Best frameworks for desktop application development

As we move with the technology that is completely dependent on the Internet, the necessity for web app development increases. Cloud computing is rapidly growing in popularity and similarly web-based apps fall right into that line. A reason behind increasing popularity of web applications is that you can utilize the internet as your processor, instead of your computer. Light-weight photo editors, email clients, word processing and spreadsheet applications are very popular as web-based applications. They are also not as robust as what you would find through something like Word, Photoshop or a Windows desktop application. For this reason, desktop applications continue to be extremely popular and extremely useful. 

Types of Desktop Applications-

Selection of the best framework for desktop application development is depend on the tasks that application should perform. 

1. Desktop frameworks for Windows app Development-

Microsoft provides an extensive toolkit for the development of the powerful backend and interactive frontend for the Windows app development. The desktop app framework is accessible and also advanced. It has various pricing models that will be perfect for students who are learning how to design their first desktop application and for the professional desktop software development companies who create the most sophisticated enterprise software solutions. There is a wide choice available for choosing the best framework for windows desktop application. You can choose the set of tools for the Universal Windows Platform application development, or you can choose the set of tools provided for the development of the regular runtime desktop software distributed as the installation packages.

2. Desktop Framework for MacOS-

Just like microsoft, apple encourages developers to create beautiful versions of the software product

For the past few years, Apple is moving towards unification of the OS architecture and development tools for all the platforms including iOS, MacOS, WatchOS, and even tvOS.  Creating apps for MacOS has never been an easy and comfortable as it is now.

3. Cross platform desktop Frameworks-

Using the cross-platform desktop framework is the best decision to develop a desktop app. Because later it can be easily transitioned to another desktop platform, mobile devices, and web-applications. The most significant advantage of working with any cross-platform desktop framework is that-  you’re creating the unified codebase that later can be reused for the SaaS version of the software product thus significantly increase the target audience. You have multiple options available to choose the desktop framework.

Frameworks for Native Windows Development-

1. WPF-

Windows Presentation Foundation is the most popular framework for native Windows application development. It is a framework within .NET framework which is mainly used for developing the graphics of a desktop application. You can use it to create the user interface for software. It is a first choice for many of the programmers. The runtime libraries of the WPF are embedded in the Windows operating system. A key characteristic of WPF is its ability to unify different user interface elements. These elements include vector graphics, adaptive documents, pre-rendered media objects and rendering 2D and 3D.

2. UWP-

UWP is next important framework for desktop application development. It is also a framework that is highly credited for the popularity of the .NET platform. This is because it allows developers to create cross-platform desktop applications. UWP allows developers to create applications that can run on many multiple Microsoft-owned platforms. This implies your software can run on multiple devices. This is made possible by a special algorithm for Windows application development.

UWP framework is highly scalable. If you had built an application for desktop, the framework will make it possible to scale it for mobile devices. The application will most likely run easily on portable, tablet and even on Xbox. It is a framework that has incredibly improve the usefulness of Visual Studio.

Native MacOS development-

There are millions of people in the world who use the MacOS operating system. So you can not avoid the  development for Mac operating systems. For this there is Cocoa framework that allows you the efficient development.

1. Cocoa-

Cocoa is a native framework for native MacOs development. It is an object-oriented framework for building a user interface for the MacOS, iOS, and tvOS. It not only adds the functionality of the UI  and also makes the interface more interesting. Cocoa framework allows you to add the animation feature, graphic control element and gesture recognition features into a desktop application. All the development tools for Cocoa are provided by Apple. When it comes to writing codes, the framework can be used with Python, Perl, and Ruby languages. To use these languages, you will need bridges such as PyObjC, PasCocoa, and RubyCocoa. 

Cross platform development-

1. Electron-

Electron is a cross-platform development framework, developed by GitHub. This framework uses Node.js and developers can use it to build cross-platform desktop applications. Many big companies like Facebook, Microsoft, and Stack uses this framework. The framework allows developers to focus on the basic important functionalities of the software. This is because the framework handles the hardest parts of a software development process.

