advantages. So, the answer to the question “Which is better Python or PHP?” is subjective – depending on your project needs.

The post PHP vs Python: Which Language Is Dominating The Market? appeared first on Appinventiv.

But, are also entertaining all the originating buzzwords. While some are using them as synonyms, others are taking an interest in finding the differences between them.

One such newly originated name is Distributed Ledger Technology (DLT).

‘What is DLT’ has become one of the buzz questions of the current time, with what makes it similar/different from Blockchain being the close second 

Let’s cover both in this article. But first, let’s have a sneak peek of Blockchain by considering a platform example of both, i.e, Ethereum (Blockchain) and R3 Corda (DLT).

Now while this would have given you some insights on the difference between blockchain and distributed ledger, let’s jump into the decentralized world and study them in detail – starting with the basics of Blockchain technology.

A Simple Definition to Blockchain Technology

Blockchain is a decentralized, distributed and often public database type where data is saved in blocks, such that the hashcode present in any block is created using the data of the previous block. These blocks offer a complete set of characteristics like transparency, immutability, and scalability that makes every brand and developer interested in investing their time and effort into Blockchain development guide.

Advantages of Blockchain:

1. Blockchain technology enables businesses to verify any transaction without involving any intermediaries.
2. Since transactions stored in the blocks are stored on millions of devices participating in the Blockchain ecosystem, the risk of data recovery is minimal. 
3. As consensus protocols are used to verify every entry, there is no chance of double entry or fraud.
4. Another benefit of blockchain technology is that it offers transparency in the network, which makes it easier for anyone to be familiar with transactions in real-time. 

The technology came into limelight as backend support force for cryptocurrencies, but soon made its place in different business verticals including Healthcare, Travel, Real Estate, Retail, Finance, and On-demand. A complete information about different industries that Blockchain is disrupting can be taken from this image:-

With this attended to, let’s turn towards DLT.

A Brief Introduction to DLT

The answer to what is DLT technology and how distributed ledger technology works is that it is a digital system used for storing the transaction of assets, even when the data is stored at multiple places simultaneously. It might sound like a traditional database, but is different because of the fact that there is no centralized storage place or administration functionality. Meaning, every node of the ledger processes and validates every item, and this way, contributes to generating a record of each item and building a consensus on each item’s veracity.

A timeline of DLT’s movement can be seen in this image –

The concept is attracting almost every app development company due to the number of advantages of distributed ledger technology. 

Benefits of distributed ledger technology

1. The use of dlt ensures that the data is 100% tamper proof till the database ledger is distributed.
2. It offers a highly secure and trustworthy experience by distributed ledger technology companies.
3. A decentralized private distributed network enhances the robustness of the system and assures continuous operation without any interruption.

Now, as we have looked into what the two terms mean, let’s look into the DLT and Blockchain relationship before turning towards the comparison of two.

Relation Between Blockchain and DLT

As depicted in the above image, Blockchain is just a piece of the vast ecosystem of DLT. It is a type of DLT where records are stored in blocks after being validated cryptographically. That implies, a hash created using the data stored in a block is fed in the next block added such that it gives an impression of a chain.

“Every blockchain is a distributed ledger, but not every distributed ledger is a blockchain.”

Now, let’s move on to the core part of this article – DLT vs Blockchain comparison.

Factors to Consider While Comparing Blockchain and DLT

1. Consensus model

The foremost factor to focus upon while checking into DLT vs Blockchain is the consensus mechanism.

Since only a limited number of nodes participate in the case of DLT, there is no need for any consensus. But, the same is not true in the case of Blockchain where anyone can participate and contribute to the addition of a new block to the chain.

2. Block structure

Another factor that you must keep a watch on while comparing DLT technology vs blockchain is block structure.

While blocks are added in the form of a chain in a Blockchain, they can be organized in different forms in the case of DLT.

3. Tokens

Tokens, i.e, the programmable assets governed by Smart Contracts or underlying distributed ledger, is also one of the prime comparing factors in Blockchain vs distributed ledger war.

While tokens are a must to consider while working with Blockchain technology, they are not required when dealing with DLT. And the reason behind is that only invited (limited) nodes are allowed to participate and validate any transaction in the DLT environment which reduces the size of the complete ecosystem.

However, you might be requiring tokens when you wish to perform block spamming or work on an anti-spamming detection process.

4. Sequence

In the Blockchain environment, all the blocks are arranged in a particular sequence, i.e, in a serial mode. However, there is no such constraint when talking about DLT. Blocks are organized in different ways in the case of DLTs.

5. Efficiency

A DLT can complete a significantly higher number of transactions in a minute than what is possible by Blockchains. So, it delivers higher efficiency with minimal cost than Blockchain-based solutions.

