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What Technology Is Better for Big Data Projects: Comparing Python and Java (+R)

by Irina Kravchenko

Beginners to data science are able to choose one technology out of a plenty of the technologies that will help him/her master this science faster. However, nobody will tell you what programming language is best suited to meet the goal. Your achievements in this sphere will be affected by many factors. We will try to review all of those factors. Besides, the objective of this article is to compare and contrast Python Vs Java for Big Data - the two are considered to be the top technologies for creating Big Data solutions.


Be ready to learn a lot of new things. You will need to master different software packs and modules to write code. Your level of expertise is influenced mainly by the availability of object-oriented software packages for your language.


A leading data scientist has a good command of coding and is able to calculate and analyze data. The most part of the everyday work is focused on search and handling the benchmark data. Unfortunately, no advanced machine learning kits will help you meet the above-mentioned objectives.


In a forever changing science environment, there are a lot of opportunities to get the desired job. However, due to a fast development of data science, it is often accompanied by technical debts. Only persistent practice can bring tech debts to a minimum.


Sometimes, it’s very important to optimize the productivity of your code, especially if you work with big data volumes of special importance. Compiled technologies are usually quicker than interpreted ones.

Each of the languages under review has one of the features described above.

Considering these factors, let’s review Java vs Python for machine learning and Big Data.

Java for Data Science

java for data scienceJava is a popular technology for general purposes that is run on Java Virtual Machine (JVM). This is an abstract computer system ensuring a smooth transfer between the platforms. Today, it is supported by the Oracle Corporation.


  • Flexibility. A lot of current systems and apps are developed with this technology. A great advantage of this programming language is the ability to integrate data science methods into an existing code database.
  • Strong typing. Java cares a lot about type safety. This feature is of great importance for developing Big Data applications.
  • Java is a high effective compiled language which is used to write the code with high productivity (ETL) and algorithms for machine learning.


  • «The verbiage» of Java is not good for analysis making and developing more sophisticated static applications.
  • It doesn’t have a great number of Java data science libraries for static methods as compared with some other object-oriented languages like R, for example.

Java can be considered the most suitable language for data science objectives. A lot of companies value the ability to integrate the finished product code into the existing code base.

Java for Data Analysis: Is Java Required for Data Science?

Although a lot of specialists argue in favor of Python, Java is also required for data analytics. A great deal of Big Data systems is developed in Java or created to run on JVM. The stack may include the following tools:

  1. Spark is used to stream data and distribute batch.
  2. Kafka - to queue huge volumes of information.
  3. Cassandra - to query and store Big Data.
  4. Spring Boot - to provide system’s options to the customers via REST API.
  5. Elasticsearch - to store and quiry large volumes of information.


Python for Data ScienceIn 1991 Guido Van Rossum presented Python. From that point forward it has turned out to be exceptionally famous. It is broadly used by data masters.


  • Python is an all-purpose technology. It has a wide range of developed modules for developers. A lot of online services provide API for Python.
  • Python is easy to study. A short learning curve makes it perfect to study first.
  • Such software suites as Tensorflow, Pandas, and Scikit-Learn make Python a reliable option for modern applications for machine learning.


  • Type safety. The language is dynamically typed that’s why you should carefully use it. Bugs, errors, and types non-conformity can appear from time to time.
  • With regards to the solid objectives of statistical analysis, an extensive variety of R kits is more advantageous than Python.

Python is a good fit for data science. The most part of data science is focused on ETL (Extract-Transform-Load). This feature makes Python a perfect match for these activities. Python data science libraries like Tensorflow by Google makes Python a very interesting language for machine learning.

Java Vs Python for Big Data Projects

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:

TypeGeneral purpose.

Write once, run everywhere.

High level.

Code readability and short syntax.

CompilationEasily complies on any platform. Easily complies on Linux.
ProductivityLess productive than Python because of the need to define each of the variables.Fewer lines. 5-10 times more productive than C++ or Java.
TypesStatically typed. All variables must be explicitly declared. Dynamically typed. Developers don’t have to declare anything.
SpeedJava is 25 times faster than Python in games. Is not as faster as Java.
DistributionDue 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.

Hadoop: Python vs Java

Have you ever wondered how Google does their queries into the amounts of data or how Facebook is able to quickly deal with such large quantities of information? They use data management that is called Big Data, other terms like Hadoop or MapReduce. You can be sure that they all will be a regular part of your conversations in the coming month or years. This is because 90% of the world’s data was generated in just last two years. All the data in the world was mostly generated in the last two years. And this accelerated trend is going to continue. All this new data is coming from smartphones, social networks, trading platforms, machines, and other sources.

In the past when a large quantity of data needed to be interrogated businesses would simply write larger and larger checks to the database vendors of choice. However, in early 2000s companies like Google were running into a wall. The vast quantities of data were simply too large to come through a single database and they could not simply write a check to process the data. To address this, the Google apps team developed an algorithm that allowed for the large data calculations to be chaptered into the smaller chunks and mapped to many computers. Then when the calculations were done they come together to reduce the resulting data set. They called this algorithm MapReduce.