2. Swing-

Swing is a Java-based framework to develop a desktop application. The applications built by Swing can run on any platform. The main function of Swing is to build a better GUI for desktop applications. It can copy the design, look, and style of many desktop applications. The framework includes various UI components such as buttons, panels, tables scroll panes and checkboxes.

Conclusion-

As you can see, some frameworks are for native Windows, others are MacOs while others are cross-platform development frameworks. The nature of the desktop application that you are developing will determine the development framework to use. Looking at the best frameworks for desktop application development, always remember to look at the following:

The Cost: If you have a low budget or no budget, then the free frameworks will work best for you. If you can spend more, do so because you get more features and more functionality that will make your applications even better.

The Features: Some frameworks has many features, whereas some are with less. There are some developers who only want to make their own code, while others love being able to drag-and-drop.

The Distribution Model: Making an application is one thing, getting it out there is another. Regardless of whether you need it on the web, on desktops, tablets, you need a framework that handles the distribution for you. This incorporates good auto-updates, cross-platform integration, and security features.

Get a free quote for any desktop application development. Developers at Solace are well trained with the new frameworks for desktop application development.

Top 10 Blockchain Programming Languages for 2019

What is Blockchain?

Blockchain, as the name implies, is a chain of blocks; connected sequentially using complex cryptographic logic. This technology was first used in the implementation of the popular BitCoin cryptocurrency. Because of the high level security it gives in business exchanges, the blockchain innovation is used intensely in the business. From corporate firms to industrial banks, blockchain developers are sought everywhere equally. To help you learn more about the best programming language for blockchain, our experts, at Solace, curated this blog. Let us see which programming languages you should use for blockchain development and why?

Top 10 Blockchain Programming languages-

1. Vyper-

It is a new blockchain programming language. Vyper aims at making writing smart contracts a breeze. Just like solidity, it is usually used for the EVM. It differ considerably from Solidity with respect to control structures and handling security issues. Vyper removes many of the object-oriented functionalities present in Solidity, such as modifiers, recursive calling, and infinite loops. Vyper does so to avoid security issues that arise with their use. If you’re looking for a simple blockchain coding language for writing smart contracts, Vyper is worth a look.

2. Scilla-

Scilla is another newly-emerged blockchain programming language. It is maintained by Zilliqa and aims at reducing safety risks and providing a formal verification for smart contracts. These formal verifications offer static guarantees of smart contracts before their commission to a blockchain.

This language reduces occurrence of error-prone codes substantially and also decreases the need for extensive audibility. The intermediate level blockchain coding language is compiled into bytecodes and so it is highly portable.

3. C++- 

The language behind each significant innovation utilized in the business, C++ holds its predominance even in the blockchain innovation. Bitcoin is the first ever implementation of blockchain was written initially in this ultra-precise and powerful language. C++ is the best programming language blockchain development due to many reasons such as primitive control over memory, advanced multithreading capabilities, move semantics and core object-oriented features like runtime polymorphism and function overloading. Due to the object oriented feature of this language it gives developers the ability to bind the data and methods intended to execute them together.

4. Java-

Syntax of Java is similar to the syntax of C++. Java is an interpreted language: an internal representation (bytecode) and an interpreter of this representation are defined for it, which is already available on most platforms.

The interpreter simplifies debugging for programs written in Java, and guarantees their portability to new platforms and adaptability to new environments. It dispenses with the impact of Java programs on different projects and files accessible on the new platform, and subsequently guarantees security when executing.These properties of Java make it conceivable to use as the fundamental programming language for projects dispersed over networks. This is the reason why Java can be used as a blockchain coding language.

5. Ruby-

Although quite old and tested by the industry, Ruby gained popularity as a blockchain programming language in the last couple of years or so. Ruby is an interpreted high-level language with object-oriented features. It is used for the blockchain programming because of many reasons. It offers the ability to prototype their vision rapidly using open source third-party APIs and plugins. The Ruby ecosystem is thriving with loyal contributors since its inception as the de-facto web language starting from the first half of this millennium. It’s especially prevalent within the Asian developers, the most substantial fraction of open source blockchain developers.