An outcome of this is that today, various Blockchain development companies are looking ahead to enter into the DLT ecosystem.

6. Trustability

Another factor that you can consider to see the difference between Blockchain and distributed ledger is Trustability.

In the case of DLT, trust among participating nodes is high. And it is even higher when the corporate initiates building their own internal blockchain or organizing a consortium. Also, the censorship resistance in this template is low since they can be centralized and/or private.

However, it is not so in the case of Blockchain.

In the Blockchain ecosystem, the censorship resistance is too high with one vote per PC. But, with the progressive concentration of mining with upgraded hash power in the hands of fewer decision makers, the chances of trustability is low.

7. Proof of work

Another major difference between blockchain and DLT is proof of work. Distributed ledger is comparatively more scalable as it does not need proof of work. Blockchain, on the other hand, is a subset of distributed ledger but it has additional functionality beyond the traditional DLT’s scope.

8. Security

To access data stored in a blockchain, users have to employ a key. If they lose the key, they would lose access to their account and funds. A real-life example of which is the loss of access to nearly $145M of bitcoins and digital assets on the death of a cryptocurrency exchange CEO.

However, such situations are not majorly possible in the case of DLT, since the data is distributed, encrypted, and synchronized across multiple ports.

9. Real-life implementations

When talking about the comparison of blockchain and DLT in terms of real-life implementations, blockchain leads the battle.

This is so because many entrepreneurs are slowly and gradually understanding the nature of Blockchain via some guide and use them into their traditional model for leveraging better advantages. In fact, various recognized brands like Amazon, IBM, Oracle, and Alibaba have started offering Blockchain As a Service (BaaS) solutions.

But, when it comes to Distributed Ledger technology, DLT enthusiasts and application developers have begun to explore the core of the technology. They are looking ahead to come across different use cases of DLT, but there are not many significant real-life implementations yet.

Now that we have done a detailed comparison between Blockchain and DLT, let’s learn about other types of DLT besides DLT Blockchain technology.

Popular Forms of DLT Apart from Blockchain

Here are the popular types of distributed ledger technology: 

1. Holochain

Holochain, in simplified terms, is a type of DLT which does not rely upon consensus models or on the concept of tokenization. 

Here, each participating node has its own secure ledger and can act independently, while also interacting with other devices on the network to meet the basic needs of decentralization.  This way, it lets you build more customized and scalable solutions than what Blockchain offers. 

Learn more about Holochain by checking out this video:-

2. Hashgraph

Another form of DLT that exists in the market is Hashgraph. It is basically a patented algorithm that has the potential of delivering all the benefits of Blockchain (decentralization, security, and distribution), but without compromising at transaction speed rate. Something for which it relies upon the concept of Gossip about Gossip protocol and Virtual voting technique.

One real-life implementation of Hashgraph that has proven to hold the potential of becoming a replacement of Blockchain is Hedera Hashgraph, about which you can learn more in this blog.

3. Direct Acyclic Graph (DAG)

Direct Acyclic Graphs (DAGs) or you can say Tangle is also one of the prime types of DLT that pertains in the tech world.

Under this concept, multiple chains of nodes are created and managed at the same time and are interconnected to one another. They, unlike Blockchain, exist both in serial and parallel form.

One of the prime crypto projects that operates on this type of distributed ledger technology is IOTA Blockchain.

Frequently Asked Questions About Blockchain vs Distributed Ledger

Q. Is DLT the same as Blockchain?

No. It’s true that Blockchain is a type of Distributed Ledger database, but the two are not the same. 

Q. Why are DLT and Blockchain terms used interchangeably?

All the Blockchain are a type of DLT. However, the converse is not true. Since most people are unfamiliar with this fact, they often use the two terms as synonyms.

Q. Why Compare Distributed Ledger And Blockchain?

Many people are living with the notion that Blockchain and Distributed ledger are the same thing. However, the two are different and even have different potential – having a knowledge of which can make a difference in the future. So, it is necessary for anyone to look into distributed ledger technology vs Blockchain vs DLT and know which one to pick for better opportunities in the future.

So this was all about what you should know when looking into distributed ledger technology vs Blockchain vs DLT. But, in case you have some more queries or are confused about which one to invest in, feel free to connect with a reputed blockchain app development company. They will guide you through the entire process and offer top-notch blockchain development services.

The post Blockchain vs DLT – An Explanatory Guide You Can’t Miss On appeared first on Appinventiv.

However, we have a debate at hand right now, so let’s take a look

Person 1: “Django is the best web development framework as it offers easy and understandable code.”

Person 2: “Well, Ruby on Rails offers an expeditious development process, which gives it an edge.”

Such discussions regarding Django vs Ruby on Rails are becoming widely popular and unending debates still continue to exist. In such a scenario, we have done our research to present our take on it. 

