Java for Big Data – Why To Learn and Where to Start

Java and Big Data have a long-lasting relationship, and a growing trend among most data scientists and programmers today is to invest in learning Java. Here’s Alex Yelenevych, Co-founder and CMO of CodeGym.cc explaining what you should know about Java and when to learn it.

The Big Data area has become too blurred on the Internet. Data technologies are plentiful and this can be a huge hurdle for beginners. Figuring the perfect starting point can be tricky. 

In this article, I am going to explain what Big Data is, and why Java is one of the excellent entry points to this area. 

What Is Big Data and Why To Learn It? 

Big Data has certain characteristics that are called 5V: Volume, Velocity, Veracity, Valence, Value. Big data will definitely remain among the information technologies demanded by the market for a long time. By 2025, businesses are projected to generate about 60% of all global data. Companies in the field of finance, telecommunications, e-commerce generate almost continuous streams of information. These enterprises need technology solutions that can efficiently collect, store, and use large amounts of data. This is one reason the demand for Big Data professionals will only grow in the coming years. 

According to the U.S. Bureau of Labor StatisticsOpens a new window , this field will grow about 27.9% through 2026. Also, there’s currently a rise in the number of business science majors and CS students willing to learn the methods of analyzing insights. Some researchers predictOpens a new window that the most successful and well-paying jobs in the coming decade will be data-related.

In recent times, Data Science and Big Data have experienced growth, bringing about a change in the undergraduate students’ choice of course to study. For example, a hot pick for most Harvard students is Introduction to Statistics, while Berkeley has Introduction to Data Science among the fastest-growing classOpens a new window .

Modern companies use Big Data for customer transactions, optimization, threat and fraud prevention. Over the past years, companies such as IBM, Google, Amazon, Uber have been creating jobs for data science programmers. 

Learn More: Highway to Heaven: Building a Strong Cloud-Based Business Roadmap

Big Data Fields 

There are many trajectories of Big Data. Roughly, we can divide them into two categories:

• Big Data engineering
• Big Data Analytics (Scientist)

These fields are somewhat connected but different from each other.

Big Data engineering develops the framework, captures and stores data, and makes the relevant data available for a variety of consumer and internal applications.

You need to accumulate good programming skills and understand how computers interact over the Internet. Also you should know some specific frameworks, I am going to write about them below. You don’t need to go deep into math and statistics here. 

Big Data Analytics is an environment for using large amounts of data from ready-made systems developed by Big data engineering. Big data analysis involves analyzing trends, patterns, and developing various classification and forecasting systems. Here some “magic” happens as results get interpreted by Data Analytics.

To be a data scientist, you need not only be a good software developer but also know a bit of mathematical analysis, probability theory, statistics, and combinatorics. 

Thus, Big Data analytics includes advanced data computing. Whereas Big Data engineering involves the design and deployment of systems on which computations must be performed.

Learn More: What Is a Data Catalog? Definition, Examples, and Best Practices

Java for Big Data

So, whichever industry application of Big Data you choose, you need to learn how to program and learn a programming language in any case. There are several options here. Most often, Big Data programmers use Java, Scala, Python, and R. I recommend beginners learning Java or Python first. 

R is really powerful in terms of Big Data, but it is also a very specific programming language. If you change your mind about Data Science or Big Data, it will be very difficult to find a job. Scala is a really good and optimized language but not very beginner-friendly. At the same time, Scala is a language that, like Java, runs on the JVM. Scala programs are very similar to Java programs, and they are free to interact with Java code. Hence, you can easily learn Scala after picking Java.

In general, I recommend starting with Java. The number of Big Data projects in Java and Python is roughly the same, but there are a few things to consider.

As I said, learning Scala after Java is easy. Moreover: any other language after Java is easier to learn than after Python.

Java is known for its versatility and ability to use different data science techniques. Most of the currently available platforms for storing and processing data were written in Java and Scala. An example of this is Hadoop HDFS, which is also a storage and processing platform for Big Data.

“To a large extent, Big Data is Java. Hadoop and quite a large part of the Hadoop ecosystem are written in Java. The MapReduce interface for Hadoop is Java too. So it will be fairly easy for a Java developer to move to Big Data simply by building Java solutions that run on top of Hadoop. There are also Java libraries like Cascading that make things easier. Java is also very useful for debugging, even if you’re using something like Hive [Apache Hive is a Hadoop-based database management system].” said Marcin Meyran, data scientist and vice president of data engineering at the Eight company.

Besides Hadoop, Storm is written in Java, and Spark (that is, the likely future of Hadoop) is written in Scala. As you can see, Java plays a huge role in Big Data. These are all open source tools, which means that developers in companies can build extensions or add functionality for them. Java development is often involved in this work.

Java can work on any computing system, and it is used in many ETL applications like Apatar, Apache Kafka, and Apache Camel. These applications are used in loading, transforming, and extracting information in Big Data environments. There are so many things in common between Java and Big Data — for example, a lot of online learning tools and a high demand for  specialists.

Besides, many enterprise solutions use Java — and Big Data is almost a synonym for enterprise. Large companies always operate huge datasets. So, it is safe to refer to Java as a basic programming language for Big Data.

I presume, Python is worth mentioning here. At first glance, Python is a laconic and simple language, so why not focus on it? Python is one of the main languages ​​for teaching non-programmers today as it has a very low entry threshold. Python is very useful for prototyping, quick testing of ideas. 

