Why Learning Java is a Starting Point For Big Data Developers Of The Future
By: John Selawsky
Tags: Big Data, Data Science, Java, Big Data Analytics

Why Learning Java is a Starting Point For Big Data Developers Of The Future

Java is a big friend to Big Data scientists and developers. Here I am going to tell you why is that so

Photo by Franki Chamaki on Unsplash

Considering the impressive pace of big data growth over the last 2–4 years, it’s clear that this subset of data science will dominate the future tech. In this post, I decided to take a closer look at the technologies that are widely used for big data projects.

Java is a leading language when it comes to handling big data projects. Here’s why and how a beginning developer can learn Java for handling complex BD tasks.

A Few Words About Big Data Objectives

Before discussing the impact of Java in the big data of the future, let’s take a look at what types of projects data scientists will be focused on in the next 3–5 years. Here’s my personal take on what big data will mature into in the near future.

1. It’s going to converge with analytics

Now that businesses can gather terabytes of data on every user, tracking on-site behavior, communication preferences, and other relevant metrics, companies are more encouraged to invest in analytics than ever.

Gartner, for one, predicts that, if a business owner doesn’t invest in reporting tools by the end of the year, the company will no longer be competitive by the end of 2021.

Adopting big data algorithms will improve the precision of analytics and give business owners a big-picture view of brand reputation and customer relationships. The introduction of decision trees, linear regression, and other visualization and prognosis methods will help business owners anticipate customers’ needs and increase the quality of brand interactions.

2. Big data will help fight climate change

Although the impact of climate change is real, scientists lack the understanding of the most immediate threats humanity will face in the next 20 years. Big data is a way for the researching community to consolidate their efforts and stay connected via a stream of reliable real-time insights.

Needless to say, the ability of big data tools to process large datasets will improve the precision of approximations and the efficiency of contingency plans people can build based on these insights.

3. The impact of data cloud will grow but not dominate

According to statistics, the value of the global cloud computing market is expected to exceed $623 billion by the end of 2021. Right now, an increasing number of companies are switching to cloud from on-premise solutions.

Having said that, fully migrating to the cloud is a complex process, not to mention the security concerns that come along with trusting online third-party vendors with huge datasets.

In the big data environment, hybrid environments are a common solution, with cloud tools used to host dynamic datasets and on-premise storages used to keep track of the static ones.

Why is Java Still The Best Choice For Big Data Projects?

If you are a software developer considering a career in big data, learning Java should be your starting point. Let’s take a look as to why saying “Java is Big Data” wouldn’t be an exaggeration.

1. Big Data tools for Java are accessible

When considering big data implementation, most business owners are looking for the cheapest tech stack possible. Since most Java tools used in big data (Hadoop, Spark, Mahout) are open-source, such a tech stack is free and highly flexible. As a result, most employees looking for big data engineers will focus on Java proficiency and the working knowledge of the tools that use the language.

2. Java is Type-Safe

Almost every data scientist out there would confirm that being able to understand what types of data you are dealing with when working on a project is crucial when the set of information to process is huge. Being a type-safe language, Java is a first-choice for a fair share of developers and business owners since type-safety allows spending less time on unit testing and facilitates codebase maintenance.

3. Java is scalable

Most projects big data professionals work on are ambitious, designed with upscaling in mind. Thanks to its robustness, wide toolkit, huge community, and cross-platform compatibility, Java is unmatched by other languages in terms of scalability and makes a perfect fit for designing complex big data infrastructures.

4. Wide range of built-in features (not to mention libraries and frameworks)

If most of the other languages are only beginning to acknowledge the importance of machine learning and data science, Java was the first one to jump on the bandwagon. As a result, it has more tools for DS project than most alternatives, to name a few:

Other than libraries, the language becomes more suited to data science with every new release. Say, Java 8’s Lambdas help to make the code to-the-point and concise, the ninth version re-introduced REPL that made iterative development faster and more efficient.

Whether you prefer Hadoop or Spark, knowing Java will be crucial to be proficient in either platform. To make the most out of BD tools, developers often need to add new features to the source code — that’s where Java knowledge is essential.

Reviewing Top 5 Java Tools For Big Data Projects

After having explained the benefits business owners get from choosing Java for big data projects and the reasons why developers should mark the starting point of a data science career with ensuring Java proficiency, let’s take a look at the most widely used Java tools in big data projects.