Because both of the frameworks are loved by top web and mobile app developers, we have compared them both on 17 points of difference. However, before anything, first let’s clear out the air around what they are.

What is Django?

Released back in 2005, Django is a Python-based web framework, and a primary choice for Python app development. What makes it so popular is that it is a general-purpose, open-source, and free framework. Features of Django are highly praised by developers. It was designed with the purpose of simplifying the process of creating complex and database-driven websites. This framework encourages rapid development with a neat and pragmatic design. 

What is Ruby on Rails?

Written under MIT Licence, Ruby on Rails, is a server-side, open-source web app framework. Being a model-view-controller, Rails provides amazing default structures for a database, web services, and pages. It is considered as a time-saving method for developers to write code. 

This framework works majorly on two principles – DRY (Don’t Repeat Yourself) and Convention Over Configuration. The former being self-explanatory eliminates the need of doing the same task of coding again and again, while the latter means that the environment you work in, such as systems, libraries, languages and more, allows many logical situations by default. That means you can adapt to them instead of creating your own rules each time, making the whole programming process a whole lot easier.

Django vs Ruby on Rails Frameworks: What is the Difference

Language

While Django uses Python, Rails is developed using Ruby. Python ranks among the top programming languages and is known for emphasizing clarity and readability of the code, whereas Ruby is renowned for its attributes such as flexibility and freedom and also for its understandable syntax. 

(Learn more about Python in our article “PHP vs Python: Which Language Is Dominating The Market?”)

Moreover, Python is the easiest programming language to learn and write fast. There are various kinds of applications that can be developed with Python. 

On the other side of the table, Ruby was designed with the emphasis on “enjoying” writing the language. Though the applications built using either of them will look and function almost exactly the same, the main difference can be seen under the covers.  

Architecture

One thing that both of the web Development frameworks have in common is both have adopted MVC (Model-View-Controller). However, for Django, it is called MVT (Model-View-Template). Both MVC and MVT are similar for the most part and differ ever so slightly.

In Django, the Model represents the database that describes the data structure, View is the Regular Expression-based URL Dispatcher which controls what the users are supposed to see. Last but not least, Template signifies a web templating system that amalgamates with Django Template Language (DLT). The controller part here is handled by Django itself.

As for in RoR, the Model denotes the data of the database such as comments, pictures, posts, etc. All this is taken care of by ActiveRecord. Here, the View covers the data in the HTML template and then sends it to the Controller, which then later is handled by ActiveView. Now, the ActionController is the one to connect Model and View and handle requests while also taking care of sending responses to the web browser.

User Interface

When performing Django vs Ruby on Rails 2021 comparison on the grounds of the user interface, they both are absolutely the winners. It is because both of them are created to offer a high-class experience. These web-centered frameworks allow every web app development agency to develop highly-functional websites loaded with impeccable add-ons and plugins

Speed and Performance

In the battle of Ruby on Rails vs. Django performance, it is observed that Rails is faster by 0.7%. It is because Rails has the advantage of a rich repository of amazing libraries and plugins to enhance the speed and eventually the performance of this framework. Nevertheless, Django equally encourages expeditious development processes and is an incredible choice for a web framework.

Django REST framework delivers convenience in the form of authentication modules, JSON serializers/deserializers, etc. When comparing Django vs Rails development frameworks, we can say that Django may have an edge as REST is one of the prominent advantages of Django.

Nevertheless, the performance of Django vs Rails is high for both as they leverage modern programming languages while providing the tools to optimize the code.

Stability

Creativity and stability are two parallel elements that are necessary for development. We could consider the one winner who can handle both effectively. Ruby on Rails has the ability to juggle both as it lets users reuse the code to minimize the dependencies. It also uses the Convention over Configuration approach, freeing the coders from any additional efforts.

On the other side of the table, Python practices a more conventional approach by sticking to any proven method to solve the problems at hand, providing stability.

Installation

Comparing Ruby on Rails vs Python Django on the basis of the installation process is not a hard nut to crack. Django’s installation process is very easy, and hence, it only takes about a minute to install it completely.

However, the same thing cannot be said about RoR. You need to first understand what Bundle and Gems are, as they are needed to install the packages for Ruby. First install these two and then run Command Gem Install Rails to install the latest version of the Rails framework.

Security

In Django vs Rails comparison, we had to include the security factor as it is an indispensable part of any website or applications for that matter. 

Django has definitely gained this feature from Python. In fact, NASA also uses the Django framework python, which is a fact enough in itself advocating how secure it is. Django is backed with middlewares while Rails is supported with active records. Django has tools that safeguard your Django app from SQL injection, cross-site scripting (XSS), etc. and so does Rails.

