Events continuously happen all around the world. Often, we create a record of a unique event at a specific time and space. These recorded events collectively represent what we know as data. In contrast, data science uses scientific processes, systems, methods, and algorithms to abstract knowledge and insights from these structured and unstructured data. But if you are still lagging and not aware of growing trends of data, then learn about it from the Data Science course.
Moreover, Data Science is an interdisciplinary approach that blends principles and practices from different fields like statistics, computer engineering, artificial intelligence, and mathematics to analyze the vastly available data.
As modern businesses are inundated with data, Data Science uncovers unknown transformative patterns, provides real-time optimization, and helps innovate on-demand products and services.
But where does Java come in the middle of data science and how can it enhance it? This blog will cover everything about Java and Data Science that will help you understand why Java is beneficial for Data Science.
Without further ado, let’s get started!
A Little Introduction to Java
Java, an object-oriented, functional, reflective, imperative, concurrent, high-level programming language, was designed by James Gosling at Sun Microsystems in 1995. Java was designed to be a general-purpose, WORA (write once, run anywhere) based programming language with as minimal implementation dependencies as possible.
Read: How to Build Microservices in Java
Simply put, once you have compiled Java code, it can run on any platform supporting Java without recompiling. It is a widely used language among software developers for over two decades. It has been popular among programmers due to its interpretability, reliability, security, and speed. Some common use cases of Java include cloud computing, artificial intelligence, big data, the internet of things, and game development.
Why Java for Data Science?
Well, many data scientists tend to lean toward R for data visualization or Python for quick algorithms experiments and REPL capabilities. But, Java contributes to multiple machine learning and AI use cases. Besides, technologies like Hadoop, Hive, Cassandra, Spark, and Flink all are essential for Big Data processing, however, they run on Java Virtual Machine (JVM).
Undoubtedly, Java is an invisible force behind multiple applications and devices, making it one of the most preferred programming languages by developers for numerous reasons, including but not limited to the following.
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Even after being the oldest language for enterprise application development, Java isn’t outdated. A survey at Stack Overflow stated that over 33.4% of professional developers are more likely to use Java over languages like PHP, C#, Kotlin, R, Swift, Go, or Rust. What contributes to the popularity of Java are its large user community, friendly operations, frequent updates, versatility, and numerous frameworks. Another reason why Java has multiple applications across industries is the simplified integration and minimized compatibility issues it offers.
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Most of the popular tools and frameworks for Big Data like Hadoop, Spark, Hive, and Fink are written in Java.
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Java has several applications in data analysis including data import and export, data cleansing, statistical analysis, deep learning, data visualization, and natural language processing.
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JVM is considered one of the most robust platforms for data science and machine learning as it allows developers to write identical code that can be used across multiple platforms. It also allows programmers to build custom tools and features of IDEs swiftly that enhance productivity.
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Lambda expressions of Java 8 allow developers to specify functionalities in minimum lines of code that can be called right away. This in return simplifies the development of complex data science projects.
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Java offers some powerful frameworks like ADAMS, DL4J, RapidMiner, Weka, etc. to be used to build data science solutions. These frameworks can be used for advanced data mining, deep learning, machine learning, knowledge analysis, and object-oriented artificial neural networks.
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Being a statically typed language, Java executes type checks during code compilation reducing the execution time and making the management of large data science applications easier.
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For data science projects, scalability is one of the key aspects that developers need to consider. As Java makes scaling even a complex and large project efficient, it becomes a great choice for creating Data Science applications.
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Java’s extraordinary code structure offers clarity to the developers about data types, data sources, variables, etc. they have to work with, making it easier to retain the code base.
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Java has a well-developed set of mechanisms. It also makes the developers highly productive because of a range of mature elements and IDEs.
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Java for Data Scientists: When to Use It
Although languages like R and Python have rich ecosystems to handle numerous problems in data science, in some situations, Java needs to be explored.
Read: Introduction to Apache Beam Using Java
By learning Java for data science, you can explore a broader range of data products. A few scenarios where using Java is beneficial for data scientists are as follows.
To build a low-latency system
If you want to build a low-latency system that includes feature vectors for the project in real-time and offers predictions as result, then Java could be an ideal choice for you. With the rich ecosystem of Java, it becomes easier to do so. You can use Java with NoSQL databases such as MongoDB, Redis, or Couchbase to attain low latency.
