The subject of data science is rapidly expanding. While many data scientists leverage interpreted programming languages like R and Python, they eventually run into Java or the JVM when connecting to real-time data streaming engines or large-scale databases. Many of the big data stack’s frameworks operate on the JVM, including Spark, Kafka, Hadoop, Hive, Cassandra, ElasticSearch, and Flink.
Scaling ETL, distributed training, and model deployment is aided by Java and other JVM languages. Indeed, Java can do it all, or at the very least, make it easier for developers working in other languages to do the same tasks.
This blog talks about the role of Data Science in Java in great detail. It first introduces the key features of Java before jumping into the importance of Data Science in Java which makes it a crucial factor for improving productivity and boosting business growth.
Table of Contents
What is Java?
Java is one of the most widely used programming languages in the business world. In the area of development and technology, “old” usually denotes “outdated.” This is not the case, however. Owing to Java’s rich history, many businesses are likely already using a major portion of the programming language without even realizing it.
Here are a few additional features of Java:
- Object-Oriented: In Java, everything is treated as an object which has some behavior and data. Java can be easily extended since it is based on the Object Model. Basic concepts of Object-Oriented Programming include Inheritance, Polymorphism, Abstraction, Encapsulation, and much more.
- Secure: Java is the first choice when it comes to security. Java provides Java Secure features that allow you to develop temper-free and virus-free systems. Since the Java programs always run in the Java Runtime Environment (JRE) with almost no interaction with the system OS, they are more secure.
- Platform Independent: Programming Languages like C/C++ are compiled into platform-specific machines. Java, on the other hand, is guaranteed to be a write-once, run-anywhere language.
- Distributed: Java doubles as a distributed language. This allows programs to be designed to run on different computer networks. Java provides a special class library that can be used to communicate using TCP/IP protocols. It is comparatively easier to create network connections in Java against C/C++.
- Improved Polyglot Programming: A Polyglot refers to a script or program, written in a form that is valid in multiple programming languages while performing the same operation across these programming languages. Java 8 introduced this feature for improved productivity.
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What is the Importance of Data Science in Java?
To begin with, using Java for data science is primarily a preference decision made by either the individual data scientist or the organization. The correlation between data science job posts and preferred programming languages is interesting, but it doesn’t convey the whole story. Employers will provide a long list of “Preferred” or “Desirable” qualifications, with Java sandwiched between Python, R, SQL, C++, etc. So it’s not a good idea to assume that the 10% of Java-related data science job advertising just includes Java as a preferred language. In terms of specific data science functions, however, Java may be used for a lot of the same things:
- Data import and export.
- Cleaning data.
- Statistical analysis.
- Machine learning and Deep learning.
- Deep learning.
- Text analytics is also known as Natural Language Processing (NLP).
- Data visualization.
Here is why Data Science in Java can play an important role in your business:
Data Science in Java: Excellent Frameworks
Developers can save time and money by using these frameworks, which provide basic functionality. The following are some examples of popular machine learning frameworks:
- Deeplearning4J: It’s a deep-learning toolkit for Java that allows you to deploy neural nets. It’s compatible with Hadoop and Spark.
- ND4J: For Java, it stands for N Dimension-array objects. It’s a scientific computing, signal processing, and linear algebra toolset. Numpy and MATLAB are among the built-in libraries.
- Apache Mahout: This is a distributed and scalable algebra framework. It aids classification, clustering, and suggestion.
There are numerous data-handling frameworks in Java, including:
- Hadoop: The MapReduce algorithm is used in this framework to store data in a distributed file system.
- Kafka: It employs a TCP-based message set abstraction protocol to organically organize messages into linear writes.
- Apache Spark: Apache Spark is used for processing large datasets. It is built on top of Apache Hadoop MapReduce. The major advantage of Apache Spark is its in-memory cluster computing.
- MALLET: MALLET is an acronym for Machine learning and for Language Toolkit. It is an extensive open-source library that comprises utilities for Natural Language Processing.
- Java-ML: In the Java-Machine Learning library you can find a wide collection of machine learning and data mining algorithms. These algorithms can be used for data preprocessing, feature extraction, classification, and clustering.
- Weka: Weka stands for Waikato Environment for Knowledge Analysis. It is an open-source machine learning library for Java. This library can be used for data mining, data analysis, and predictive modeling.
- Tablesaw: Tablesaw is a Java library used for data frames and visualization. Data loading, transformation, summarize, and filtering functions are available in this library.
Data Science in Java: Easy to Understand
The majority of developers are comfortable developing with Java. Aside from its large user base, Java is one of the most in-demand abilities in the market, since organizations often employ it for all projects that can be completed fast. Java is also known as a heritage language. Therefore, it is used in a lot of big apps and companies all over the world.