6. Python-

Python is one of the most easy-to-use programming languages. It tends to be effectively utilized as a blockchain programming language. Python can accept large data sets, clean, process, analyze, and visualize them. These benefits are very well suited for industries that deal with big data processing, such as finance and IoT. This is also the reason why Python is one of the programming languages used for aerospace project creation.

7. Go-

Go is the best blockchain programming language for building hyper ledger fabric. The statically-typed yet compiled language is on par the performance level required by a blockchain coding language. Go comes with every modern features such as classes and inheritance, generics, annotations, constructors, and exceptions that you need when building your first blockchain. Go offers top-notch concurrency support in blockchain applications, because of its smart implementation of channels and interfaces. So, it’s one of the best programming languages for blockchain when it comes to developing a system that is not only efficient but also lightning fast.

8. Solidity-

Solidity is a JavaScript-like object-oriented language for developing smart contracts. Contracts are used in Ethereum blockchain coding and they have a very important feature: each line of code, each team and even simply storing data has its own specific value (ETH).

9. Rust-

Rust targets at providing open source devs the capability to build fast and efficient blockchain systems. It is found to be best when it comes to CPU bound tasks. You can adopt either a useful strategy or a basic one with Rust for building up your blockchain. It’s one of the best programming languages for blockchain because of its highly-capable mechanism of handling mutable states. The quick, memory safe, and only simultaneous nature of this blockchain coding language makes it most suitable for developing real-world blockchains.

10. PHP-  

It is used to build simple to complex blockchain systems, because of its object-oriented features and a vast active open source community. If you’re a new programmer looking for getting your hands dirty at blockchain coding, PHP might turn out to be the best option for you. A large number of PHP developers will guarantee a ready workforce in case you develop something highly capable and intend on going corporate. For more information you can read our blog- Use PHP for blockchain development.

Conclusion-

It is fairly simple to foresee that the blockchain innovation will keep on changing the manner in which information and resources are moved in modern businesses. It will have a big impact on industries like the banking industry and big pharma corporations shortly, just like it does currently on cryptocurrencies. So more opportunities will be created for you with high-level skills of a blockchain programming language.

Are you looking to use blockchain technology for your business? Then Solace is the right place to start. Developers at Solace are well trained at blockchain development to give you the best solution. Contact us for blockchain development to modernize your business.

Data Science vs Machine Learning : Know the difference

Many concepts are closely related and typically interconnected to one another, so might even cause a confusion. There are many misconceptions about these technologies. Here we would like to focus on data science and Machine learning and their differences. Data Science and machine learning are developing rapidly and companies are now looking for experts who can filter the information and help them drive quick business decisions effectively. 

Data science vs Machine Learning

 What is Data Science?

Data science is the field that covers scientific approaches of processing and structuring data in order to extract knowledge. This is a more extensive term that brings together various strategies for gathering information for different associations, organizations, and governments. In short , it applies methods and approaches from mathematics, computer science, statistics and information science.

As the large amount of data considerably grows, the main difficulty is not storage anymore, but the right ways of processing and receiving the most most important. A lot of scientists direct their attention toward this issue. Therefore data science is a special approach that applies a number of processes for describing, predicting, interfering, and extracting insights from huge data. The main task is to discover productive solutions by applying capable hardware, simple and complex systems as well as data science algorithms. The concept relies on creatively binding different expertise such as mathematics, technical and business skills.

Working of Data Science-

Firstly, the quantitative techniques used in mathematics help to find solutions for data utilization. It allows data insights and the creation of data products. The data is expressed mathematically through its texture, dimension, and correlation. Data science for business deals not only with statistics but analytical functions also. 

