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David Collins
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Java Creates New Big Data Opportunities

If you think that we live in the age of digital information, you are totally right. But do you know just how much data is generated globally?

It is impossible to determine the exact figure, but rough estimations say that people, machines, and organizations create more than 2.5 quintillion bytes of data every day. It’s a mind-blowing number and it proves that modern businesses need super-powerful programs to collect and analyze big data.

Java is one of the best tools in this field as it creates a whole bunch of new big data opportunities. If you are interested in learning how it works, keep reading to see the impact of Java in big data.

1. How Big Is Big Data?

Before we focus on Java, we want to explain the sheer magnitude of big data and prove that it’s really a major business concept. According to the report, Amazon attracts more than 4.3 thousand unique visitors every minute. If you think that’s a lot, wait until you see the following stats:

  • Google users run over 40 thousand searches every second.
  • Twitter users publish 500 million tweets every day.
  • WhatsApp users exchange up to 65 billion messages daily.
  • Businesses harnessing the potential of big data see a profit increase of up to 10%.
java big data

On the other hand, studies also reveal that 95% of businesses struggle with unstructured data and cannot exploit the potential of their information libraries to the fullest extent.

This is where IT professionals step in to help businesses thrive. LinkedIn named data science the fastest-growing job in 2017, while big data is projected to generate 11.5 million new jobs by 2026. At the same time, Glassdoor claims that the average big data engineer makes more than $100 thousand annually.

We guess these numbers are enough to get you thinking about a big data career, so let’s see how Java can help you with that.

2. Five Reasons Why Java Is Perfect for Big Data Projects

You don’t have to be a Java developer to understand the potential of this programming language, but too many IT professionals still don’t know why it plays such a major role in big data. We shortlisted five practical reasons to use Java in big data analytics:

  1. Availability of Java-focused big data tools

The first reason on our list is simple – big data tools compatible with Java are available and easy to access. These are open-source platforms such as Hadoop or Mahout, which gives you a fair share of flexibility and the right to use Java tools free of charge. The bottom line is that you don’t have to learn any other programming language when Java-focused tools are free to use.

  1. Type safety is Java’s advantage

Type safety is a huge issue among data scientists because it’s critical to figure out and categorize data types properly. This is particularly important in big data projects with massive information libraries.

Jake Gardner, one of the best essay writers at the assignment proofreading services, explains that it gives Java a comparative advantage: “Java is type-safe, so developers don’t have to waste time on redundant tests.”

  1. Scalability of the programming language

A lot of big data projects are continuous and require ongoing adjustments and maintenance. As such, they need a reliable tool with enough scalability potential. Java fits in perfectly because its broad toolkit, interoperability, and developer community allow big data projects to grow and scale as needed.

  1. Built-in features and libraries

As one of the leaders in the programming universe, Java is also the pioneer of big data management. It comes with a fairs share of built-in features and libraries designed with big data purposes. In other words, you rarely ever need to start from scratch and build everything on your own.

  1. Lots of learning resources

Of course, big data projects force Java developers to learn continuously and improve their knowledge year after year. The best thing about Java is that you can find a lot of learning resources online and keep pace with the latest findings and trends in this field. Some of our favorite Java-oriented resources include Code Combat, Codegym, Java In-Depth, and The Ultimate Hands-On Hadoop.

3. Best Java Tools for Big Data Projects

Java is obviously the way to go when it comes to big data projects, but it’s not so easy to choose the best tools for this purpose. After all, there are hundreds of Java platforms you could check out here, so it’s much easier to shortlist options and focus on top-performing tools only. We selected five proven Java tools for big data projects:

  • Hadoop

Hadoop is probably the most complex of all Java-related big data tools you can play with, but it’s totally worth the effort because it offers a variety of business applications. It contains numerous libraries and platforms specialized for data management and large information volumes. The problem for many developers is the fact that Hadoop’s MapReduce requires a lot of time to figure out and perfect.

  • Spark

If you are looking for a reliable Hadoop alternative, then Spark may as well be your best choice. The tool is very efficient as it offers developers more than enough functions and agility, while it also doesn’t require as much learning time. As such, Spark is typically the most desirable big data platform for massive SQL and machine learning projects.

  • Mahout

Big data goes hand in hand with machine learning, which is also why Mahout plays such a major role in big data projects. Mahout is the library of machine learning tools and learning resources that focused on Java in particular. The library is based mainly on Hadoop, so the two platforms complement each other.

  • Storm

Developers looking for a tool specialized in real-time data processing and analysis should keep an eye on Storm. Despite being much more focused than Spark and similar tools, Storm is unmatched in terms of scalability and interoperability. This definitely makes Storm one of your must-learn Java tools for big data projects.

  • Deeplearning4j

We saved developers’ favorite tool for last. Deeplearning4j is all-encompassing and intuitive at the same time, which makes it fairly easy to learn. Besides that, Deeplearning4j is known for its scalability and comprehensive micro-service support. You can also integrate the platform with the majority of popular big data tools.

4. The Bottom Line

Java is one of the most popular programming languages thanks to its near-unlimited applications, so it’s not a surprise to see that it earned its spot in the big data world as well. Java creates some serious big data opportunities and makes an excellent career choice for young developers globally.

In this post, we introduced you to the key big data facts and showed you a few practical reasons why to use Java for big data purposes. Java has a huge potential in data analytics, so don’t hesitate to experiment with it and try your knowledge in big data projects.

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