Scala vs. Java
Scala and Java are two programming languages commonly used for building data applications and data pipelines. Although these languages have distinct differences in terms of syntax, features, and capabilities, don’t think Scala vs. Java, but rather why use Scala with Java. Together, these languages streamline big data workflows and offer numerous advantages over using Java alone. In this article, we’ll compare Java and Scala, explaining their similarities and differences, highlighting their strengths and weaknesses, and showing how these two languages work together for more efficient development.
Scala vs. Java: Similarities and Differences
Let’s first evaluate the similarities and differences between Scala and Java, looking at seven key factors. This comparison provides an overview of the capabilities and strengths of each language.
Intended purpose
Java is a popular tool for creating data applications, data pipelines, distributed data processing systems, stream processing systems, and NoSQL databases. It is also the primary language for developing Hadoop-related tools, including MapReduce and YARN.
Scala is a general-purpose programming language built atop the Java Virtual Machine (JVM) that enables developers to build web apps, write back-end code for mobile applications, and build big data systems. As the name implies, it offers massive scalability for big data workloads, making it possible to process and analyze large amounts of data quickly and efficiently. It can leverage many of the same functions as Python. For this reason, it can also be used for building machine learning models.
Code type
Scala packs a lot of power into a few lines of code. That stands in contrast to Java, which tends to be much less succinct. Scala stays tight by treating everything as an object and by using type inference, a feature that allows the Scala compiler to infer the types of expressions automatically using contextual information. Type inference eliminates the need to declare them explicitly.
Backward compatibility
Java can run on an older or newer version without issue. However, Scala isn’t backward compatible and can only be run on the most recent version.
Lazy evaluation
Lazy evaluation is a feature only available in Scala. Lazy evaluation delays the computation of an expression until its value is actually needed—when it’s called or referenced. This not only enhances performance but can also help resolve issues with circular dependency.
Operator overloading
Another Scala-exclusive feature is its support for operator overloading, an advanced programming technique used to optimize code when one class has more than one method with the same name but different signatures.
Learning curve
The learning curve for Scala is much steeper than for Java. Scala’s more complex syntax and succinct, less-is-more approach to coding make it significantly harder for beginners to use. In contrast, Java is easy to learn. It's a well-structured language with a relatively simple syntax, especially when compared to Scala.
Community support
Java benefits from a rich collection of frameworks and libraries and an active community of users. Scala is a newer language with a much smaller user base. Although the popularity of Scala is growing, the collection of developer resources and community support is comparatively small.
Where Java and Scala Each Excel
Java and Scala address the needs of software developers in different ways. Let’s look at how Java and Scala are each used in building data applications, highlighting specific use cases for each.
Java
Data pipelines and analytics
Java is useful for storing, analyzing, and processing large data sets in ELT/ETL processes. Beyond data analysis, Java can also be deployed for a number of other data science use cases, including data import and cleaning, statistical analysis, and data visualization.
Hadoop development
Hadoop’s MapReduce program is a Java-based programming framework that interacts with Hadoop components. Its map function is used to filter and sort data, while its reduce function integrates the output results of the map function.
IoT systems
Java is an ideal platform for IoT devices and systems because of its flexibility and versatility. Java is platform independent, so it can run on many different types of devices and operating systems with no required changes to the code. For IoT devices with limited resources and different hardware, Java streamlines the deployment of IoT systems for numerous use cases, including consumer, manufacturing, quality control, and automation.
Scala
Data processing
Scala is an incredibly useful tool for data processing and wrangling. It facilitates interaction between distributed databases and enables parallel processing, making it possible to process massive amounts of data quickly and efficiently. One of Scala’s biggest contributions to big data processing is Apache Spark. Written in Scala, Apache Spark is an incredibly popular big data processing engine designed for working with data in a distributed computing environment, but it can be difficult to manage and expensive to scale. The Spark abstractions do not hide the inherent complexities of using distributed resources like memory and compute. Significant effort is devoted to infrastructure instead of focusing on data.
Machine learning
Scala's support for functional programming and its interoperability with Java make it well suited for use in machine learning (ML). In addition, Scala libraries for ML and natural language processing provide algorithms that can be used with Scala.
Stream processing
Scala is a popular choice for stream processing. Apache Flink, the popular open-source stream processing framework with support for real-time processing, is written using Scala. Apache Kafka is another popular tool for building real-time data pipelines and is commonly used with Scala and Flink to build stream processing applications. In addition, Akka, another popular Scala framework used for building distributed systems, also supports stream processing.
Using Java with Scala
Since Scala is designed to run on the Java Virtual Machine, its code can interoperate with Java code seamlessly. Scala can call Java code directly and make use of Java libraries and frameworks natively. Scala developers routinely use Java libraries and frameworks in their Scala projects, and vice versa. In addition, Scala code can use Java classes, methods, and variables in the same way as it uses its own. This tight compatibility allows developers to leverage the benefits of both languages within the same project, creating a powerful synergy for a number of big data use cases.
Building Data Pipelines in Snowflake with Snowpark
Snowflake seamlessly supports both Java and Scala. Snowpark is a developer framework for Snowflake that brings data processing and pipelines written in Python, Java, and Scala to Snowflake's elastic processing engine. Snowpark allows data engineers, data scientists, and data developers to execute pipelines feeding ML models and applications faster and more securely in a single Snowflake platform using their language of choice.
Unlock the full potential of your data and drive better insights and decision-making with Snowflake.