Secondly, technology is an essential part of the overall process. Data scientists apply digital tools to organize a large amount of data, handle the complex tasks and ease the process flows. Their technical skills concentrates on building, recomposing and finally receiving the final products. Python, R, SQL, SAS are used for coding. In short, the data scientists have to be capable of complexity and finding cohesive approaches.

Thirdly, Applying mathematical and technical expertise data scientists are determined to learn from it, share observations and make conclusions. This makes them be good business assistants. Therefore data science for business is the valuable tool to guide the process and provide the business consultations.

What is machine learning?

Machine learning uses algorithms to extract data, learn from it and then forecast future trends for that topic. Traditional machine learning involves statistical analysis and predictive analysis. These analysis is used to spot patterns and catch concealed bits of information which is dependent on perceived data. One of the best examples of machine learning implementation is Facebook. Facebook’s machine learning algorithms collect behavioral information for every user on the social platform. Based on past behavior of individual, the algorithm predicts interests and suggests articles and notifications on the news feed. For eg., Amazon recommends products, or when Netflix recommends movies based on past behaviors, machine learning is at work.

Machine learning will not exist without data science as long as it applies data science algorithms and techniques for its performance. Theory and methods used here are conveyed from a mathematical study of optimization, predictive analytics, computational statistics, etc. Machine learning is a great option to reproduce patterns and make the most of its experience. The algorithms perform automatically, while human experts concentrate on the better and harder solution. Machine learning experts should obtain programming, data modeling, and evaluation skills also with the knowledge of statistics and Probability theory.

Furthermore, it is time-saving because machine learning analyzes and delivers valuable solutions faster. It requires less time to create the data analyzing model as compared to the time required by human experts so it manually. So this approach has proved its effectiveness by AI and human cooperation. It has greatly contributed to different spheres and easier processing and analyzing the data. You can use machine learning for web search, advertising, marketing, data security, healthcare, fraud detection, image and speech recognition etc. You can see the difference between Artificial intelligence and Machine learning at- Artificial Intelligence and Machine Learning: A Comparison.

Key Difference between Data Science vs Machine Learning-

• Data science creates insights from data dealing with all real world complexities. It includes task like understand requirements, extracting data etc. Whereas Machine learning accurately classify or predict outcome for new data point by learning patterns from historical data.
• Most of the input data is generated as human consumable data which is to be read or analyzed by humans like tabular data or images. specifically for algorithms ,Input data for ML will be transformed. Feature scaling, Word embedding or adding polynomial features are some examples
• Complexity for data science is with components for handling unstructured raw data. Whereas complexity in machine learning is with algorithms and mathematical concepts behind it.
• In data science lot of moving components are scheduled by an orchestration layer to synchronize independent jobs, and with machine learning, Ensemble models will have more than one ML model and each will have weighted contribution on final output.
• To be the expert of data science one must posses the following skillset- ETL and data profiling, Domain expertise, Strong SQL, NoSQL systems, Standard reporting/ visualization. Ans to be the machine learning expert one must have Strong Maths understanding, Python/R programming, Data wrangling with SQL, Model-specific visualization.

Conclusion- 

Due to the continuous development of technologies and the increasing amount of data, a lot of specialists are searching for better approaches for organizing, utilizing and gaining from it. Data science manages various processes to receive reasonable solutions whereas machine learning as its subdivision deals with data science algorithm. The two methodologies have effectively suggested themselves at the market and are typically utilized in various circles.

Are looking to incorporate machine learning to your business? Then you are at the right place. Solace developers are well trained for machine learning development and believes in effectiveness of using ML. Get a free quote for machine learning development that help you to achieve the success that you deserve.

Why you should use go programming?

As innovation advances, so does the quantity of new programming languages. Trends such as universal multiprocessor devices, vast increase in number of mobile devices and current IoT revolution have helped drive this blast. But whatever the reason, nowadays developers are living in  inexorably divided world. So it is your responsibility to keep up with the latest technologies or languages such as “Go”. You can also see the difference between Python, Ruby and GoLang at our blog- A Battle of Trios: Python vs Ruby vs Golang. Let us see, what makes it different from its competitors and why should you use it?

