Java vs Python: In-Depth Comparison and Analysis in 2021

Contents
    Igor Zats
    Igor Zats
    Copywriter

    Today we are going to review two titans in terms of programming: Java and Python. Both of these languages are widely used and can have a lot of advantages and disadvantages. In this article, we are going to compare their main aspects and determine in which situation each is better.

    Contents

      What is Java?

      Java is considered one of the most widely used programming languages of all time. Basically, we are talking about a general-purpose, object-oriented language with class and concurrent basis. The language is almost 20 years old, and it is continuously evolving. As a result of such an effort, you can check out Autoboxing, Generics, Enum, and more. No doubt, many programmers from all around the world believe that Java is a masterpiece in the world of programming languages.

      Back in June 1991, Mike Sheridan, Patrick Naughton, and James Gosling started a new programming language project. Interestingly, this project was originally oriented for interactive television, but soon it was obvious that the final result was too advanced for the TV industry in 1991. Java was called Oak, after the tree outside James’ office. After that, it was renamed as Green, and the final name was Java, after Java coffee. It was originally designed with a C and C++ syntax, which is familiar for app and system programmers.

      Where is Java used?

      Where can we use Java? According to Oracle, there are over 3 billion applications, websites, and devices that run Java. Spectacular number, isn’t it? That basically tells us that Java runs some pretty relevant and useful applications. Let’s learn about the most widely used Java-based applications:

      • Web Application Development. Java is heavily used in web application writing, as well as for eCommerce websites and applications. Since eCommerce has become more and more popular, Java has become more popular as well. Judge for yourself: Youtube, Netflix, Google… it seems that the list is endless.

      • Android Application Development. It’s not a secret that most of your Android applications are written in Java. You can guess what that tells us about the current potential. A wide range of applications are available in PlayStore, such as Facebook, Linkedin, Whatsapp, etc.

      • Java and Big Data. Today every big company has access to a large database. Hadoop (big data management tool) allows you to use data in a variety of ways. In other words: Elastic Search, Apache Spark, Hbase.

      • Other Areas. As a developer, you can meet a wide range of different software tools that will make work with your project much easier.

      Let’s talk about other advantages of Java and (unavoidable, to be honest) disadvantages.

      Strengths of Java

      No one argues that Java is an extremely popular programming language used in a variety of different areas. Let’s sort out why people love it so much.

      1. Object-Oriented Approach. With such an approach, developers can make modular programs with reusable code, which keeps the system flexible and extensible.

      2. Learning Curve. Due to the fact that the learning curve is short, you can easily debug, compile, and write with Java. It is much easier than in the case of other widely used programming languages, like Objective-C, C++, and C#. This is an incredible benefit: as a programmer, you can become much more productive in a short time. Furthermore, Java comes with a familiar English syntax. Due to this fact, it is much easier to understand the logic of your code.

      3. APIs. With Java, you can get access to APIs for any purpose, whether it’s XML parsing, networking, database connection, utilities, I/O, etc.

      4. It is free. This point is hard to miss. When you are working in the development area, you are faced with huge competition. Thousands of small and medium enterprises don’t have a big budget, but they want to complete web or application development for their business. Due to the fact that Java comes with free access, that can be a huge benefit for the business.

      5. Open-source Libraries. There are numerous open-source libraries that fit industrial support, so Java can be used literally in any area. A wide range of these libraries has already contributed to such notorious companies as Facebook, Yahoo, Apache, Google, Linkedin.

      6. Open-source Development Tools. Java provides Eclipse and Netbeans, which are powerful, efficient options for a developer. These platforms are basically making Java’s name in the world of programming languages. With them, coding and debugging become much simpler.

      7. Community. Java’s community is immeasurable. You can easily find the solution to your problem, you can learn from experienced programmers, and so on.

      Weaknesses of Java

      Advantages are always accompanied by disadvantages. You can’t run from this fact. Same goes with Java.

      1. Specification and implementation are not separated.

      2. Poorly optimized compiler if you compare it to C++, for example.

      3. Quite expensive memory management.

      4. It is possible to find some bugs in example programs and browsers.

      5. You can face some limitations while you create data structures because of the lack of templates.

      Let’s keep digging deeper by taking a look at some Java libraries!

      Top Companies Using Java

      According to iDatalabs, there are 164,679 companies using Java!

      Let’s take a look at the most notorious companies.

      1. StackOverflow. This company runs probably the most visited programming websites. Here you can find numerous answers and questions.

      2. Dzone. A pretty fun programming oriented website with a lot of developers.

      3. Leetcode. A social platform for IT interviews.

      4. GitHub. It is one of the most advanced repositories on the web and is a perfect place to learn Java frameworks and libraries.

      5. Coursera. You’ll never find a better site for video lectures (just kidding). Here you can find well-made courses related to computer science from top schools and professors from all around the world.

      Top Java libraries

      You are free to use a seemingly endless amount of libraries. If you want to save your precious time when it comes to searching for the best libraries with the best documentation, support, and updates - we can help you!

