Most Essential Big Data Tools for Java Developers

Most Essential Big Data Tools for Java Developers

The world of digital technology evolved at a rapid pace. Java is here for more than 20 years as one of the most preferred programming languages. If you look deeply into all available programming languages, databases, operating systems, something has changed over the past few years. With the commencement of a new technology space with IoT and big data, we will see lots of improvements. But Java is also the strength of various big data tools for Java developers. Moreover, it is the right fit for big data because few of the core modules of big data are written in Java. Additionally, most of these big data tools are open source. Thus, they are easy to access. Here in this post, we will identify the use and future of Java for Big data tools. We will also discuss the most helpful Big Data Applications Using Java as their core programming language.

What is the Future of Java Programming in Big Data? 

Although Java has numerous reasons to dislike it programmers find lots of reasons to learn it. Let’s discuss some of the best reasons why an individual wants to use it:

Simplicity 

As an objected-oriented language, Java provides an easy user experience for end-users and developers. Java’s inbuilt design is its most favorable benefit when contrasted with other programming languages. Dissimilar to C++, Java also eliminated the use of interface and pointers.

Portability 

Java runtime can run anywhere and anytime. Thus, you can run Java on any software or hardware program.

Allocation

Java has a stack provisioning functionality that enables to re-establish instantly. Although, Java has the potential for waste collection and automatic memory supply.

Distributive 

Java is extremely networking competent. It sends and receives files informally.

Secure 

Java powers high-security protocols with secured programming.

Now, let’s understand where Java fits alongside Big data –

Now, the number of data generated every day shows exponential growth. Furthermore, identifying such a large number of data will only continue to enhance over time. Batch data processing is an authentic way of investigating such huge data. This is usually done by the utilization of open-source tools like Spark and Hadoop.

Excitingly, most open big data tools are based on Java because it is deeply rooted in open-source communities. Thus, a vast amount of Java code is available. So, with these accessible code foundations like Google, Apache is majorly contributing to building open source big data tools.

For the most preferred big data tool Hadoop, Java is a programming language. Consequently, it is simple for Java developers to adopt Hadoop. The truth is understanding these big data tools are equivalent to learning new API.

Not just Hadoop, yet Pig is also a big data tool that can be learned easily as Pig Latin utilizes JavaScript.

Top Open-Source Big Data Tools in the Market 

Undoubtedly, java is the future of big data. There are various big data tools available for java developers. These tools are open source from Apache. Here, we have specified a few majorly used big data tools available in the market.

1. Apache Hadoop

Hadoop is a Java subproject and broadly used big data tools in the world of programming. It is an Apache Software Foundation tool that was later contributed by Yahoo. It is a free Java-oriented programming framework that processes a big amount of datasets in a distributed computing environment. Also, you can easily install it on a bunch of standard systems.

2. Apache Spark

Apache Spark works similar to Hadoop and is getting preeminent for big data processing. Spark is a cluster computing framework and can operate on thousands of systems. Also, it can run on a bunch of distributed machines on the gigantic datasets across the systems and merge the results.

Spark is majorly used in big ETL (extract, transform, and load) predictive analysis, operation, and reporting applications. A Spark system would do the following:

• It can load most data into the RDD
• Performs the data transformation to make it compatible
• Cache the reusable data across sessions
• Perform some custom operations on the data

The core language used for Spark in Scala is written in Java. Thus, Java is the building block of the Apache Spark and it is completely supported by all its products.

3. Java JFreechart 

Data visualization is an essential element in big data analysis. As big data deals with an enormous amount of data, it is a challenge to discover trends by only looking out raw data. But when similar data is plotted on a chart, it becomes easy and efficient to investigate the relationships and patterns within data.

Bottom Line 

As big data is rising at a fast speed, new tools and technologies are taking the place. There is no doubt that Hadoop is the best among the big data tools. Thus, before you learn any of the big data tools, you must understand Hadoop.

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FAQs

Why do we need big data tools?

Big Data helps the companies to produce new growth opportunities and completely new categories of businesses that can syndicate and study industry data. These organizations have plenty of information about the services and products, customer preferences that can be analyzed and captured.

What tools are used for big data?

Top 8 Big Data Tools in 2020

1) Apache Hadoop.

2) Xplenty.

3) CDH (Cloudera Distribution for Hadoop)

4) Knime.

5) Cassandra.

6) Lumify.

7) Datawrapper.

8) MongoDB.

Is Hadoop a big data tool?

Hadoop is an open-source framework and it is the main key to step into the Big Data environment. With Hadoop, one can professionally perform innovative analytics that incorporates data mining, predictive analytics, and machine learning applications.

What is big data technologies?

Big Data Technologies is the utilized applications that involve data sharing, data storage, data mining, and data visualization, data framework including tools and techniques utilized to examine and alter data.

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