All in all, both Django web development framework and Ruby framework are a reliable option and can be trusted for security.

Scalability

Though the Django web framework has inherited scalability from Python, it still lags behind a little in comparison to Rails. It has better scalability which is the result of its attributes such as freedom and flexibility of code. They both are heavyweight web development frameworks, so they both are designed keeping scalability in mind, but Ruby on Rails development here takes the winner title.

Syntax

It is a known fact that Ruby’s syntax is highly flexible. Well, this can not always be associated with the advantages of Ruby on Rails. It can cause problems and makes it harder to pass on the project to other team members as one feature can be achieved in many distinct ways, creating confusion.

Whereas, Python advocates that there should be only one obvious way to do something, making the code easier to debug and read.

Principles of Development

Principles are like the glue that holds the whole development process together. Django has two prominent principles that are noteworthy – DRY (which we had discussed already) and “Explicit is better than Implicit”. This principle lets developers create applications that are easy to understand and are maintained by numerous people.

Ruby on Rails is likewise not short on development principles. It also uses DRY and Convention over Configuration, which advocates that to be productive you must follow conventions instead of creating your own configurations. Doing this increases speed and efficiency.

Documentation of Frameworks

To be straightforward, it’s a tie between Django vs Rails development frameworks in this respect. Both the frameworks are well documented, making it easy to find the most popular FAQs and answers to the queries. The language of the documentation of both is very easy, understandable, and straightforward, without putting the reader into the state of mayhem. 

Maturity of Platform

Django was first introduced in 2005 and since then has been working its way up in the list of the best web development frameworks. Its recent version was released in April 2019 with many new features and better functionality.

Ruby on Rails was first introduced in 2003 but was officially declared as an open-source framework in 2004. It is also regularly updated, as the latest version of it was launched in August 2018.

HTML Templating

Although both Django and Ruby on Rails framework have templating as a core feature, Django uses a simple templating language in order to enable developers to build templates with minimal programming and HTML skills. On the contrary, Rails’ views (individual page templates) seem more complex.

Usage

If you are looking for a framework that helps in developing complex database-driven websites and web apps in less time, with efficiency in system administration, scientific programming, data analytics and manipulation, then Django is the way to go.

On the other hand, Rails also assists in developing database-backend web apps by providing better usability and freedom to the programmers. Because Ruby is flexible in nature, it is an ideal choice for meta programming and creating pleasing codes.

Web Servers

For Django, the main web servers are NGINX using WSGI, Apache, Gunicorn, and Cherokee. The database servers for Django are SQLite, PostgreSQL,  MySQL, and Oracle.

The Ruby on Rails web framework uses NGINX, Apache (running Phusion Passenger Module), and WEBrick as the prominent servers. Database servers for Rails include PostgreSQL and MySQL.

Community Support and Ecosystem

Django being an open-source framework, also has an open-source ecosystem, implying that there is a sea of libraries and tools, both paid and free, available to developers. Moreover, the official documentation for Django is more than enough for reference in case you need an answer to any problem. 

Django community has over 11,000 people with more than 4,000 readymade packages for developers to use. Ruby on Rails in contrast also has a highly active community of 5,000 people devoted to it who have already contributed a lot of Gems with reusable code.

Learning Curve

It is widely known that Python is a very easy programming language to learn among its contenders, which also makes the learning curve of Django small. There are various resources available offline as well as online, making it easier to resolve queries.

On the contrary, Rails has a very steep learning curve due to independent concepts, that a developer needs to hone in order to become proficient in Railṣ. So, it is only recommended for seasoned programmers and developers.

Use Cases of Django

Use Cases of Ruby on Rails

Frequently Asked Questions

Q. Which is Better Ruby on Rails or Django?

Well, it is hard to answer this question in a sentence. There are many similarities between Django and Rails along with their differences. Both of them are efficient in different tasks. They both give a hard competition to each other in performance, speed, community support, scalability, security and more.

Q. Is Django Faster than Rails?

Yes. To be precise, Ruby on Rails is 0.7% faster than Django.

Q. Is Ruby on Rails Worth Learning in 2021?

Absolutely. Ruby on Rails is one of the preferred choices of developers and programmers to create websites and web applications. In fact, RoR’s popularity is expected to continue in 2020, making it imperative for you to learn it.

Q. What Apps are Still using Ruby on Rails?

Applications and websites like AirBnB, Hulu, Instagram, Crunchbase, etc. are developed using Ruby on Rails.

Django vs Ruby on Rails – Which Framework to Choose and When?

All in all, both Django and Ruby on Rails web frameworks are at the top of their category giving a hard competition to each other. Nevertheless, there are certain areas where one supersedes the other. 