      We’ll start with Standard Libraries. Sounds too basic? Wrong! There are a lot of people who underestimate the real power of standard libraries, especially when it comes to Java. Here is a top list of the best standard libraries:

      Java.util. With this library, you can find every data structure or collection that is available.

      Java.net. This is ideal when it comes to working with sockets and network applications.

      Java.math. This one provides great functionality so you can work with integer and decimal values.

      Java.io. With this library, you can work with streams, pipes, and read files.

      Furthermore, Java comes with java.nio. You can consider it as the alternative to the previous one for nonstop I/O. It is a perfect solution for the input/output operations.

      What else? We have something useful for GUI - javax.swing. Basically, it is an extension for java.awt.

      If you want to create some midi files or even play music - use java.sound.

      Now, let’s talk about more specific libraries:

      1. Maven. This is a good example of a tool that helps with software project management. You can build whole enterprise applications (in theory) with this library. This library really helps with configurations and project dependencies management. You can build documentation or configurations. All you have to do is just to specify them in one single pom.xml.

      2. Guava. Now we are entering the Google core libraries section, and this is a must-have. With Guava, you can extend some of the basic Java functionalities. If you are looking for something that will help you (we bet you do) to order collections and compare the contents on an advanced level with multiple conditions and columns - Guava is your solution.

      3. JHipster. This development platform helps generate, deploy, and develop Spring microservices and web applications with Spring Boot + Angular. JHipster can save you weeks or even months of work. This library can generate the whole app from the frontend to the backend. All you have to do is to add some human business logic into the architecture.

      4. Google-Gson. With this library, you can easily convert JSON to Java Object. It can be really helpful when it comes to mobile app development, along with using (or creating) REST API.

      5. Slf4j and Log4j. When it comes to logging - these libraries are worth attention. The first one represents an abstraction for Log4j (or other logging frameworks), which is simple and handy.

      6. Hibernate-ORM. This object-relational mapper is used in data persisting in relational databases. As a developer, you can perform such operations much easier. By the way, Hibernate-ORM uses JDBC in the implementation and basically, it is an implementation of a specification called JPA. 

      7. Mockito. With this mocking framework, you can write tests and create mocks with a simple and clean code.

      8.  JUnit. It is a Java framework with free access that was designed to perform writing unit tests. But note that it could lead to some problems if you overdo it with the numbers (% of the code).

      It looks like we’re quite familiar with Java at this point. What about our second competitor?

      What is Python?

      Python is an interpreted, advanced dynamic programming language for general purposes, which is frequently preferred over C++ and Java. Due to its focus on simplicity and readability, this programming language found its use in such areas as education, science, data science, web development, game development, and so on. Today we can see a lot of big companies like Instagram and Google commonly using Python in their object-oriented functional and viral programming. Furthermore, Python gained its reputation because of its comprehensive nature and a huge standard library that offers vibrant features and automatic memory management.

      Let’s take a look at the pros and cons that come with Python!

      Strengths of Python

      Python is widely used in software development companies for prototyping, language, applications, web frameworks development, and gaming.

      1. Integration feature. With Python, you can easily develop Web services with COBRA or COM components due to the integration with the EAI (enterprise application integration). Furthermore, it gives you powerful control because Jython performs calls through Java, C++, or C.

      2. Programmer’s Productivity. Python comes with extensive support libraries and a well-made object-oriented design. Due to this, the programmer’s productivity can be significantly increased while using languages like VB, C, C#, C++, Perl, and, of course, Java.

      3. Standard Libraries. Python comes with a lot of standard libraries that touch such areas as web service tools, Internet, string operations, OS protocols, and interfaces. Python scripts already include most parts of highly used programming tasks, so the length of your code can be notably shorter.

      4. Productivity. Python offers a strong range of unit testing frameworks, integration features, and control capabilities. So most applications can be much faster and productive. For example, you can build great multi-protocol network apps with great scalability.

      Weaknesses of Python

      As we can see, we’re dealing with a pretty serious snake. However, it doesn’t mean that this language doesn’t have weak points. Let’s check out the most obvious weaknesses.

      1. Weak Mobile Computing. Python built its name on server and desktop platforms. However, when it comes to mobile computing, the situation is not that sparkling.

      2. It is too difficult to use other languages because people that get used to Python face some problems when it comes to working with or learning other programming languages.

      3. It can be slow from time to time. The thing is that Python executes with the help of an interpreter, not a compiler. This may cause deceleration because execution and compilation really help with performance stability. It’s still pretty fast for most applications, though.

      4. Runtime Exceptions. This language is dynamically typed, and there are a lot of design restrictions that are reported here and there. It even looks like Python needs more time for testing, so an application can raise an error when it’s actually running.

      5. Database Access Layers Issues. If you compare Python to ODBC or JDBC, you’ll note that Python’s DB access layer is pretty primitive and underdeveloped. However, you can always apply it in the enterprise for the smooth interaction of the complex data.

      Top Companies Using Python

      According to iDatalabs, there are 74,252 companies that use Python one way or another. Let’s take a glance at some top-tier companies that you (most likely) know that use Python!

