Beginners to data science can choose one technology out of plenty that will help him/her master this science faster. However, nobody will tell you what programming language is best suited to meet the goal. Many factors will affect your achievements in this sphere. We’ll 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 the top technologies for creating Big Data solutions.

This article will be helpful for both developers and customers searching for the best technology stacks to start their Big Data project in 2019-2020. 

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Top 4 Things You Need to Start a Data Science Project Using Python or Java

Specificity

Be ready to learn a lot of new things. You’ll 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.

Flexibility

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 the search and handling of the benchmark data. Unfortunately, no advanced machine learning kits will help you meet the above-mentioned objectives.

Effectiveness

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

Productivity

Sometimes, it’s very important to optimize the productivity of your code, especially if you work with specific big data sources. 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.  

What’s better for enterprise applications? Java or PHP? Check out the answer 

Java for Data Science

java for data science

Java 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.

Advantages of Java for Data Engineering

  • Flexibility. A lot of existing 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 and handling data science in Java.
  • Java is a high effective compiled language that is used to write the code with high productivity (ETL) and algorithms for machine learning.

Disadvantages of Data Science with Java

  • «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.

To sum it up, 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 codebase.

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 the system’s options to the customers via REST API.
  5. Elasticsearch – to store and query large volumes of information.

Big Data with Python

Python for Data Science

In 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.

Advantages:

  • 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.

Disadvantages:

  • 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:

Java vs Python for data science

Python vs Java — Speed

In the table above, you’ve already seen that Java speed is 25 times better than Python speed. Why Python is slower? Let’s figure it out. Actually, a Python interpreter is slower than Java’s virtual machine. The reason for that is that Python is a much more dynamic language. So, it’s harder to write and optimize a compiler. If you compare Python performance with Java or .NET virtual machines, Python will be at a disadvantage. 

If that is necessary, you can optimize the performance of Python. You can, for example, run Python on an alternative interpreter that is called PyPy. It can make your code much faster. Read more about the performance in the next section.  

Python vs Java — Performance

To compare the performance of the two programming languages, you should first provide a case. Surely, the performance will differ mainly because of the dynamic and static nature of languages. Dynamic languages like Python are usually slower than static. And here are some reasons for that:

  • It’s hard to compile with traditional techniques. 
  • Methods can be added or removed.
  • Object types can change.

Nevertheless, in case it is needed, developers can speed up a dynamic language by making some language-level and virtual machine improvements. 

Hadoop: Python vs Java for Data Science

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 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.

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What Is MapReduce and How Does It Work?

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 the 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 that, 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 came together to reduce the resulting data set. They called this algorithm MapReduce. Below is a great example of a word count in the text allowing us to understand how MapReduce works.  

How Map Reduce Works

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 the 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:

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

Python is your perfect fit 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 you won’t get complete support in case of any failures. On the other hand, custom 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:

  1. Streaming processing. It 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.
  2. 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? How Can We Help You?

If you made up your mind in favor of a custom 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:

Consulting and Business Analysis

The first thing to get started with your project is to consult the experts. They will analyze your business requirements and needs, write an SRS document, and estimate approximately the cost of the development.

Development

Big Data projects can take time and effort to be developed and implemented successfully and on time. Rely on professionals. Be sure you are not caught by scams. Here are useful tips on how to check if an outsourcing company is cheating.  The article is about cheating among the outsourcing companies and how to detect it but it is also a good guide on how to never be cheated by vendors.

Implementation and Training

If your solution is already developed you will need the specialists that will help your company implement it successfully and teach your employees to use it effectively.

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 Statista, 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

Trend 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.

Trend 2. Safety is 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 that is being secured.

Trend 3. Internet of Things. 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.

Trend 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.

Trend 5. Automation will become one of the major sources of data creation.

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According to these future trends, Diceus recommends to middle-sized and large companies 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 data science experts.