For example, if you want a highly detailed app loaded with remarkable features then you should go with Django. However, if you are thinking of a quick launch and then work on the details of the website or a web app then Ruby on Rails is your ideal bet. It is because it possesses shortcuts and automation features, which makes it easier to add complex features in the web applications. To know more about the web development solutions and  frameworks and make your market presence, contact a web development company today. 

The post Django Vs Ruby on Rails – Which Framework is Best for 2021 appeared first on Appinventiv.

With modern businesses relying upon heap of data to better understand their consumers and market, the technologies like Big Data are gaining a huge momentum. 

Big Data, just like AI has not just landed into the list of top tech trends for 2020, but is expected to be embraced by both startups and Fortune 500 companies for enjoying exponential business growth and ensure higher customer loyalty. A clear indication of which is that the Big Data market is predicted to hit $103B by 2027.

Now, while this on one side everyone is highly motivated to replace their traditional data analytics tools with Big Data – the one that prepares the ground for advancement of Blockchain and AI, they are also confused about choosing the right Big data tool. They are facing the dilemma of picking between Apache Hadoop and Spark – the two titans of Big Data world.

So, considering this thought, today we will be covering an article on Apache Spark vs Hadoop and help you to determine which one is the right option for your needs.

But, firstly, let’s have a brief introduction of what is Hadoop and Spark.

Apache Hadoop is an open-source, distributed, and Java-based framework that enables users to store and process big data across multiple clusters of computers using simple programming constructs. It comprises of various modules that work together to deliver an enhanced experience, which are:- 

1. Hadoop Common
2. Hadoop Distributed File System (HDFS)
3. Hadoop YARN
4. Hadoop MapReduce

Whereas, Apache Spark is an open-source distributed cluster-computing big data framework that is ‘easy-to-use’ and offers faster services.

The two big data frameworks are backed by numerous big companies due to the set of opportunities they offer.

Advantages of Hadoop Big Data Framework

1. Fast

One of the features of Hadoop that makes it popular in the big data world is that it is fast.

Its storage method is based on a distributed file system that primarily ‘maps’ data wherever located on a cluster. Also, data and tools used for data processing are usually available on the same server, which makes data processing a hassle-free and faster task.

In fact, it has been found that Hadoop can process terabytes of unstructured data in just a few minutes, while petabytes in hours.

2. Flexible

Hadoop, unlike traditional data processing tools, offers high-end flexibility. 

It lets businesses gather data from different sources (like social media, emails, etc.), work with different data types (both structured and unstructured), and get valuable insights to further use for varied purposes (like log processing, market campaign analysis, fraud detection,etc).

3. Scalable

Another advantage of Hadoop is that it is highly scalable. The platform, unlike traditional relational database systems (RDBMS), enables businesses to store and distribute large data sets from hundreds of servers that operate parallely. 

4. Cost-Effective

Apache Hadoop, when compared to other big data analytics tools, is much inexpensive. This is because it does not require any specialized machine; it runs on a group of commodity hardware. Also, it is easier to add more nodes in the long run.

Meaning, one case easily increase nodes without suffering from any downtime of pre-planning requirements.

5. High Throughput

In the case of Hadoop framework, data is stored in a distributed manner such that a small job is split into multiple chunks of data in parallel. This makes it easier for businesses to get more jobs done in less time, which eventually results in higher throughput.

6. Resilient to Failure

Last but not least, Hadoop offers high fault-tolerance options which helps to mitigate the consequences of failure. It stores a replica of every block that makes it possible to recover data whenever any node goes down.

Disadvantages of Hadoop Framework

1. Issues with Small Files

The biggest drawback of considering Hadoop for big data analytics is that it lacks the potential to support random reading of small files efficiently and effectively. 

The reason behind this is that a small file has comparatively lower memory size that the HDFS block size. In such a scenario, if one stores a vast number of small files, there’s higher chances of overloading of NameNode that stores the namespace of HDFS, which is practically not a good idea.

2. Iterative Processing

The data flow in big data Hadoop framework is in the form of a chain, such that the output of one becomes the input of another stage. Whereas, the data flow in iterative processing is cyclic in nature. 

Because of this, Hadoop is an unfit choice for Machine Learning or Iterative processing-based solutions.

3. Low Security

Another disadvantage of going with Hadoop framework is that offers lower security features.

The framework, for example, has security model disabled by default. If someone using this big data tool does not know how to enable it, their data could be at higher risk of being stolen/misused. Also, Hadoop does not provide the functionality of encryption at the storage and network levels, which again increases the chances of data breach threat.

4. Higher Vulnerability

Hadoop framework is written in Java, the most popular yet heavily exploited programming language. This makes it easier for cybercriminals to easily get access to Hadoop-based solutions and misuse the sensitive data.

