Java Tools For Data Science

Big data, the recent buzzword in the IT and corporate environment, has been in the mainstream for a little over 5-years.

But the rate at which the World is generating enterprise data is outpacing our computation capability of analysing it efficiently. The challenge here is to transform these voluminous data streams from a liability into power and translate them into tangible business value.

Thanks to Java — a platform-independent and robust programming language with significant application in data science, machine learning, and artificial intelligence domains and helps data scientists develop power-packed data-driven products.

Let’s go through the Java tools for data science and look into how they enable companies to automate and function at a higher level by enriching their decision strategies.

Why Use Java in Data Science?

Data Science is a study area that aims to develop scientific techniques for efficiently fusing interfacing, machine learning algorithms, deep learning, statistics, and predictive and text analytics to help data experts mine massive troves of data sets from different sources, analyse them to acquire actionable business insights and assess their analytics maturity.

Thanks to Java — it supports an array of data science techniques, for instance, deep learning, data mining and cleaning, data processing, analysis, statistical analytics, visualisation, and text analytics (NLP).

Coupling with Big Data analytics, Java enables data analysts to implement ML algorithms on mission-critical enterprise-grade applications and data-based products.

Though it may need considerable effort to ramp up Java coding, it’s great for devising data-driven products that support real-time data streaming or have low latency requirements.

Let’s look into the features that make Java so popular among data scientists:

Java Frameworks for Data Science: Java comes with multiple chart-topping frameworks, for instance, DL4J, ND4J, Apache Mahout, etc., with high-level predictive models without making your IT infrastructure make alternations in the current technology stacks. We’ll dig deeper into the Java frameworks for data science and data analysis later on.
Java is Type-Safe: The Java compiler validates data types at the compile-time and shows alerts of type errors. The type-safe feature is essential for data experts as it allows them to shorten the required data analysis time, bypassing codebase maintenance and unit testing.
Scalability: Java is a highly scalable language that allows scaling up and scaling out components and features besides load balancing for improved performance. Plus, you get a spectrum of data analysis toolkits, a big community of coding whizzes, and cross-platform support with Java. These make Java excellent for helping build convoluted Big Data infrastructures from the ground up.
Data warehousing and OLTP programs execute batch processing relying on mainframe systems. And Java can integrate with 3-tier architecture better than other coding languages. Plus, it’s seamlessly integrable with powerful middleware and COBOL. For any industry that aims to accelerate the pace of its investment in data analytics, big data initiatives, and high-level systems supporting OLTP design, Java is the best choice.

Java Tools for Data Science

Let’s uncover the Java frameworks for data science and big data tools written in Java:

Apache Hadoop

Apache Hadoop, an open-source data science software by Apache Foundation, released under the Apache Licence 2.0, is a Java-written coding framework best for distributed processing of large data sets.

It employs parallel processing across the nodes for faster computation. The data chunks are distributed among these nodes in the Hadoop cluster and parallel processing facilitates intricate and data-intensive computation tasks.

Key Features

Offers flexible and accelerated data processing
Hadoop cluster supports horizontal scaling (adding unlimited nodes) and vertical scaling (boosting hardware capacity of those nodes) for improved scalability.
Hadoop decreases the bandwidth utilisation in a system by running on the data locality concept — instead of moving data to the computation, Hadoop shifts computation closer to where data is generated.

Apache Spark

Built on Apache Hadoop MapReduce, Apache Spark is another chart-topping open-source top-end big data processing engine that can execute large-scale ELT tasks and advanced data analytics in a breeze.

Spark is a Scala-based framework that supports language interoperability with Java.

Key Features

Spark supports in-memory cluster computing for faster parallel processing.
It seamlessly connects to different data sources, like S3, HBase, HDFS, and Cassandra.
Spark Streaming offers dynamic, high-throughput, fault-tolerant, and scalable live data stream processing.

Apache Mahout

Apache Mahout, an Apache Software Foundation product, is an open-source framework and big data system that allows FREE implementation of distributed or otherwise scalable ML algorithms based on linear algebra. It’s a mathematically explicit scala DSL implemented on top of Apache Hadoop for efficient scaling in cloud infrastructure and employing the MapReduce paradigm.

Once big data gets consolidated on the HDFS layer, Mahout enables data science software to automatically identify the characterisation and hidden patterns in those data sets.

Key Features

Mahout’s core algorithms contain clustering, collaborative filtering, recommendation mining, and frequent item-set mining.
Supports vector and matrix libraries
Offers fault tolerance

Java JFreeChart

JFreeChart is a Java-based open-source library that allows data scientists to visualise complex data sets using high-quality interactive and non-interactive data-centric charts.

Key Features

It comes with a well-documented and compatible API to support a wide variety of customisable chart layouts — line charts, bar charts, Gantt charts, wind and bubble charts, pie charts, and more.
Offers plug-in support in different IDE’s — Netbeans, Eclipse, Netbeans, etc
Multiple result formats supported — Swing and JavaFX layout components, image (PNG, JEPG), and vector graphics (EPS, PDF, SVG).

Deeplearning4j

Developed by Eclipse, DL4J is the only Java-based distributed and open-source framework for JVM with a spectrum of built-in tools to facilitate working with deep learning algorithms.

Authorised under the Apache Licence 2.0, DJ4J ensures prompter prototyping for people with no/minimum technical flair and allows deploying neural net models via Keras from most leading frameworks.

Key Features

Multiple OS supported (Windows, Linux, Android, macOS, and iOS).
Micro-service architecture supported
Integrable with Apache Spark and Hadoop
Powered by ND4J, a numeric computation library for JVM

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Swanintelligence

Data Analytics Expert at Swan Intelligence

Dr. Alexander Turing is a visionary in the field of AI and big data analytics. With a Ph.D. in Computer Science and over a decade of experience in technology research, Dr. Turing brings a wealth of knowledge and insight into the evolving world of business intelligence. His passion for uncovering hidden patterns and driving innovation makes him a leading voice in the industry. At Swan Intelligence, he explores the intersection of AI, big data, and business strategy, offering readers a glimpse into the future of technology-driven decision-making.

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