      1. Industrial Light and Magic. It’s a famous special effects company founded in 1975 by one guy, what’s his name… George Lucas! The company is mostly used to create special effects for the Star Wars series. Besides that, this company is associated only with FX. ILM uses Python 1.4 instead of Tcl and Perl, due to the fact that Python is super fast in terms of integration with the infrastructure. It easily works with C++ and C, so ILM easily imported Python into their software.

      2. Google. Maybe you didn’t know, but Google has been the number one supporter of Python from the very beginning. As the founders of Google mentioned years later, they “C++ where they must, Python where they can”.

      3. Facebook. Production engineers prefer Python as the number three programming language at Facebook (just after C++ and Hack (PHPs dialect). There are more than 5,500 services and utilities that manage binary distribution, whole infrastructure, operational automation, and hardware imaging.

      4. Instagram. Back in 2016, the engineering team mentioned that their platform is running with Django, and as we know, Django is entirely written in Python. According to an Instagram software engineer: “We use Python due to its simplicity, practicality, and reputation, which perfectly aligns with Instagram’s philosophy ‘do the simple thing first.’”

      5. Spotify. This music streaming platform is a big fan of Python, and this language is used mostly for data analysis and backend services. You can see a lot of services, all of which are communicating over ZeroMQ. This is a library that is written in C++ and Python.

      Top Python Libraries

      Let’s check out some good examples of fresh Python libraries and where you can use them in real life. 

      1. Pipenv. It’s a brand new library that can affect each aspect of the developer's workflow. Today it is officially recommended on Python.org for those who need to perform dependencies management.

      2. PyTorch. This library gained spectacular popularity during this year, especially in the Deep Learning area.

      3. Pendulum. This library can make your life much simpler, especially if you are working with datetimes in Python. It replaces the datetime class, and you can integrate it with the current code and modify its functionality whenever you need it. 

      4. Caffe2. This year Facebook released another DL framework. The framework called Caffe has been used for many years. However, recent trends in the DL made Caffe a little bit outdated, so Caffe2 is attempting to give a second life to Caffe.

      5. Dash. If you are working with data science, you are probably familiar with a wide range of available tools in Python’s toolkit (scikit-learn, Pandas, etc). Your workflow is supported by Jupyter Notebooks, so you and your colleagues can feel happy. But what if you (or your colleagues) don’t know how to work with these tools? Dash is the answer! This open-source library was created in order to simplify web application development, specifically when it comes to data visualization.

      Java vs Python: Step-by-Step Comparison

      So, it looks like we’re pretty familiar with both of our programming languages. These two languages have non-intersecting use cases and support communities, so it won’t be easy to choose the winner in a Java vs Python type of battle, but at least we can try!

      What can we do? We can inspect their differences, similarities, compare their advantages and disadvantages on the battlefield, etc.

      Let’s start with Performance!

      Performance

      Some say that “slow” is open for “interpreted.” You probably heard these familiar words from Java users, “You can write it in Python or any other programming language. Anyway, you have to rewrite it in Java if you want your app to scale.”

      Why is it so difficult to compare the speed of programming languages? The answer is the environment. A developer can create some tests that compare different languages in a lab setting, but it’s hard to call the result accurate. The result that matters is that which you can see in the actual field. Furthermore, if you want to feel the real difference between Java and Python’s performance, you have to consider such issues as the coding style, libraries, etc. The thing is that Python supports a couple of coding paradigms. Thus, if you compare Python performance (drawing on Python applications that are written with a functional paradigm) with an object-oriented paradigm, you’ll get results that will differ.

      Also, we have to consider how one or another version of the programming language affects application performance. In order to avoid outdated information, let’s consider the latest versions in our article. In Python’s case, 2.x is typically slower than 3.x, providing that the application is written with specific libraries that optimize Python 3.x. At first glance, it might seem counterproductive, but many data science programmers still prefer Python 2.x in order to have access to particular libraries. The whole Python community has struggled (and is still struggling in some cases) with the transition from version 2 to 3, so they keep maintaining both editions.

      Benchmark

      Let’s take a look at some performance benchmarks that will show which programming language is faster in certain conditions:

      pidigits:

      SourceSecsMemoryGZCPULoad
      Python15,57439,95551227,9826%, 93%, 33%, 33%
      Java10,55609,92293031,7573%, 79%, 73%, 84%

      regex-redux

      SourceSecsMemoryGZCPULoad
      Python3,5210,523873,521%, 1%, 0%, 100%
      Java3,1637,19353,374%, 4%, 1%, 98%

      reverse-complement

      SourceSecsMemoryGZCPULoad
      Python16,761,005,24081420,0866%, 22%, 45%, 18%
      Java3,18672,26521757,1768%, 47%, 44%, 78%

      Readability

      As we know, Java is a statically typed programming language, and Python is dynamically typed. What could go wrong with static typing? Well, you have to declare every variable name along with its type. This is because Java containers only hold objects in the generic type. If you want to save an object from the container, it will not remember the type, so you have to call the variable explicitly with the desired type.  In the case when you assign the wrong type of one or another variable, you’ll get the exception.

