BDA for Java Developers
If you think that Big Data Analytics (BDA) is a buzzword, think again. Almost every industry is now using Big Data, from healthcare providers to finance. Big Data is now having a huge impact on mobile app development, and leaders in the field are exploring how to use AI for big data analysis.
All this said, working with BDA in Java has been somewhat overlooked. This is strange because many of the tools used by developers working with BDA are written natively in Java. In our guide to big data, in a nutshell, we looked at some high-level tools for BDA, but in this article, we’ll take a more fundamental approach, and give you some tools to work with Big Data directly from Java.
Big Data Analytics in Java
Many developers working with BDA don’t touch Java. That’s a shame because Java offers a number of advantages for working with Big Data. Foremost among these is that the Java runtime is inherently portable, and so can be run anywhere, and on any hardware or software platform. The stack provisioning that Java offers, and particularly its garbage collection and automatic memory distribution also make it a natural choice for working with BDA.
Java can be used to undertake BDA for (almost) any complex data acquisition system, but the most common application is to analyze data from eCommerce stores. Used in conjunction with high-performance web hosting providers, developers can segment and analyze visits and sales to an unparalleled level of granularity. These data can then be used to underpin highly targeted marketing strategies, and to make predictions on future sales.
Alongside this type of deployment, many developers also find that using Java for BDA can be used to improve the security of their systems. One of the major lessons to be drawn from the high profile data breaches of the past few years is that the sheer amount of data that the average developer now has access to – and responsibility for – means that it is difficult to retain oversight on it.
This is particularly true during scaling processes. Many businesses, after reaching the limit of what they can achieve through their current data infrastructure and eCommerce store, will migrate to an interconnected group of systems that handle eCommerce, website analytics, and marketing statistics independently.
For most businesses, given the popularity of WordPress, the first instance of this will be when they migrate their WordPress site, but any migration process of this kind can lead to an explosion of data for developers to deal with. By working with Big Data at a more fundamental level, through Java, it is easier to keep control of the data generated by acquisition systems.
Big Data Tools for Java
Working with BDA in Java relies on a number of tools. Most of these are open source, and when used together they form a BDA stack that provides a powerful level of functionality. Here are the most commonly used tools.
Most developers looking to implement BDA in Java will start with Hadoop. This tool has been built (and made freely available) by the Apache Software Foundation and offers a Java-based programming framework for working with Big Data across a distributed computing environment.
Because of this, the tool has become extremely popular for organizations who want to store huge amounts of data on one system, and perform analysis on another. In addition, Hadoop offers a whole ecosystem of tools for working with Big Data through Java: everything from machine learning systems to advanced search functionality:
Apache Spark is similar to the MapReduce component that is found in Hadoop but is becoming more popular than it’s rival due to improved performance and resilience. Spark makes use of an RDD (Resilient Distributed Dataset) that not only makes working with Big Data more efficient but can also improve cybersecurity.
The language that underpins Spark is Scala, which itself is based on Java. Because of this, Spark offers an extensive Java API and is easy to work with for Java developers. And just like Hadoop, in recent years Spark has expanded to offer a complete ecosystem of tools for working with Big Data.
Apache Mahout is a more narrowly focused tool for working with Big Data. It provides a machine learning framework that can be used for recommendations, clustering, and classification. It runs on Hadoop, and can, therefore, be integrated easily into distributed environments.
Jfreechart is focused on a different part of the BDA workflow. It provides data visualization tools, written natively in Java, that can be used to generate a wide range of charts and plots from your data. When used in conjunction with data analysis tools like Hadoop and Apache Spark, Jfreechart can be configured to automate the process of visualization and produce dashboards that will quickly show you key trends in your data.
Deeplearning4j is a Java library that is used to design neural networks for use in BDA. It can be integrated with Apache Spark or Hadoop and can be easily scaled on either. It can run on distributed networks, and can even be configured to run on GPUs, making the best use of the computing resources available in the average organization.
Apache Storm is an alternative to Apache Spark. At first glance, both systems appear to function in a similar way. However, Storm is focused on providing true streaming functionality through Java; whilst Spark appears to offer this, in reality, Spark acts as a wrapper around batch processes. Because it is based on a true streaming model, Storm has become popular for online systems that require lightning-fast data analysis.
As a Java developer, it makes sense to use your skills when working in BDA. Using Java APIs in your Big Data systems gives you more control over them than using high-level tools, which is important for both system resilience and cybersecurity. The tools we’ve shown you above are all based on Java, and so can be picked up quickly by anyone with experience in the language.
If you want to go further, though, you should read our guide on leveraging big data, where we explain just how much you can get out of BDA systems.