A look at the long-lasting relationship between Java and Big Data

Data science is the most competitive and high-demanding field. According to Glassdoor, data scientists' jobs rank among the top positions of 2022. Also, in terms of job satisfaction, Glassdoor ranks data scientist jobs among the highest scorers.

‍

Today, data is constantly generated anytime when we open an app, search on Google, or travel with our mobile devices. The result? Massive collection of valuable information that companies store, manage, visualize, and analyze.

‍

If you are a computer enthusiast interested in learning how extensive data works, now is the time to get into this in-demand field. According to the US Bureau of Labor Statistics, more than 11.5 million data science and analytics jobs will be introduced by 2026.

‍

4 Reasons To Learn Big Data In 2022

Here are some of the reasons to become a data scientist and how it helps organizations understand customer behavior. So, let’s get going:

‍

1. Make Data-Driven Decisions

Indeed, data-driven decisions are more accurate than human-generated decisions. Many big companies use big data to understand customer behavior and run marketing campaigns by identifying which marketing strategy has the higher return on investment.

When you study big data, you will get the opportunity to gather customer data and revamp your business strategy accordingly. According to the Big Data Framework, only 11% of companies use big data. However, most organizations still have a long way to go.

‍

2. Flexible To Learn

The best thing about big data is that you don’t need a very high educational background to become eligible for this course. There are tons of online and offline courses to kickstart your journey as a data scientist.

As per the current trend, you can opt for online classes to take the course from the comfort of your home.

‍

3. Learn Diverse Skills

Learning big data will make you strong in mathematics, algorithms, data structure, planning, visualization, predictive modeling, and many more.

Getting hands-on experience in these skill sets will make your profile stand-out, and polish your thought process.

‍

4. Trading And Finance

If you continually invest in stock markets, you must consider taking a data science course. Today, the equity process relies on big data and machine learning algorithms to predict stock market fluctuations and variations of stock prices.

Additionally, big data can be used to discover promising investment and trading opportunities.

‍

Java For Data Science: Why Is It Necessary?

Most data scientists use Python and R more than Java. Java is the most widely used programming language, and it offers a plethora of other services for creating internet applications.

‍

Many big players, such as Uber, Spotify, Airbnb, etc., rely on Java. With that in mind, here are some reasons why learning Java is crucial for data scientists:

1. Java Has Excellent Frameworks For Data Science

Java offers multiple frameworks for big data, including:

• Hadoop - It uses the MapReduce algorithm for storing users’ data.
• Kafka - It is a framework implementation that aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.

‍Hadoop is mostly used in big data and data analytics, and most data scientists say that “Java Is A Big Data.”

2. Java Is Easy

Java is easy to learn, and surprisingly, most developers feel confident when coding with Java. It has the highest demand in the corporate world as companies hire developers to work on executable projects.

3. Scala Is A Relative Of Java

Java is the best choice for big data projects. A new API will always be available first in Scala, and later it may or may not be in Java. Scala was created to make it a better language in terms of syntax.

4. Java Has A Unique Syntax

Java’s syntax is easy to learn, and it allows users to understand conventions, requirements for a variable, and coding methodology. Most companies maintain a standard syntax for their code repository. Java helps them by keeping its standard conventions, which can be adhered to.

‍Understanding Java-Based Big Data Tools

‍

1. Hadoop

Hadoop's primary goal is to manage structured and unstructured data, and it is implemented as a Hadoop cluster on racks of commodity servers.  Hadoop is known as a self-healing cluster. It means that it can detect changes, including failures, and adjust to those changes and continue working without interruption.

Here are some of the common features of Hadoop:

• Scalability
• Failover support
• Local data processing
• Low intensity on hardware

‍Indeed, Hadoop is in high demand. According to Techvidvan, the Hadoop and big data markets are set to reach $99.31B in 2022, attaining a CAGR of 28.6%.

‍

2. Mahout

When creating scalable machine learning algorithms, Apache Mahout comes in handy. These algorithms are used for:

• Clustering
• Classifications
• Recommendations

Mahout runs on a Hadoop algorithm, which will work well in a distributed environment.

3. Spark

Apache Spark is quite popular, and it is in high demand nowadays. Learning Apache Spark can be your best bet if you want to learn big data.

It can be used for in-memory computing for ETI, machine learning, and data science workloads to Hadoop. Hence, Java is the building block for Apache Spark stack, and all its products fully support it.

4. Hive

Facebook created this framework to combine the scalability of the big data frameworks. Hive is open-source software that allows engineers to analyze large data sets on Hadoop.

‍

Wrapping Up

As new data evolves, more and more Java-based big data tools are coming into the picture. This field is in high demand; thus, it’s high time to kickstart your journey by joining a big data online course.

For data scientists, Java provides various data science functionalities such as big data analysis, data processing, data visualization, and more. As a result, you won’t regret learning Java if you work on big data. You can always visit Cogent university for more such articles.

Cogent University offers a Java full stack development bootcamp to kickstart your career as a Java Developer. The 8-week course is designed and delivered by technology leaders with decades of experience. It covers database concepts and Core Java, Web servers, and JSP tools. Participants are also trained in the application of Spring, Hibernate, AngularJS, CSS, JavaScript, jQuery, AJAX, and much more.

The bootcamp is also supplemented by a capstone project that pipelines graduates into the industry for high-paying software development positions as Java developers. Thus, if you have gotten all the answers about a potential career in Java, Cogent University's Java bootcamp is an excellent place to start your journey.