Data Science in Java: Scalability
The majority of programmers use Java to create apps that can be scaled up or down based on business needs. If your firm is building an application from the ground up, Java is a fantastic choice because it has scale-out and scale-up features. Java’s load balancing possibilities give you an extra edge over your competition.
As a data scientist, you’ll discover that writing complicated Java applications and scaling them is simple; for example, ApacheSpark is a scaling analytics tool that can also be used to create multi-threaded programs.
Data Science in Java: Unique Syntax
The easy-to-understand grammar of Java is well-known around the world. This syntax enables developers to comprehend conventions, variable requirements, and coding methodology. Java is strongly typed, which means that each data type is predefined in the language’s structure, and all variables must belong to one of these data types.
The majority of significant corporations use a common syntax for their code repository. As a result, all developers will code per the production codebase’s norms. Java assists them by keeping its own set of standard conventions that can be followed.
Data Science in Java: Fast Processing Speed
For data science applications, the majority of data scientists utilize Python. Java is 25 times faster than Python, which may surprise you. Java also outperforms Python when it comes to applications that perform several computations at the same time.
Not only does Java development take less time than many other languages, but it also takes less time to create a product. It includes a lot of IDE and mature capabilities for constructing large-scale commercial applications and may employ business-specific tools for development.
Data Science in Java: Java Virtual Machine
The Java Virtual Machine ecosystem enables developers to write code on multiple platforms. Java is a provisional language that developers use for building applications that are efficient.
Machine Learning services require high performance, which programmers can achieve through Java. Along with the Hadoop ecosystem, JVMs are an amazing environment to work with data and setup analytics. The JVM also allows developers to quickly create tools. Therefore, Java can be used for any machine learning model that requires the development of various features and tools.
Data Science in Java: Faster Development
Java is said to be 25 times faster than Python. Java’s processing speed is also unbeatable compared to other programming languages. There are many things that Java can handle easily.
Data Science in Java: Algorithm Deployment
Java makes it easy to develop and deploy algorithms. As a result, programmers who know both Java and Python are more likely to be hired by a company than anyone else. The Java codebase also provides a high level of integration. You can easily connect the algorithm to your codebase and new developers can easily start assigning code. Deploying algorithms in Java is easy because the syntax of the programming language is simple.
Data Science in Java: Wide Community
One of the main reasons data scientists need to know about Java programming languages is that there is a large community of Java programming languages. If your data scientists need documentation or resource support, Java is one of the most developer-friendly programming languages, so it’s easy to get.
In addition, with the help of the community, you can build machine learning applications and participate in various projects. The community is growing day by day.
Data Science in Java: Compatibility with OLTP Systems
For batch processing, Data Warehousing and Online Transaction Processing Systems (OLTP) usually leverage mainframe computers. Java, more than any other language, fits into that design more naturally. Java can be used in conjunction with COBOL and middleware software.
Java can also be used in conjunction with OLTP standards and architectures. Java is an excellent choice for firms wishing to invest in apps that perform data analysis on big scale systems with transaction processing design.
The Scenario when Data scientists use Java for Data Science purposes
The popularity of Python and R either among Data Scientists or in the Data Science community is quite high. But there are some situations where Java is preferred over Python. There are a few situations where it is beneficial to know Java as well in data science. Let’s discuss those scenarios one by one:
- Java is helpful in model production
When you need to build an end-to-end data product, data pipeline building comes into the picture. Data is fetched from a source, features are calculated based on retrieved data, the model is applied to the resultant feature vector, and in the final step model results are saved or streamed to another ecosystem. Python is a perfect fit for model training but when it comes to model serving you need to use different tools.
This is the case when Java comes to the rescue as with Java you can implement commonly used data pipelines tools like Apache Hadoop, Apache Kafka, Apache Beam, and Apache Flink. If you are looking for building an end-to-end production model then Java has a wide range of applications.
- Low-latency system can be developed using JAVA
In order to productize a model, the Machine Learning model is exposed as an endpoint. Several Python libraries such as Flask provide this functionality but the performance of these libraries is not operational. If you need to cater to a large throughput and low latency capabilities in real-time the Python libraries are not feasible. Java provides you with a rich ecosystem for achieving low latency. If your requirements are to build feature vectors for models in real-time and serve predictions as an endpoint, using Java is beneficial.
Java is an object-oriented, versatile, and unique programming language with a wide range of capabilities. Because of its high performance and speed, it is one of the most in-demand abilities on the market. Security, network-centric programming, and platform independence are also included.
Java supports a variety of data science features for data scientists, including data analysis, data processing, statistical analysis, data visualization, and natural language processing (NLP). Java can assist in the implementation of machine learning algorithms in real-world applications. You can use batch and stream processing techniques to create adaptive and predictive models. It also simplifies the development of large-scale applications thanks to the REPL and lambda expression.
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