What is Go?

GoLang

Google started Go language. It emphasizes simplicity and clarity in its code so it is easy to understand and maintain. Also Go possess a strong features that enable exceptionally complex applications to be developed. It is a response to developing trends that seek to reduce the psychological overhead  that a programming languages needs and reduce compile times to support code-build test loops required by methodologies.

Things you will like about Go-Lang-

1. Golang has good credentials-

Creators of Go contributed to influential things as C, B, Unix, JVM, and others. To meet the challenges of the present and anticipate the challenges of the future, Golang is develop.

2. Golang is open source by nature-

It’s significant for a programming language to be open-source to become better, cleaner, and more efficient. If a code has defects, gophers will recognize and wipe out the problems.

3. Go lang is Fast-

Go has a simple structure and syntax also. It is without classes and type inheritance. This language is based on functions, so it is simple and also fast to learn. It is a compiled language so, it provides faster feedback, shorter time to market, and also saves time and money. Because of its simplicity, it is more maintainable, and development is faster and cheaper.

4. Golang is concurrent-

Concurrency is more important. It allows multiple processes running simultaneously and effectively. Golang has efficient concurrency, like C, C++, Java, and at the same time concurrency in Go is done much easier.

5. Cross-platform-

Different platforms, such as Windows, Linux, Unix and BSD versions and mobile devices can use Go language. Also, it compiles well on many OS’s.

6. Garbage Collector-

A form of automatic memory management which has an important effect on performance and helps to make concurrency more efficient.

7. Golang keeps the bugs away-

Since it is a compiled language, with very strict typization, and it is statically typed, developers have to be more accurate and attentive, so the code is neater and safer.

What makes Go different?

One essential issue Go attempts to explain is the need to diminish the psychological overhead that it puts on engineers. Several aspects of the language highlight this. These include:

Focused vocabulary-

Languages have various keywords that a developer must remember. Some of these keywords are designed to support programming concepts that have been around for decades. With Go, many of these concepts have been consolidated however much as could be expected so as to reduce the amount of keywords required. As a result, Go currently has around 25 keywords, as opposed to 50 or more for languages like Java and C#.

Simple scoping rules-

Many languages offer great flexibility to guarantee that variables and functions can be hidden from other parts of the code base. Go has only three levels of scoping and very simple conventions for determining scope:

1. Local variables (declared within a function) are scoped to the current block.
2. Package level variables are scoped to the package if they start with a lower-case letter.
3. Package level variables are publicly scoped if they start with an upper-case letter.

There’s no “private” scope. But in practice, this actually opens up a lot of opportunities.

Built-in garbage collection-

Garbage collection is a difficult issue. However, manual memory management often causes a genuinely surprising expense on the developer and makes it more difficult to write error-free code. This is the reason ,why the developers of Go made it a requirement to have it in the language. Initially, the garbage collector could negatively affect an application as far as execution. However, the cost of garbage collection is falling rapidly thanks to determined effort.

It is Powerful-

Concurrent execution is a challenge that every modern application must manage so as to exploit the today’s multi-processor computing environments. Go adopted the Communicating Sequential Process (CSP) model that’s been used successfully by Erlang. Go combines light-weight green threads (called goroutines) with communication pipelines called channels to form a simple, powerful concurrency model. This enables an application to run with thousands of actors, without the load of trying to keep shared memory free from corruption.

First-class functions-

When object oriented language overwhelmed the software development landscape, the lowly function was largely relegated to being characterized with regards to a class. With the discovery that first-class functions were one of JavaScript’s good parts, many languages have worked to restore functions to first-class status. Go absorbed this lesson and enables functions to be created and passed around the application. That doesn’t imply that it has abandoned object-orientation; rather, it mixes the best aspects of functional programming and object-oriented styles to provide greater flexibility with as little ceremony as possible.