5. Support for Batch Processing Only

Unlike various other big data frameworks, Hadoop does not process streamed data. It supports batch processing only, and the reason behind is that MapReduce fails to take advantage of memory of the Hadoop Cluster to the maximum.

While this is all about Hadoop, its features and drawbacks, let’s have a look into the pros and cons of Spark to find an ease in understanding the difference between the two.

Benefits of Apache Spark Framework

1. Dynamic in Nature

Since Apache Spark offers around 80 high-level operators, it can be used for processing data dynamically. It can be considered the right big data tool to develop and manage parallel apps.

2. Powerful

Due to its low-latency in-memory data processing capability and availability of various built-in libraries for machine learning and graph analytics algorithms, it can handle various analytics challenges. This makes it a powerful big data option in the market to go with.

3. Advanced Analytics

Another distinctive feature of Spark is that it not only encourages ‘MAP’ and ‘reduce’, but also supports Machine Learning (ML), SQL queries, Graph algorithms, and Streaming data. This makes it suitable for enjoying advanced analytics.

4. Reusability

Unlike Hadoop, Spark code can be reused for batch-processing, run ad-hoc queries on stream state, join stream against historical data, and more.

5. Real-time Stream Processing

Another advantage of going with Apache Spark is that it enables handling and processing of data in real-time.

6. Multilingual Support

Last but not least, this big data analytics tool support multiple languages for coding, including Java, Python, and Scala.

Limitations of Spark Big Data Tool

1. No File Management Process

The prime disadvantage of going with Apache Spark is that it does not have its own file management system. It relies on other platforms like Hadoop for meeting this requirement.

2. Few Algorithms

Apache Spark also lags behind other big data frameworks when considering the availability of algorithms like Tanimoto distance.

3. Small Files Issue

Another disadvantage of using Spark is that it does not handle small files efficiently.

This is because it operates with Hadoop Distributed File System (HDFS) which find it easier to manage a limited number of large files over a plenty of small files.

4. No automatic Optimization Process

Unlike various other big data and cloud-based platforms, Spark does not have any automatic code optimization process. One has to optimize code manually only.

5. Not Suitable for Multi-User Environment

Since Apache Spark cannot handle multiple users at the same time, it does not operate efficiently in multi-user environment. Something that again adds to its limitations.

With the basics of both the big data frameworks covered, it is likely that you are hoping to get familiar with the differences between Spark and Hadoop.

So, let’s wait no further and head towards their comparison to see which one leads the ‘Spark vs Hadoop’ battle.

Spark vs Hadoop: How the Two Big Data Tools Stack Up Against Each Other

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1. Architecture

When it comes to Spark and Hadoop architecture, the latter leads even when both operate in distributed computing environment. 

This is so because, the architecture of Hadoop – unlike Spark- has two prime elements – HDFS (Hadoop Distributed File System) and YARN (Yet Another Resource Negotiator). Here, HDFS handles big data storage across varied nodes, whereas YARN take care of processing tasks via resource allocation and job scheduling mechanisms. These components are then further divided into more components to deliver better solutions with services like Fault tolerance.

2. Ease of Use

Apache Spark enables developers to introduce various user-friendly APIs like that for Scala, Python, R, Java, and Spark SQL in their development environment. Also, it comes loaded with an interactive mode that supports both users and developers. This makes it easy-to-use and with low learning curve.

Whereas, when talking about Hadoop, it offers add-ons to support users, but not an interactive mode. This makes Spark win over Hadoop in this ‘big data’ battle.

3. Fault Tolerance and Security

While both Apache Spark and Hadoop MapReduce offers fault tolerance facility, the latter wins the battle.

This is because one has to start from scratch in case a process crashes in the middle of operation in Spark environment. But, when it comes to Hadoop, they can continue from the point of the crash itself.

4. Performance

When it comes to considering Spark vs MapReduce performance, the former wins over the latter.

Spark framework is able to run 10 times faster on disk and 100 times in-memory. This makes it possible to manage 100 TB of data 3 times faster than Hadoop MapReduce.

5. Data Processing

Another factor to consider during Apache Spark vs Hadoop comparison is data processing.

While Apache Hadoop offers an opportunity to batch processing only, the other big data framework enables working with interactive, iterative, stream, graph, and batch processing. Something that proves that Spark is a better option to go with for enjoying better data processing services.

6. Compatibility

The compatibility of Spark and Hadoop MapReduce is somewhat the same. 

While sometimes, both big data frameworks act as standalone applications, they can work together as well. Spark can run efficiently on top of Hadoop YARN, while Hadoop can easily integrate with Sqoop and Flume. Because of this, both support each others’ data sources and file formats.