      With dynamically typed programming language, you can forget about such pitfalls. Python’s container can hold any object with any type. E.g., if you save an object from your container, it will remember the type of that object. While working with Python, you have to declare each and every variable. In this case, the assignment statement binds the to an object automatically, regardless of the inherent type.

      Furthermore, the assigned name of one type can be assigned to the object of a different type. Such specificity makes programmers work faster and simpler.

      What about the length of the code? Basically, with Python, you can express the same thing as with Java, but the code will be shorter. Obviously, a longer code will take more time to write, and the longer it gets, the better chance of having errors and bugs. Thus, coding in Java is more time consuming than in Python.

      However, Java shows better results in cross-platform portability. Each script that is written in Java is compatible and portable with a wide range of platforms. You can port Java code from Windows to Android, for example. How is this possible? The answer is in the Java virtual machine. You can run JavaScript on a platform if you have JVM on this platform. Meanwhile, Python is not a cross-platform programming language. If you have a Python code that is written in windows, you cannot port it to any other platform. Of course, you have the embedded interpreter for this case, and you can achieve the same goal doing it with Java.

      If you need to maintain and update codebase, you’ll have to put extra effort and time to achieve this in Java. However, you can do it easily with Python due to its readable and reusable code. That’s how you can save a lot of time as a developer.

      Scalability

      What is scalability exactly? It is the ability of the application to perform more requests after adding computational resources. There are 2 types of scalability: horizontal and vertical.

      • Horizontal scalability is when the code is able to process a bigger number of requests after adding a cluster (the set of computer systems) to a set of clusters that run the app and manage the processing requests.

      • Vertical scalability can be achieved by adding additional computational resources (CPUs and RAM) to a single system. Despite the fact that it is easier to achieve, it’s pretty limited.

      Imagine developing software that takes in free-form text, splitting into sentences before doing some kind of grammar analysis. Lexically, each sentence will be split by a period followed by one or more whitespace characters:

      What about Java? Well, it does not differ a lot. It’s not worse, but it’s not better than other languages. You can increase the number of requests that your system will handle, but it depends on the architecture of the current system (avoid database, caching, sharding, etc.). With Java, it is better to scale a medium-size system due to its culture. Why? Because there is a number of techniques that do not scale that well:

      • Vertical scaling

      • Large components

      • Favors multithreading

      • Multiple layers and tiers

      • Shared state

      Java performs very well, and excellent performance means good scaling right out of the box.

      Finally, let’s talk about portability. Python is a compact language because you can implement its codes with more agility and fewer, while Java is not that compact due to its longer code.

      Automation testing

      It seems like Python is the best programming language in terms of test automation. Its conciseness allows a programmer to easily understand the essence of the current test case. Furthermore, Python provides a lot of test support packages. Well, Java is good as well - you have a rich set of packages and tools; it is also integrated continuously with ANT/Gradle/Maven and Jenkins. You can also consider Ruby due to the fact that it works similarly to Python in terms of automation testing.

      Which one is better? Well, as always, it depends on your case, and the case is the specificity of a domain. JavaScript, for example, is not doing well for general purposes, but it acts great when it comes to pure web application testing. JavaScript is great for testing for MEAN stack development. And don’t forget about TypeScript, which could be even better due to its scalability and safety. If you are working with Microsoft shops - C# is great; it provides super test support, but it depends on the Microsoft software. What about .NET development tools? Well, not all of them are available for free, and the command line operations can be really tough.

      When it comes to other languages, the situation is quite narrow. They shouldn’t be used, despite the fact that they could be used for automation purposes. For example, Perl is not safe. It doesn’t provide self-documenting code with good scalability and structure. C++ and C are not so convenient because you will deal with a few low-level robust frameworks. The list goes on.

      Below, we rated eight points for the most popular languages:

       PythonJavaJSC#C/C++Perl
      Usabilityperfectgreatgreatgreatpathetichorrible
      Elegancyperfectgreatfinegreathorriblehorrible
      Test Frameworksperfectperfectperfectgreatfinehorrible
      Packagesperfectperfectfinegreatgreatgreat
      Command Lineperfectgreatgreatfinehorriblefine
      Build Integration Simplicitygreatgreatgreatgreathorriblehorrible
      Available IDE Supportgreatperfectgreatgreatfinepathetic

      As we can see, Python is the best choice for automation testing. Of course, it does not meet each and every requirement, but there is no possible way to find a better option.

      Security

      When talking about cybersecurity, your code should meet different scenarios and provide reusability, libraries, speed, and functionality. Python supports such scenarios, so system administrators or testers can develop customized tools for different purposes. It’s not a secret that hackers, testers, or any other security experts need a library that will provide only the most powerful and useful feature for his or her application.

      Python comes with built-in modules that can easily help with such web activities like XML and HTTP building clients. Due to syntax’ simplicity, the code can be simpler and clearer with a good extension. Python’s versatility is number one in terms of cybersecurity. With Python, you can create compiled and complex applications, and the scalability makes it the best choice for a variety of coding needs within your company.