1. Apache Hadoop

Hadoop Ecosystem and Their Components by Data-flair.training

Hadoop is a go-to big data processing technologies for most business owners — there are dozens of libraries and tools dedicated to sorting through and storing large datasets. Despite its popularity, among talent managers, Hadoop development job openings are known as the hardest ones to fill in since most developers lack an in-depth understanding of MapReduce.

Although Hadoop is one of the most complex technologies out there, the gain of becoming proficient in it is definitely worth the pain. According to statistics, the median yearly salary of a Hadoop developer is $103, 000.

2. Spark

Spark is a common Hadoop alternative that is attractive to developers thanks to its high speed, agility, and smooth learning curve. Typically, Spark is preferred over Hadoop for large-scale SQL projects, data streaming, and machine learning tasks.

It’s worth mentioning that Spark isn’t fully written in Java but in Scala — however, the interfaces of both languages are similar (as a proficient Java developer, you will not need a lot of time to get the hang of Scala).

As for workplace opportunities, Spark is a lucrative technology to master since some of the biggest names in tech are looking for professionals in the field — Facebook, Microsoft, Apple, or IBM.

3. Mahout

From the Bauman National Library wiki

Since big data projects are closely intertwined with machine learning, big data developers often cross paths with Mahout — an open-source, Java-based library of ML tools. Mahout gained a tremendous following thanks to its scalability and a large data processing toolset.

Typically, developers start learning Apache Mahout after understanding the functionality of Hadoop, which makes sense (a fair share of the library’s infrastructure is made up of repurposed Hadoop code).

4. Storm

Compared to Hadoop or Spark, Apache Storm is a narrower toolset of big data tools, focused predominantly on enabling real-time distributed data streaming. Equipped to deal with high-volume and velocity data, the platform is repeatedly praised for its high scalability and fault tolerance.

Other than that, Storm’s compatibility with most popular queuing and database systems makes learning how to use the platform a must-have for a beginning big data developer.

5. Deeplearning4j

Deeplearning4j is a Java-based tool neural network developers swear by. This platform is well-done on so many levels, from the ease of use to the quality of documentation. Deeplearning4j is scalable — you can integrate it with Apache Spark or run the platform on GPUs. The tool supports microservice projects as well — it’s one of the few platforms out there with a robust microservice infrastructure.

List of Resources to Learn Java For Big Data

If you are planning to build a career in big data, becoming proficient in Java is essential. However, since there are so many language learning resources around, developers often struggle to distinguish between the good and the bad ones.

Here’s the list of resources I compiled for my students over the years — in my opinion, these are all the tools you need to get from a newbie to a professional in Java.

Coding Games and Courses:

Codegym is a gamified platform for beginning and advanced Java learners. Using gamification and practical tasks to explain the core concepts of the language, this tool is your way to start coding from day one. My students repeatedly complimented the platform, saying it improves knowledge retention and gives a clear idea of how the concepts they learn are useful in the workplace.

Although Code Combat doesn’t offer programming learners a solid theoretical framework, the tool is perfect for using your skills to complete real-life tasks. Combined with high-quality graphics and an intelligent plot, Code Combat is an enjoyable RPG you wouldn’t mind spending your weekends with.

Being a data scientist, as well as a programmer, is about collaboration and teamwork. If you are anxious about coordinating with peers at the office, check Coding Game out. The idea behind the product is both simple and ambitious — uniting developers from all over the world to build a game together.

When it comes to learning from the best Java developers and getting noticed by top-notch employees, CodingGame is a decent resource.

This retro-style game might seem basic at the first glance — yet, there’s more to this robot battle than it lets show. Personally, I like this platform exactly because of how raw and stripped down it is. Other than enjoying robot battles, you can join hardcore challenges and chat with fellow developers via a dedicated forum.

Online courses

Learn Java For Big Data:

Books

Learning the basics of Java

Learning Java For Big Data:

Forums and social media

Learning Java for Big Data:

Conclusion

Personally for me, Java and big data always came together. Although I acknowledge the potential of Python as well, in my experience, a successful data scientist shouldn’t choose one over the other — rather, you learn how to handle both languages and draw on their respective strengths.

Since Java is one of the best-taught languages online, I am confident that, with enough determination and the understanding of basic programming concepts, you can be good at using its tools for DS projects.

Hopefully, the resources I linked above will be a good starting point to fuel a beginner developer’s journey. Good luck discovering the full potential of Java and using the language in your projects.