Java has been the go-to coding language for decades, but as advancements in big data processing continue to emerge, Java developers are forced to learn new skills and explore additional programming languages. That is especially true when developers start working with massive amounts of data and need more elegant solutions, faster.
As an alternative to Hadoop, Apache Spark is gaining popularity in the software development world. Spark is a fast, data-processing platform that is perfect for working with big data. Its creators call it a "unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing."
Apache Spark can process analytics and machine learning workloads, perform ETL processing and execution of SQL queries, streamline machine learning applications, and more. But, one of the most important differences when working with Apache Spark is that it allows for the user to perform multiple operations simultaneously, taking the complication out of working with large or unwieldy data or complex machine learning algorithms, and instead crafting an integrated and complete data pipeline. Even more exciting for developers, all these actions are usually performed in memory, only moving to disk when memory limitations present.
In terms of programming languages, Spark is written in Scala, but it also supports Java, Python and R. Scala is a functional programming language, which is important for processing big data because it offers immutability, pure functions, referential transparency, lazy evaluation, and composability. All these features make it easier to develop applications in a distributed environment, just as long as developers can get past the language's steep learning curve.
For engineers that already have a good handle on Java 8 and beyond, there are aspects of Java worth mastering that can boost your familiarity with Spark. Here are the components worth focusing on and why Scala is a great language to pick up if you've already mastered Java.
Unlike Scala, Java is not a purely functional programming language, but it still has a few functional programming features that can translate well over to Scala.
Java 8 onwards includes the lambda expressions feature, which helps define methods without declaring them. Lambda expressions allow behaviors to be passed to functions, much like anonymous inner classes, but far more concise.
For Java developers, it's important to get familiar with Lambda expressions as they are an important aspect of functional programming when it comes to working with Apache Spark.
As developers began to request more functional programming features, Java introduced the Streams API in Java 8. Considered one of the more advanced features of Java, streams allow developers to perform sophisticated data manipulation operations, including searching, filtering, or mapping data.
Although streams are not well suited for use with big data, Java developers can use them to learn the basics of Spark because the paradigm is very similar. The Spark Streaming API handles operations such as transformations, checkpointing, and design patterns in much the same way as Java's streams, so Java developers with knowledge of Java streams should have an easier time transferring their skills.
The purpose of generics is that they allow types to become parameters when defining classes, interfaces, and methods, enabling developers to reuse code with different inputs. Effectively, generics allow you to reduce the amount of specialized code, avoid or minimize boilerplate code, and use generic algorithms that apply to various types. All benefits that create more streamlined code, but also get developers thinking about defining more generic characteristics.
Generics have been around in Java since 1.5, so most developers have a good handle on this already and can migrate those skills over to Scala or Apache Spark.
Since Java 8, many interfaces in Java's standard library comply with the requirements of functional interfaces, so they are worth looking into if developers want to focus on more functional programming.
While Java requires functional interfaces to do functional programming, functions are first-class citizens in Scala, which has many functional features already built right into the language.
In terms of developing a full-blown big data application, Java has—and will probably always have—good levels of performance and reliability. However, while Java is well-suited to production projects and data pipelines, it is not well-suited to the kind of exploratory analysis required in Spark big data projects.
One of the main use cases for big data is data exploration, a process that commonly involves issuing Hadoop queries using tools like notebooks or Shell, which can't be used with Java for Spark. Integrated Development Environments (IDE) are great for writing and debugging Java programs, but Scala (or Python) is more suited to exploratory data analysis, with notebooks allowing the execution of several snippets of code. For instance, one paragraph in a notebook can be used for gathering data, another for consolidating data, and others for cleaning it. Java also does not provide the same number of libraries as Python, Scala or R for this purpose.
Although the features and functionality of Java are improving in each version, and the language is broadly used and understood throughout the software development industry, it is lagging the likes of Scala and Python when it comes to big data.
Developers can stand out from the crowd by learning at least one language in each of the different coding paradigms, such as imperative, logical, functional, and object-oriented programming (OOP). Scala supports both functional and OOP, so it's a valuable language to understand, especially if you want to add big data and Spark to your skillset.
For many developers, learning new technologies, methodologies, tools, frameworks, etc.. is just another day at work. However, learning new skills with the support and resources necessary to master them is another story. At Perficient Latin America, we believe in fostering an environment of continuous learning; it's one of our core values. We do our best to prioritize training and development for each individual and give them the space and resources they need to try new things, explore their potential, and share their knowledge and skills.
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About the author: Juan Sossa is a Big Data Tech Lead at Perficient Latin America. With 10+ years of experience in the IT industry, he has worked as a Senior Software Engineer, IT Consultant, Big Data Engineer, and Professor. His experience includes integration projects with large volumes of data in industrial sectors that process large-scale processing and storage. Currently, in Perficient Latin America he works on big data projects to facilitate data analytics of complex data, making it suitable for exploratory data analysis, visualization, and machine learning models.