      A lot of cybersecurity specialists are working with the existing code. With Python you have an ability to use the existing code, thus saving time, providing great security possibilities, and preserving existing functionality.

      On the other hand, the brand new Java release is, if not stronger, at least it is not weaker than Python. The new release comes with a stronger algorithm and lower sized keys. Its code environment perfectly combines with a variety of programming languages. Just like with Python, you can embed your code that is written in Python in Java without any problems. Java comes with a built-in security API, which uses such programming approaches as dependency and context injection so you can simplify your security API in all platforms. The new Java comes with improved application portability in terms of self-contained security.  Excellent libraries are also worth mentioning, with which developers can create the app without an effort.

      Frameworks

      Let’s talk about the part without which it is impossible to compare the two programming languages — “Frameworks.” Both Java and Python provide high-quality frameworks for different purposes. Let’s begin with Java and check them out!

      1. JavaServer Faces or JSF. This framework is supported by Oracle and comes with highly-detailed documentation. You can do anything you want with this framework when you work with the Java programming language. We definitely recommend you to read this documentation.

      2. Struts. This open-source framework is available for free, and you can create elegant Java applications with an aesthetically pleasing design. Due to its Model View Controller pattern, the developer can make the foundation for web applications. Struts is not that widely used like JSF, and the support is also weaker, but it is still worth mentioning, though.

      3. Hibernate. If you are dealing with database accesses, you need a powerful and high-quality solution, and this ORM framework can help you. Hibernate a great device for object-relational mapping. In terms of computer science, you can convert data between two incompatible systems. Furthermore, these systems can work with each other through programming languages with object-orientation. Of course, it is possible with Java.

      4. Google Web Toolkit. This toolkit is provided by (surprise) Google. With this open-source framework, you can modify and develop frontend applications that are built with Java, and forget about their complexity.

      5. Vaadin. This open-source framework is licensed by the Apache Software Foundation, a nonprofit company that is aimed at the creation of tools for languages such as C++ and Java. This framework is powerful, useful, and supported by a wide community from all around the world.

      Now, let’s focus on some hissing frameworks, provided by Python:

      1. Django. This is the most popular web framework for Python. How to describe the Django? It is the full-stack, high-level web framework that significantly simplifies the development of complex, large web apps due to its robust features. The Python core-team is constantly evolving it to meet the latest trends in terms of web application development. Django’s features help the developer accomplish such web development tasks as user authentication, sitemap, RSS feeds, content administration, and so on. It is also a pretty secure web-framework. There are a lot of inbuilt security features, so it prevents cross-site scripting, SQL injection, clickjacking, and cross-site request forgery. And last but not least, Django can help you scale your website quickly, which is really handy if you face an unexpectable spike in traffic.

      2. Torch. It is a scientific computing framework with great support for a wide range of machine learning algorithms. Torch is based on Lua and is widely used among notorious giants, like Twitter, Facebook, and Google. It uses C and C++ along with CUDA libraries for processing purposes. Basically, Troch was made in order to bring the solution for scaling the production of building models and flexibility.

      3. TensorFlow. It is an open-source project for machine learning, production, and research. It offers a number of APIs both for experts and beginners, so they can develop web, desktop, mobile, and cloud applications.

      4. Selenium. This framework is basically a code structure, which helps make code maintenance simpler. Selenium helps produce a wide range of beneficial outcomes, like higher portability, code readability, code re-usage, and reduce script maintenance cost.

      5. Apache Hadoop. Basically, it is a collection of open-source utilities that improve using a network of several computers in order to solve different problems, including massive computations and work with big data.

      Now we are familiar with both Java and Python frameworks for different purposes. Another touchstone by which we will compare our programming languages is open-source projects. We’ll start with Java, and then we will take a look at some open-source projects built with Python. Hit the road Jack!

      Open-source projects

      1. Java-design-patterns. It seems like people haven’t talked about this project for years, but it is constantly improving! Design pattern helps developers solve basic problems while busy with designing a system or application. It helps to speed up the development process and provides proven (and tested) development paradigms. Furthermore, if you reuse the pattern, you can prevent some misalignment problems from becoming a much bigger problem at the end of the project.

      2. RxJava. It is one of the most famous Java repos available on GitHub, as well as a popular framework in the past. RxJava is based on ReactiveX API. Basically, it is a library for composing event-based, asynchronous programs.

      3. Atom. It is a hackable text editor that you can find on GitHub.

      4. MpAndroidChart. It’s a brand new, simple, and powerful way to use chart libraries related to Android development, which runs on level 8 (and higher) API. You can draw different graphs for applications that run on Android.

      What about Python?

      1. PyTorch. This open-source project provides Dynamic neural networks and Tensors in Python with a strong GPU acceleration.

      2. Sanic. A simple Async web server that is written to work fast for Python 3.5+.

      3. Python-fire. The library for automatic generation of CLIs (command line interface).

      4. spaCy 2.0. Industrial oriented NLP (natural language processing) built with Cython and Python.