7. Security

Spark environment is loaded with different security features like event logging and use of javax servlet filters for safeguarding web UIs. Also, it encourages authentication via shared secret and can take leverage of potential of HDFS file permissions, inter-mode encryption, and Kerberos when integrated with YARN and HDFS.

Whereas, Hadoop supports Kerberos authentication, third-party authentication, conventional file permissions, and access control lists, and more, which eventually offers better security results.

So, when considering Spark vs Hadoop comparison in terms of Security, the latter leads.

8. Cost-Effectiveness

When comparing Hadoop and Spark, the former needs more memory on disk while the latter requires more RAM. Also, since Spark is quite new in comparison to Apache Hadoop, developers working with Spark are rarer.

This makes working with Spark an expensive affair. Meaning, Hadoop offers cost-effective solutions when one focuses on Hadoop vs Spark cost.

9. Market Scope

While both Apache Spark and Hadoop are backed by big companies and have been used for different purposes, the latter leads in terms of market scope.

As per the market statistics, Apache Hadoop market is predicted to grow with a CAGR of 65.6% during the period of 2018 to 2025, when compared to Spark with a CAGR of 33.9% only.

While these factors will help in determining the right big data tool for your business, it is profitable to get acquainted with their use cases. So, let’s cover here.

Use Cases of Apache Spark Framework

This big data tool is embraced by businesses when they wish to:

• Stream and analyze data in real-time.
• Relish the power of Machine Learning.
• Work with interactive analytics.
• Introduce Fog and Edge Computing to their business model.

Use Cases of Apache Hadoop Framework

Hadoop is preferred by startups and Enterprises when they want to:-

1. Analyze archive data.
2. Enjoy better financial trading and forecasting options.
3. Execute operations comprising of Commodity hardware.
4. Consider Linear data processing.

With this, we hope that you have decided which one is the winner of ‘Spark vs Hadoop’ battle with respect to your business. If not, feel free to connect with our Big Data Experts to clear all the doubts and get exemplary services with higher success ratio.

FREQUENTLY ASKED QUESTIONS         

1. Which Big Data Framework to Choose?

The choice depends completely on your business needs. If you are focusing on performance, data compatibility, and ease-of-use, Spark is better than Hadoop. Whereas, Hadoop big data framework is better when you focus on architecture, security, and cost-effectiveness.

2. What is Difference between Hadoop and Spark?

There are various differences between Spark and Hadoop. For example:-

• Spark is 100-times factor that Hadoop MapReduce.
• While Hadoop is employed for batch processing, Spark is meant for batch, graph, machine learning, and iterative processing.
• Spark is compact and easier than the Hadoop big data framework.
• Unlike Spark, Hadoop does not support caching of data.

3. Is Spark Better than Hadoop?

Spark is better than Hadoop when your prime focus is on speed and security. However, in other cases, this big data analytics tool lags behind Apache Hadoop.

4. Why Spark is Faster than Hadoop?

Spark is faster than Hadoop because of the lower number of read/write cycle to disk and storing intermediate data in-memory.

5. What is Apache Spark Used for?

Apache Spark is used for data analysis when one wants to-

• Analyze data in real-time.
• Introduce ML, and Fog Computing into your business model.
• Work with Interactive Analytics.

The post Spark vs Hadoop: Which Big Data Framework Will Elevate Your Business? appeared first on Appinventiv.

The most no-nonsense way to understand its meaning is that if a product is being liked by a market which is demanding the product heavily, the product’s parent company has achieved a product market fit. 

While the definition paints a picture of nothing less than a utopia, the reality is that there are very limited ways to achieve this utopia. And the ways come packed with confusions. 

The three confusion-clouded ways that help entrepreneurs reach the product market fit are: Proof Of Concept, Prototype, and Minimum Viable Product. 

The major confusion that surrounds these three app viability check strategies is what separates them from each other and which one would get them closest to achieving the product market fit utopia before their competitors act on the idea. 

The intent of this article is to solve the confusion and help you decide which strategy to go with to achieve a product market fit in your app industry. 

But before you even get to the point of making a decision between POC vs minimum viable product vs prototype, there are a few things that you should keep into consideration:

1. Who is your target audience?
2. What are you trying to validate?

POC vs. MVP vs. Prototype: The Concept 

POC (Proof of Concept)

As the name denotes, POC (Proof of Concept) is an approach which businesses use to validate the idea or the feasibility of the concept. The stage comes into existence way before the mobile app development process starts. Here a small project is implemented for verifying if a concept can be implemented on the technical capability and business model grounds. 

A mobile app POC is not usually shown to the public or the customers for the main idea behind it is to simply check if the idea is workable or not. 