Batteries included-

To create modern applications, you need more than just a great language. Modern applications depend on testing to ensure correctness, documentation generators to communicate how the application works and to convey how the application functions, and linters to guarantee that coding norms are clung to. Go embraces each of these issues as core concerns of the language, not simply an add-on to be dealt with by another team. As a result, installing the Go development tools provides instant access to all of these capabilities and a guarantee that they’ll work together without having to fight just to get everything working.

Conclusion-

Development with Go is different than other languages. Instead of using dozens of libraries and learning each, Go tells you to learn just a few concepts and then focus on applying those concepts to your problem.

Are you thinking to develop software with Go language? Then Solace is the right platform to start. Developers at Solace are well trained for Go development and they believe in the effectiveness of using Go. Contact us for your developing your business software advanced Go language.

Swift 3 migration for IoS app

When Apple released the newest version of its IDE Xcode 8 in June 2016, they released a beta update of Swift 3 along with it—the newest, open-source iteration of the Swift mobile programming language.

About Swift 3-

Swift 3

If you are new to Swift, just have a look to Swift vs Objective C. Apple developed Swift. It is a newer, streamlined language to better align with the platform’s evolving hardware and software. Swift allows rapid development by allowing developers to be more productive. This requires less code and also has better readability and is prone to fewer errors than its predecessors. Swift represents the direction that iOS has been headed for a while. If you haven’t migrated over yet, the Swift 3 iteration is a vital point and also a great opportunity to roll out the improvement.

Migrating to Swift 3-

How critical is to migrate existing code to Swift 3? Apple underlines that it is the right time to get on board—especially if you need to exploit all the new features in Xcode 8. This is the version that most closely aligns with the future of iOS development – migrating code from previous versions now will help you future-proof your app. Xcode 8 has a Swift Migration Assistant and a quick pre-migration checklist. Make a note that migrating to Swift 3.0 is a quite difficult. Expect that the migration tool will require some work to fix compiler errors after migration. It is not completely error-free but Apple has compiled a list of some of the known migration errors.

Apple suggests: “If you need to apply any workarounds, discard the changes that you accepted from the migration assistant earlier, apply the workarounds, and invoke the assistant manually to re-try the conversion from the start.”  

An example of something you can be proactive about changing is type names that slam with renamed Foundation types. We had a class called Operation which post-migration conflicted with NSOperation being renamed Operation. Note that you can run the migration assistant multiple times without discarding the prior-run’s changes. 

The clunkiness and also slowness of the migration assistant created a feedback loop that was too long. An example of undesirable migration assistant behavior is generated operator overloads to account for optional comparison changes in Swift 3. Here’s an example of a method that was added to the top of one of our class files:

 fileprivate func < <T : Comparable>(lhs: T?, rhs: T?) -> Bool {

  switch (lhs, rhs) {

  case let (l?, r?):

    return l < r

  case (nil, _?):

    return true

  default:

    return false

  }

}

One thing the migration assistant does very well is handle changing how we call system APIs in Swift 3 style. For example:

N SNSNotificationCenter.defaultCenter()    Became:           NotificationCenter.default

Manual Migration-

In migration guide, Apple says “While the migrator will take care of many mechanical changes for you, it is likely that you will need to make more manual changes to be able to build the project after applying the migrator changes.” At this point in the process you have to do many more manual changes. In your note-taking exercise you have to try to identify regular patterns of issues, and also sometimes you have to make regular expressions to perform a project-wide search for code matching those patterns. This  will allow you to take large steps towards reducing compile errors as opposed to fixing individual issues and trying to recompile.

Feedback Loops-

Large mixed Swift/Objective-C projects compile particularly slowly. This long feedback cycles, and you have to make larger changes between compile attempts to offset the slow compile times when we’d usually prefer to have smaller iterations. This is basically the same challenge that kept us from needing to run the migration assistant multiple times.

private -> fileprivate-

The migration assistant changed private methods and variables to file private which is functionally similar. But what we really needed semantically, when you wrote those declarations is the equivalent of the Swift 3 private access modifier. 