      5. Pipenv. A great Python development workflow built from humans for humans.

      Let’s compare our Python vs Java on the Big Data battlefield!

      Big Data

      Those who consider themselves as beginners in the world of data science are facing a variety of different technologies that will help master this area rapidly. The problem is that most likely you have to choose only one option among the available programming languages according to your goals. Let's consider which language will help you achieve your goals. Python and Java are considered the top technologies for big data solutions. 

       

      First, let's figure out the prospects of big data in the next ten years. According to data from statista.сom, within ten years, the volume of Big Data revenue could reach $103 billion. Back in 2011, the revenue was only $7.6 billion. Furthermore, we can expect these 5 trends of info development in the next ten years:

      1. Safety will be paramount. Thanks to a number of precedents, the safety of personal data is the core goal today. According to a number of researches, we could see a huge gap between the size of the big data and its security.

      2. Internet of Things. Such growth can lead to the state of affairs when every person on the planet would collaborate with his or her device, connecting with the network almost 4,800 times a day.

      3. Today data is the most precious property, instead of just a “context” for your business. It is expected that by 2024-2026, approximately 20% of data will play a significant role in our life.

      4. A huge chunk of data creational source will be automation.

      5. Instant mobile data. It is expected that by 2025, one-fourth of data will be generated in real time. Of course, almost 100% of this data will be generated from IoT devices.

      As we already know, some of these predictions will be implemented by Java or Python. Large and middle-sized companies are looking for Big Data solutions due to the fact that they are desperate to have the best solution so the information can be collected and stored.

      You can consider Java as the programming language that meets all the requirements of Big Data. There is a wide range of companies, where the ability of integration of the finished code in the existing base is valued. 

      On the other hand, Python is a powerful solution, as well. Basically, 90% of data science is connected with Extract-Transform-Load (ETL). Due to this fact, Python can be the best match for your purposes. Thanks to such libraries like TensorFlow that was designed by Google, you can have a lot of fun and spend your time with interest while working with machine learning.

      Coding for data science requires a strong understanding of the project’s goals. This will help choose the language that is best suited. Let’s compare Java and Python in the following table:

       

      Java

      Python

      Type

      General purpose.

      Write once, run everywhere.

      High level.

      Code readability and short syntax.

      Compilation

      Easily complies on any platform.

      Easily complies on Linux.

      Productivity

      Less productive than Python because of the need to define each of the variables.

      Fewer lines. 5-10 times more productive than C++ or Java.

      Types

      Statically typed. All variables must be explicitly declared.

      Dynamically typed. Developers don’t have to declare anything.

      Speed

      Java is 25 times faster than Python in games.

      Is not as faster as Java.

      Distribution

      Due to its popularity, Java software is easy to distribute.

      Python is slower than Java. That’s the reason it is not so easily distributed as Java.


       

      Data science

      Python and Java are widely used in the research and scientific communities. But Python will be also preferred. Why? Because it comes with simple and easy to use syntax. So it can be easily adapted for people without an engineering background. It is also well-suited for fast prototyping, which is good. Furthermore, most online courses related to data science give preference to Python due to its simplicity for beginners.

      Python wears a funny nickname, “swiss knife” among the data science community members because of its versatility. It’s not so hard to understand why - Python is the most sought-after programming language, and this is the quality that data science companies (and analytics professionals) are looking for.

      What’s next? Deep learning frameworks! According to engineers reviews, Python provides them with its APIs, and let’s not forget about the scientific packages that constantly come from industry and academia. This environment makes Python spectacularly versatile and productive. In the last two years, there was a revolutionary breakthrough in deep learning frameworks that was written in Python, and as a result, we saw TensorFlow.

      Database-driven applications

      Python, R, and Java are the holy trinity for statistical analysis and data science. In terms of data science, Python is perfectly combined with R's focus on the way of execution of complex statistical functions and Java’s ability to build high-traffic applications.

      But the most important part of this list is the number of libraries. With them, it is super easy for a developer to perform statistical analysis and machine learning tasks without rewriting code lines. Some of these libraries contain useful tools for data visualization and manipulation.  You can consider NumPy, matplotlib, and SciPy, for example.

      Imagine the amount of data that YouTube, National Web Service, and New Your Stock are faced with? And there is the only one snake that unites them.

      Machine Learning

      Things are not that good for Java when it comes to machine learning either. According to current statistics, only 15% of ML scientists prefer Java and JS, while in the case of Python, there are 44% more. However, if you are looking for somebody who is working on fraud and cyber attacks detection and network security, Java is preferred, while Python and R are the least prioritized.

      When it comes to big companies in the financial sector, Java will always be preferred for fraud detection algorithms and network security. If an area is not that enterprise-focused, such as sentiment analysis or NLP (Natural Language Processing), developers prefer Python, because of the fact that this language offers a fast and simple way to create complex algorithms. Once again, it is possible because of the extensive variety of libraries.