Java Vs Python for Big Data Projects MapReduce

This algorithm was later used to develop an open source project called Hadoop which allows applications to run using the MapReduce algorithm. Simply put, we are processing data in parallel rather than in serial. Although MapReduce algorithm was introduced many years ago it’s still very relying on Java coding to be successfully implemented whereas the market is rapidly evolving and tools are coming available to help businesses adopt this architecture without the major learning of Java code. There are two ingredients that are driving organizations into investigating Hadoop. One is a lot of data, generally larger than 10 TB. The other is a high calculation complexity like statistical simulation. Any combination of these two ingredients with the need to get results faster and cheaper will drive you to turn on investment.

So, what programming language, Java or Python, is used by Hadoop?

Hadoop itself is written in Java, with some C-written components. The Big Data solutions are scalable and can be created in any language that you prefer. Depending on your preferences, advantages, and disadvantages presented above, you can use any language you want.

Considering Your Data Needs: Python, R Vs Java

It’s quite difficult to choose what language to study or use for Big Data projects. Say, if you are dealing with math or statistics R is the best fit, if you are close to programming Python is ok, if you are going to develop enterprise-grade solutions Java will be a perfect choice. You see, that it’s important to take into account the needs and goals of the entire project in order to choose the right technology. Let’s take a closer look at what goals each of the languages meets.

R will is the right choice if:

  • You need a comprehensive analysis of statistics (for example, in the IoT or financial industry).
  • You work with charts and graphs and need to turn those into interactive web apps.

Java is good for:

  • For faster developing large-scale systems.
  • For ensuring high speed and productivity of the project.

Python is your perfect fit if:

  • If you are a developer who uses statistical techniques or a data scientist who integrates his assignments with a system.
  • You need to create a sophisticated system that could be integrated with the production.

What Software to Choose to Start a Big Data Project?

First off, you should decide if you are ready to use an open-source solution or an enterprise one. Open source solutions are good because they are free to use. The main disadvantage of such software is that the support is not provided in case of any failures. Enterprise solutions can be customized and maintained. Look at the most popular BD open source tools:

Open Source Big Data Tools

The choice depends on the scope of data. If you have a large amount of information, the standard solution for this is Hadoop that offers HDFS, HBase, Hive and Spark tools, etc.

The type of data processing also will affect your choice. There are two main data processing types:

  • streaming processing is expected to analyze the data apert few seconds. Good for companies that deal with continuous data: E-commerce, SMM, retail. Every day there are more than 350 mln tweets on Twitter that’s why the company uses the streaming approach via Apache Storm to process a large amount of data.
  • with a batch processing, a complex analysis of data is used, the calculations take up to 1 minute.

What Specialists Do You Need to Develop a Big Data Solution?

If you made up your mind in favor of an enterprise solution, you should look for a team of professional Big Data developers. What services do they usually offer? Here is a list of standard services you can get from a Big Data team:

  1. Consulting. The first thing to get started with your project is to consult the experts. They will analyze your requirements and needs, write an SRS document, and estimate approximately the cost of the development.
  2. Development. Big Data projects can take time and efforts to be developed and implemented successfully and on time. Rely on professionals. Be sure you are not caught by scams. Here is our article on “How to Check If an Outsourcing Company Is Cheating?”  The article is about cheating among the outsourcing companies and how to inspect it but it is a good guideline on how to never be cheated by vendors.
  3. Implementation. If your enterprise solution is already completed you will need the specialists that will help your company implement it successfully and teach your employees to use it effectively.
  4. Integration. In case you use some other software and need a Big Data system to be integrated with some other components, systems, or modules, professionals will help you do this quickly.

What Are the Perspectives of Big Data in 2027?

According to, by 2027 the volume of BD revenue is expected to reach 103 billion dollars. Compare this revenue to that as of 2011 - it was just 7.6 billion. Here are the major 5 trends of information development for the next few years:

Big Data Revenue

  1. Data is no longer a “context” for business, but its most precious asset. It is predicted that by 2025 20% of data will play the most important role in our everyday life.
  2. Safety as a critical basis. The safety of personal data is of core interest nowadays. Researchers say that today there is a great gap between the actual size of big data and the volumes of the data being secured.
  3. Internet of Things. A growth of Big Data will lead to the situation when every single person on Earth will collaborate with devices connected in the networks approximately 4800 times per day.
  4. Mobile data in real time. By 2025 20% of data will have become the information generated in real time. 95% of this data will contain the data from IoT devices.
  5. Automation will become one of the major sources of data creation.

According to these future trends middle-sized and large companies are recommended to start thinking about Big Data solutions as well as changing the ways they collect and store the information. The key focus is on gathering the information, less but more valuable to the business. In case you are interested in innovative methods of corporate data management feel free to ask our experts.



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