Underscores in Method Signatures Everywhere-

Because Swift 3 style often prefers to not label the first method parameter, the migration assistant changed methods to suppress the first parameter label. This was an improvement at the call site over patterns:

doSomethingWithModel(model: someModel)

Which became less redundant:

doSomethingWithModel(someModel)

There were, however, instances where we did not prefer the label-free conversion where the Swift API Design Guidelines suggest a first parameter label. The above example might be better as:           

doSomething(with: someModel)                                                                                                                                                                 

Optional Handling-

Optional handling from Objective-C APIs to Swift was improved, so there are some cases where the migration assistant added code to explicitly unwrap instances that previously didn’t require the ! operator. This uncovered some places where we needed to perform proper optional handling, so we refactored accordingly. There were other cases where the migrator choose to force-unwrap expressions that could not assess to optional. In those cases, removed the ! operator. We wanted to add nullability tags to related Objective-C headers, and then if that wasn’t viable, we put in explicit optional handling on the Swift side.

Objective- C Categories-

Objective- C categories were were never again certainly available to Swift, and we regularly needed to cast back to an Objective-C type to access those category methods.

Trailing Closure Syntax-

While not a compile issue, emphatically prefer trailing closure syntax for methods that take one closure, and the migration assistant is not able to generate code that way. We changed code that migrated with closure parameters:

    DispatchQueue.main.async(execute: {

            // Do something })

To use trailing closure syntax:

     DispatchQueue.main.async {

           //Do something }

AnyObject -> Any

NSDictionarys from Objective-C used to import to Swift as [NSObject, AnyObject] and now are imported as [AnyHashable, Any]. This impact the most in our JSON parsing utility where we need to change to use Any instead of AnyObject.

Open Classes-

Some of the classes were marked with the open keyword which implies that they are subclassable outside of the module they are defined in. Most of the time these class changed to be final because no intention allowing those classes to be extended or be accessible outside the module.

@escaping-

closures passed as parameters are now non-escaping by default, so basically meaning that you can’t hold onto them after the method terminates, we had to add @escaping to closure parameters in lots of places.

Collection Indices-

Instead of calling  successor() on an Index, we needed to call index(after:) on the related collection due to a change in how collection indices work in Swift 3. Collection extension helper class includes this. The migrator left us with this: 

<#T##Collection corresponding to `i`##Collection#>.index(after: i)

Which we changed to:

self.index(after: i)

Conclusion-

This has been a story about dealing with the most frightening of Swift migrations we hope it helps you in your effort.

Are you looking to develop your business software with Swift 3? Solace is able to give you the best software solution through their experts. Contact us for any swift 3 development that will give you the success that your business deserves.

Python vs Java : Know the difference

Programming Languages are an essential piece of computer science and important to almost every programming activity. Selecting the best programming language is always a confusing task. Python and Java have been fighting for the top position of the most popular programming languages. Python is making an enormous progress in the past few years whereas Java holding onto its position. Both the languages are hottest programming languages in the market due to its versatility, efficiency and automation capabilities. It has been observed that these languages are perfect and are able to do most of the tasks. But there are some key differences that will help you decide the best language.

Python-

Python is a dynamically-typed general purpose programming language. Python’s initial development started at a research institute in the Netherlands. The inspiration driving it was to make a higher level language to cross over any barrier among C and the shell. The syntax was also motivated by a few languages like Algol68, Pascal, and ABC and was intended to be readable and clean. 

Java- 

Java is a statically typed general purpose programming language. It is an object-oriented and concurrent language. Java works as WORA (write once run anywhere) language. It was designed to run on any platform and with as few dependencies as possible, with the help of the Java Virtual Machine (JVM).

Python vs Java- Key differences-

Python vs Java

1. History- 

Python, first released in 1991 by its inventor, Guido van Rossum. It is a readable, efficient and powerful high level language with automatic memory management.