      Game development

      When it comes to web development, the task could be more difficult in C++ or Java than in Python. The thing is that Python comes with such libraries like Pygame, Tkinter, and PyOpenGl, which can be pretty helpful in the work process. However, game development with Python implies a significant disadvantage. Despite the fact that developing games with Python is faster, the final game tends to be slower than a game developed in C++. It is possible that Python developers will remove such a weak point in the future, but for now, we have what we have. Java comes with open-source engine jMonkeyEngine which is pretty good as well, but there is no way it can compete with the UnrealEngine.

      If you are interested, there are some popular video games built with Python:

      • PUBG

      • Fortnite

      • Civilization IV

      • EVE Online

      • World of Tanks

      There are only a few games built with JavaScript. Java provides managed, garbage collected runtime, which is pretty cool. Most of the time, this is a big advantage: the coding is simpler, less exception-prone, and that’s why it is actually pretty popular. However, Java in game development is not without a fly in the ointment, due to the high chance of causing latency issues for games that will lead to annoying pauses. This can be fixed to be at least less of an issue with the fresh low-latency JVM, but the problem is that graphically oriented games where high FPS should be maintained are critical.

      Let’s take a short glance at video games built with Java:

      • Football Tycoon

      • Might and Magic Mobile

      • Brothers in Arms: The Art of War

      Web Application Development

      Java and Python are widely used in the web development area, which entered the golden age. But which one should you pick up? First of all, it depends on your project, budget and the approach that will be provided by the programming language you are looking for. Let’s start with Java and check out some projects that can easily rely on Java and its advantages!

      • It is a great solution for big projects. If you want to build a project that implies developing a scalable, large system, you can count Java without a doubt.

      • The performance is supreme. Some of the notorious sites like Twitter, Facebook, and LinkedIn are a good example of using Java as a scripting language that can bring swift solutions to the users over the Internet.

      • JVM. It is an environment that brings a lot of customized tools for development purposes and makes it possible to implement the development that demands a functional and object-oriented programming approach.

      Java can be a perfect solution for web development and is a brilliant choice to develop a project that demands high efficiency. 

      So, what about Python?

      • If your project demands the implementation of technology that is outdated, it is a perfect time to pick up Python. Since it possesses lesser legacy issues, organizations often prefer Python to keep their IT infrastructure fresh.

      • Python is the best option when it comes to workflow integrations. Python provides flexibility and remains one of the most preferred programming languages among web developers.

      Scraping

      Both Java and Python are used in web scraping. They both provide different web crawler/scraping tools. Java provides such tools as Google web scrapers and Yahoo pipes.

      You can use Jaunt - an API that contains some useful library packages for web scraping, JSON queries parsing, and automation.

      Another option is Selenium webdriver. Most of the time it is used in automation testing, but you can extract some information from websites, as well. You can set up the web scraping so it can work in automated mode.

      What about Python?

      Python provides two great solutions with which developers can build simple, fast, and efficient scrappers. We are talking about Beautiful Soup and Scrapy. Let’s learn about them!

      Beautiful Soup - this is a parsing library that can work with a variety of parsers. A parser is a simple program whose purpose is data extraction from XML and HTML documents. BS’ default parser is available in the standard library. Despite the fact that Beautiful Soup is a little bit slow, it is flexible and not so strict to mistakes. If you want to improve the speed, you can just swap the slow parser with a faster parser. Beautiful Soup helps developers navigate a document and find the data that is needed. Because of this, work on the common application can be done quickly and with less pain. How is it possible? Let’s take a look at a simple example. Assume that you want to find each link in the web page, it will cost you only a few lines of code:

      In addition, BS4 can help you navigate a parsed document and find what you need. This makes it quick and painless to build common applications. For example, if you wanted to find all the links in the web page we pulled down earlier, it’s only a few lines: 

      Pretty simple, isn’t it?

      Scrapy is something more than a regular library. It is a solid web framework for scraping. With Scrapy, you can operate with redirects, handle output pipeline, preserve sessions, and manage requests. With Scrapy, you can also swap modules with different web scraping libraries provided by Python.

      You can build a simple, working, and efficient spider, like this one:

      That’s pretty much it. Let’s figure out the situation in the worldwide market arena.

      Market Share

      In this section, we are going to compare Java and Python market share generally and particularly. Once again, we start with Java:

      According to idatalabs.com, there are 164,670 companies that prefer Java. It is mostly used in the United States (64,823), India (11,140), and the United Kingdom (9,987), mostly in the Computer Software area. It is mostly used by companies that hire from 10 to 50 employees with the revenue that varies from $1,000,000 to $ 10,000,000.

      Python takes only 2.49% of the market, which is 7% less than Java's share. There are 74,252 companies that prefer Python. It is circulated in the United States (32,396), United Kingdom (4,938), and Canada (3,372). And just like Java, it is popular in the Computer Software area, but not that popular in the Information technology and services.