Read: MongoDB vs DynamoDB
To put models in production
In large-scale businesses, often data scientists are separated from spinning infrastructure and managing live data products responsibilities. Instead, they are provided with platforms like Databricks or AWS Sagemaker to run scheduled tasks or share model specifications with the development team using predictive model markup language (PMML) format and to deploy models in production respectively. But being a proprietary tool, AWS cannot be considered the best fit for different data products.
Read: Data-Oriented Programming in Java
So, the choice of developing an ML model with real-time prediction will depend on the way the model is served to the user. Simply put, a model with a streamlined pipeline will utilize distinct elements than an API-hosted model.
Therefore, if you are responsible for developing data models you will need a data pipeline where data is acquired from the source, features are decided according to the data retrieved, and a model is added to the resulting feature while being stored in another system.
Read: Java Developer RoadMap
As Java has been used to implement data pipeline tools like Kafka, Hadoop, Flink, and Spark, it has become an ideal language for building data aggregating and retrieval systems.
Java Libraries and Platforms for Data Science
Now that we know why and when to use Java for data science, let’s take a look at the top Java libraries and platforms that one can use for data analytics or data science projects.
Read: Event-Driven Microservices using Java Spring Boot Cloud Stream
Apache Mahout
It is a distributed, scalable, open-source project by Apache Software Foundation that is used to create machine learning algorithms. The core techniques on which Mahout works include clustering, recommendation mining, frequent item-set mining, and classification. Apache Mahout also offers Java/Scala libraries to execute common mathematical operations, primarily focused on statistics and linear algebra. Mahout is all about machine learning and makes a robust tool for developing software for business intelligence.
Apache Spark
It’s an open-source, unified, large-scale data processing framework. Spark is a fast and unified analytics engine used for big data analytics and machine learning. Brands like Yahoo, Netflix, and eBay use Spark at a large scale to process petabytes of data. Moreover, Spark provides distributed task scheduling, dispatching, and input/output functions through API for Java and centers on RDD(resilient distributed dataset) abstractions. The standard package of Spark including SQL queries, high-level libraries, machine learning processing, and streaming data increases the productivity of the developer while helping them to develop complex workflows.
Deeplearning4J
Eclipse DeepLearning4J is a set of tools written in Java that runs deep learning on the Java Virtual Machine (JVM). It enables developers to train Java models while interacting with the Python ecosystem. Deeplearning4J comes with multiple submodules such as Samediff, Libnd4J, ND4J, and Datavec. It is a platform with vast support for deep learning algorithms.
Java-ML
Java machine learning library is an open-source framework written in Java that aims to provide several algorithms for machine learning for developers. It is a set of data mining and machine learning algorithms that can be readily used by data scientists and software engineers for faster project development.
Apache Hadoop
It is an open-source framework used for processing a large set of data across multiple computer clusters. With Hadoop, you can efficiently store and process data ranging from gigabytes to petabytes in size. By using multiple clusters at once, Hadoop makes analyzing enormous amounts of data quicker.
Weka
A collection of algorithms for machine learning, Weka helps in data mining. It offers a modern platform for data that can manage the most intensive I/O (input or output) workloads for latency-sensitive projects.
Apache Flink
Last but not least, Apache Flink is a unified, open-source data processing engine written in Java and Scala. It executes dataflow programs in a pipelined and data-paralleled way while offering low latency and high throughput for event processing and state management.
Read: Apache Kafka
Scope of Java for Data Science
Undoubtedly, data science will continue to disrupt organizations but for all the right reasons. But, if you think that the tech stack you are using to achieve your data analytics goal is causing restrictions, you can try to expand your model using Java. Get a skillset from data science course to boost your career.
Read: Best Tech Stack for Web App Development
Needless to say, Java is an extremely fast, robust, reliable, secure, scalable, and overall useful high-level programming language that an organization can use to build numerous projects for different industries including data science.
Whether you want to build a system with incredible data analysis and data mining techniques or fast and efficient machine learning applications, Java is more than capable of attaining your goals, making it a preferred language for developers.
Read: Best Practices for Unit Testing in Java
So, if you are a business owner who wants to build a Java-based data science project, it’s recommended to hire experienced and highly skilled Java developers who can assure the success of your web app once launched in the marketplace.