Java is an object-oriented language with a C/C++-like syntax that is familiar to many programmers. Java allows new code to download and run, but not dynamically typed.

2. Performance-

First of all, languages don’t have speed, they have only semantics. If you want to compare speed you must choose specific implementations to compare with each other. Remember that performance is not only a function of the language’s execution speed, the program’s implementation, and the third party libraries performance is usually the number one factor in the equation.

3. Legacy-

Python has less legacy problem so organization faces difficulty for the script to copy and paste codes and gives it a slight edge over the other languages.

Java’s history in the enterprise and its slightly more verbose coding style imply that Java legacy systems are typically larger and more numerous than python’s.

4. Code-

It only takes 2 lines of code to read from a file in Python. This makes Python a more preferable language.

Java is very verbose as compared to Python. It takes 10 lines of code to read from a file in Java.

5. Popularity-

Popularity has always been a game between these two languages because they are close competitors in the top 3 positions of popularity. Before the Javascript release, Java was the number one most popular language. 

In 2018 developer’s survey, Python was delegated the quickest developing programming language subsequent to taking over C# recognize this year and outperforming PHP a year ago. Java is still positioned above Python being well known with 45% of developers while Python is at 39%, anyway that gap is closing. It is safe to say that both languages reside around the same area in popularity.

6. Practical Agility-

Python is most positive language for Machine Learning, Artificial Intelligence, IoT and significantly more.

Java appreciates more undeviating refactoring support than python on account of its static kind framework and all inclusiveness of IDE’s being developed. It is more popular for mobile and web applications.

Read more about Python vs Java

Python vs Java use cases-

Game Development-

Game development is a field that requires the most elevated conceivable execution to give consistent experiences to the users, and while Java and Python are not slow, they don’t provide the best performance for game development. Python is not a powerful option for game creation. There is Cocos, Panda3d, Pygame and a few other engines/frameworks for building games with Python. However, Python isn’t completely ruled out for professional game development, it’s an important tool for a game developer, as Python is a popular scripting-language option for many developers including game developers. Editing Packages like Maya also use Python as a scripting language.

Web Development-

Back-end web development uses the languages. Back-end web development is a part of web development concerned with creating the software that will run on the server. Writing your own backend technology  is extremely hard to cover all design requirements like security, reliability and effectiveness. And hence developers have created frameworks which is an abstraction in software that enables you to build your backend technology without reinventing the wheel. Know the use of Python in Artificial Intelligence, Finance etc. at our blog- Using Python in Finance, Analytics and Artificial Intelligence.

The two popular frameworks for Python are Django and Flask. Flask is a small web framework, which gives you the basic functionalities you’d need like routing requests without much overhead. Django can help you to build a powerful backend while benefiting from efficiency and security. Django is equipped with a powerful ORM layer which encourages dealing databases and performing different operations on the data.

For Java, Spring is the most well-known Java backend framework with a massive ecosystem and a huge network around it. Orange, Dell, GE, and many other enterprises uses it. Spring is not as popular as Django. It is a powerful option for building enterprise level applications.

Machine Learning-

Python is syntactically simple yet a fully-fledged general purpose programming language. It turned into a prominent choice for people from different disciplines who needed to experiment with machine learning and bring the power of AI into their respective fields. Hence AI development and machine learning development uses Python.

Java is a good option when it comes to machine learning. It is easy to debug and use also. Large-scale and enterprise level applications uses Java. Among the libraries, you could use in that area are Weka, Mallet, DeepLearning4, and MOA.  

Python and Java Comparison summary-

Java and Python are both capable and popular languages. So there are many resources available. If you’re new to programming, it will be better to use Python just because it’s really easy and uses English- like syntax. If you want to develop enterprise level applications coming from a C/ C++ world, then Java would feel pretty familiar to you.

Are you looking to develop a software for your business with Python or Java? Then Solace is the right place to start. Developers at solace believes in effectiveness of using Python in development. Get a free quote for software development using python that will lift your business to the next level.