      Job Market & Salaries

      According to UpWork.com, the situation of the job market is looks like that (at the time of writing this article): 

          

      Java

      Python

      Jobs found

      747

      1,053

      Budget: less than $100

      99

      139

      $100 - $500

      120

      163

      $500 - $1,000

      42

      53

      $1,000 - $5,000

      61

      75

      $5,000 +

      17

      21

      Developers found (hourly $)

      125,816

      (120,421 independent freelancers \ 5,395 agencies)

      64,671

      (62,520 independent freelancers \ 2,151 agencies)

      $10 and below

      39,761

      14,021

      $10 - $30

      58,593

      30,833

      $30 - $60

      18,707

      13,610

      $60 & above

      8,755

      6,207

      As we can see in the average, there are two times less Python developers, but their average salary is 10-20% higher.

      For interviews

      We made some helpful tips that can help you find a job and pass the interview. Moreover, you can learn about the current situation in the job market and understand what the average employer is looking for.

      Speaking of Python:

      • Watch and learn the interview tips from top Google engineers and their recruiters. Not every company takes such an effort to highlight their interview approach and process.

      • You can follow the Udacity course in order to prepare yourself for a technical interview.

      • There are a lot of succinct and useful tips by Philip Guo, and you can find a lot of other sources over the internet.

      • Forget about the clickbait articles from Forbes and BusinessInsider. It’s just a waste of time.

      • Focus on the Google interview university guide. Don’t take it too literally, though.

      Stick to one time-tested interview platform and work it out step by step. We can recommend leetcode.com. Forget about resources that focus only on programming contests. During the actual interview, you have to ask questions. You have to. It doesn’t matter how insignificant these questions can be. You have to validate assumptions of complexity and data structure first. Try to practice each problem and assume that it is the actual interview. Don’t rush, because you have to achieve your goals, not finish it first (if it comes to programming competition).

      And finally, most likely you’ll face one or more of these problems on the interview:

      • Number manipulation

      • String/array manipulation

      • Graph structures

      • Tree structures.

      Speaking of Java, always be honest about yourself and tell your interviewer that you already have a C++ background and you are starting to learn Java. Tell about the things that you know the best, e.g. design patterns or generics. Check out a few OOP topics similar in C++ and Java. If you’re applying for a job and want to take a chair as a J2EE programmer, you already have to know about the concepts of transaction management, ORM mapping, dependency injection, logging, and so on.

      JAVA

      Which language to learn?

      It looks like we are creeping up to a tough question. Both of these programming languages come with their unique pros and cons. You can run Python on more than 10 operating systems. On the other hand, Java opens the gate of perfect cross-platform support. Once again, you have to determine what you expect from each language. Know your end goal before you make your decision.

      However, Java is not the number one language for beginners, due to the fact that it is intended for more complex development. With Python, the situation is not that critical. It is a more forgiving language, with which you can take such shortcuts as reusing an old variable.

      What’s next? Many people find Python more understandable and readable than Java. On the other hand, after you wrote a code with Java, you can execute it from anywhere. A notable benefit to the Java language is that you can keep questionable code’s host system safe as the code runs in a secure environment. Let’s not forget that Java is network-centric, so network-based application development is not a problem.

      You have to understand that your choice should stand on your determination, preferences, and current background. If you are already familiar with Python, you probably want to amplify your knowledge before focusing on Java. If you consider yourself as a person with a strong will and a lot of time, learning Java can be a good idea. With Java you can program for multiple environments.

      Learning Curve

      As we mentioned before, the learning curve for Python or Java depends on the things you already know (or don’t know), learning the environment, and intended language used. If you are already familiar with JavaScript, C++, or C, learning Java will be not that tough or troublesome as learning it from scratch. Especially if you are writing application code.

      On the other hand, if you are not familiar with programming and find yourself as the programmer working in the scientific area, then Python is the best decision. It is the number one programming language in schools due to the fact that Python helps people master new coding techniques in a short time with a wide range of uses.

      Future and the Bottom Line

      Both Python and java are excellent, time-tested languages with a bright future. The prospects of their future are more a matter of your own preferences. Today we can find enormous numbers of enterprise software written in Java. Big players rely on Java for a good reason, so the demand will remain strong for decades. However, to be honest, Java is not the prettiest programming language in the deck. Still, it’s well-engineered, solid, and secure with a huge number of different frameworks and a huge support community. Furthermore, Java acts friendly with Android development, and this OS will be in the game in the predictable future.

      On the other hand, Python is a language designed to be as easy as possible to learn. It is a number one language to learn for kids, but at the same time, it’s not just a toy language like Scratch. Today Python is extremely popular in academia with close attention for its variety of well-engineered libraries for Machine Learning, Artificial Intelligence, and Neural Network.

      Furthermore, due to the fact that Machine Learning is extremely demanded today, Python is relevant like never before. And it’s not like it wasn’t relevant before. For example, Ruby on Rails was incredibly demanded for startups because of its rapid prototyping and suitability, but Python is no way worse.

      Another point that delineates the brightness of Python’s future is game development. Due to its friendly scripting, Python is often preferred among game developers. Basically, there are no limits in terms of ways you can go with Python. We are pretty sure when the next field of big interest pop-ups, it will be followed by Python’s hissing.

       


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