Prototype

Prototype focuses entirely on showing how an app will flow from one screen to another and how it’d look to the end users. Building a mobile app prototype is one of the most important processes of app development for it uses tools to help with understanding how the app flows, what would be its usability, and what all features must be added in the application.

Minimum Viable Product

What is Minimum Viable Product? MVP is a functional app that comes loaded with the prime features that best represent the application. 

The MVP development process helps in verifying the app feasibility, teams’ assumptions about the application, and its probable usability along with the market demand. When you build a minimum viable product, it ultimately helps in developing an application which is prepared to gather the end-user feedback.

POC vs MVP vs. Prototype: How to Choose The Best Approach?

The question: which is the best strategy out of the three, in itself is wrong. 

In order to truly achieve a product fit, entrepreneurs cannot choose between the strategies. They will have to either choose one of the strategies, depending on the stage of their application or would have to see them as steps. 

Let us explain both the points in detail. 

Scenario 1: When you are yet to enter the idea validation stage

When you haven’t started with the development of either of the strategies, you are at a stage, which we deem ideal. A stage where you have the time to chart the app validation lifecycle. 

The benefits of investing in the lifecycle that starts from POC then reaches Prototype before getting to the MVP stage are manifold. 

1. Rightful Testing of the Concept: The point of developing an application starting with the POC stage and then moving to prototype and MVP is to help check the viability of the idea. It helps entrepreneurs adjust their initial idea on the basis of how their audience reacts. 
2. Speedy Development: When you go with this approach, your partnered mobile app development agency work with limited features – one which have been market tested. Meaning, there is a lot to profit from in a very little time. 
3. Low Investment Risk: When you develop an application that has been tested at three stages, you give the investors an assurance that it is risk free. This, in turn, translates to greater investments. 

Scenario 2: When you have entered the idea validation stage 

When you have already entered the idea validation phase, the probability is very less for you to follow a step-by-step process in order to reach product market fit phase. Under this scenario, you will have to make a choice between POC vs Prototype vs MVP.

And the choice will depend on several factors – ones that are enough to be converted into IFTTT scenarios. 

Here are some scenarios to help you make the choice –

Choose MVP When:

1. When you have to show the customers a working app
2. When you have to make money out of the application
3. When you need a bug-free app ready to be used by the world
4. When you need higher retention at low cost
5. When you wish to know the market perception to improvise the application

Choose POC When:

1. When you need seed-stage funding
2. When you have to check if the idea works, technically
3. When you are aiming to share internal knowledge among team 
4. When you want to assess project or the features’ success

Choose Prototype When:

1. When you want a visualization of how the app would function
2. When you have limited money and time but you want to show the application to stakeholders
3. When there is very limited technical availability
4. When you need an instant look and feel of the application. 

FAQs About POC vs. MVP vs. Prototype

Q. What is the difference between Prototype and MVP?

The primal difference a prototype and an MVP is that the latter is a ready to publish publicly version of an application, while the former is more of a version that is there to test the idea within the internal stakeholders of a company.

Q. What is the difference between a Prototype and a Proof of Concept?

Proof of concept vs Prototype is a comparison of two very different concepts. While POC is merely a set of documents or basic level software used to check the workability of an idea, Prototype is the clickable version software that give stakeholders peek into how the user would move inside an application. 

Q. When to consider building a MVP app?

There are different instances where the MVP development method makes more sense compared to creation of POC or Prototype:

• When you have to show the customers a working application
• When you have to make money out of your app
• When you need a bug-free app ready to be used by the world
• When you need higher retention at low cost
• When you wish to know the market perception to improvise the application

Q. What is the difference between POC and MVP?

POC vs MVP is a comparison between a concept and an application that is ready to be published in the stores. While the intent between both the concept is the same – to validate an app idea, the stages in which they make an appearance are very different. 

A proof of concept methodology is the first stage of idea validation – a stage where a small project is implemented for verifying if a concept can be implemented on the technical capability and business model grounds. 

Minimum Viable Product approach, on the other hand, is a functional app that comes loaded with the prime features that best represent the application. This is the major difference between POC and MVP.

Final Note

Product development is like experimenting with a vision in mind. The process starts with placing the foundation right to create a POC and Prototype, supported by MVP. As discussed above, we have tried to erase the thin line between minimum viable product vs proof of concept vs Prototype and their role in validating a product. 

The POC helps clear your doubts about the feasibility of the idea. Prototype clears the doubts about the UX design and look of the product. And, minimum viable product helps gain continuous user feedback that forms the foundation for the full product.

You can choose one or even the combination of three to beat the odds of product development failure! If you have any further inquiries related to POC, Prototype and MVP development services, you can contact MVP app development company to get started. 

The post POC vs. MVP vs. Prototype: The Strategy Closest to Product Market Fit appeared first on Appinventiv.