It is much easier to write something simple in dynamically typed languages ​​like Python, which is why there are so many small Python programs for processing data.

However, statically typed languages ​​such as Java or Scala are used for high-speed performance and for managing data in memory. Using dynamically typed languages, like Python, ​​for really large projects becomes very difficult, both in terms of development and debugging, as well as in terms of performance and code maintainability.

Learn More: The Do’s and Don’ts of AI Pilot-to-Production 

Advantages of Learning Java for Big Data Engineering

Here are the advantages of Java for Big Data engineering:

• Being statistically typed, it’s really good for big projects. 
• Given a broad user base, Java is widely known to clients as a reliable working tool.
• There are many learning materials available to help you learn Java. You can find these materials on different learning platforms or as video tutorials and books.
• Whether you’ll use Java in the future or not, you can be sure that learning it will be worth the time. It is based on many reasonable concepts that will be useful in the work of a programmer of any direction. It is beginner-friendly and it is easy to learn another language after Java. 
• Java is the base of many Big Data tools including Apache Hadoop, Spark, Storm, Mahout, and more.
• Scala is related to Java. Learning Java can help developers become confident users of Spark by transitioning to Scala.
• Java is Flexible. Developers can use it to develop an unlimited tech stack, thanks to its multithreading and scalability support.

Learn More: Implementing AI: Moving Beyond the Hype 

List of Resources to Learn Java for Big Data

• CodeGymOpens a new window – A gamified platform that is suitable for beginners and intermediate Java learners. There are short lectures and more than 1200 coding tasks of different difficulties with code validation and tips to help you know all the concepts of the Java language. You can use the tool to start coding without hassle. 
• CodinGameOpens a new window – You need teamwork and efficient collaboration if you want to be a programmer or a data scientist. CodinGame is a suitable platform where you can learn the Java language with ease. When you learn on this platform, you will get to work with other developers in different parts of the world. The aim is for you all to work together and develop a game. 
• MOOC course on Java ProgrammingOpens a new window – Discussing with other developers is also an effective way to learn Java, and that is what you’ll get on MOOC. You can use the courses to learn the fundamentals of the Java language and how it can be applied.

Java-Based Big Data Tools: Hadoop, Spark, and More

Apache Hadoop

Apache Hadoop is one of the foundational technologies for Big Data and it was written in Java. Hadoop is a free, open source suite of utilities, libraries, and frameworks managed by the Apache Software Foundation. Originally built for scalable and distributed yet reliable computing and storage of massive amounts of information, Hadoop is naturally becoming the hub of Big Data infrastructure for many companies.

This framework is ideal for processing large datasets. With the tool, you can scale up to many machines from a single server, each providing storage.

This tool’s main feature includes scalability, local data processing, failure support, and low intensity on hardware. 

Companies around the world are actively looking for Hadoop talent, and Java is just a key skill required to master the technology. According to Slashdot developers, in 2019, many large companies, including JPMorgan Chase with record salaries for programmers, were actively looking for Hadoop specialists at the Hadoop World conference, but even there they couldn’t find enough experts with the skills they needed (in particular, knowledge of the programming model and framework for writing Hadoop MapReduce applications). 

Apache Spark

Apache Spark is another key Big Data platform that competes seriously with Hadoop.

Apache Spark is a data processing engine that operates with speed and has elegant development APIs. Apache Spark is a very fast, flexible, and developer-friendly platform so it is often used as a framework for large-scale SQL, batch and streaming, and machine learning. 

Spark’s in-memory computing can be used for data science workloads, machine learning, and ETL.

Learn More: Why We’re Seeing an Uptick in Data Science Architecting

Apache Hive

This is another example of a framework for Big Data. Facebook created this framework, and it is also a data processing tool on Hadoop. With Hive, programmers can efficiently analyze datasets. The engine can also change SQL requests to MapReduce tasks.

Apache Storm

Apache Storm is a real-time distributed streaming computing framework. Storm simplifies the robust, real-time data processing that Hadoop does for data batches. Storm integrates with any queuing system and any database system. The framework offers fault tolerance, setup ease, and flexibility. You can use Storm to build applications with high responsiveness to data, thereby allowing you to react fast.

Where To Learn? 

There is a fundamental specialization Big DataOpens a new window on Coursera from UC San Diego university. You will learn the basics of using Hadoop with MapReduce, Spark, Pig, and Hive. The description says that knowledge of programming is not required, however, it seems to me that it will be very useful to first learn the basics of the programming language, and then, continuing to learn the language, take this course in parallel.

What Else to Learn? 

Except programming language and Big Data specific tools, a good idea is to learn

Bash Scripting. One of the basic concepts to know for anyone who wants to work with Big Data is deploying a server on Linux and scripting in the Bash Scripting command line. It’s easy, but it takes a lot of practice.

Databases, SQL for Big Data. Of course you need to know databases and SQL. First, learn basics, then learn something more specific. You can find some courses, for example, this one Opens a new window from Coursera.   

The world is gradually moving to an era where Big Data plays a major role in different areas of life. With this high growth rate, data scientists will certainly have a major role to play in every future project. That is why programmers are now doing all they can to acquire skills that will help them to stay relevant.

Data scientists will not regret learning the Java language because the Java language is here to stay. This is also indicated by its increasing popularity, support for Big Data , and dominance of production code writing. 

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