Python vs. Java
Python and Java are both popular programming languages for app development, working with big data, and other data-intensive projects. But the question of Python vs. Java isn’t always a clear one. In many instances, both languages can be used to accomplish similar objectives, and they’re often used in tandem. Although Python is favored by many developers for data science applications, others prefer to use Java for certain data engineering projects. In this article, we’ll compare Python vs. Java side by side and highlight some popular use cases for both.
Comparing Python vs. Java
Python and Java are both products of the 1990s and have stood the test of time. Although there’s overlap between the two in some areas, there are distinct characteristics. Here’s how they stack up when compared with one another.
Java is a compiled language, meaning it is converted directly into machine code that the processor immediately executes. Generally speaking, Java is faster and more efficient than Python. In contrast, Python is an interpreted language—a program’s source code must first be converted into bytecode and then executed by the Python virtual machine. This additional step tends to make Python programs run slower than ones written in Java.
Ease of use
When it comes to ease of use, Python has a clear advantage as it follows a much simpler syntax written in a way that’s very similar to natural language. It’s far more concise and readable than Java. Python’s simplicity makes it possible for users to develop and iterate more quickly. Java is more complex and verbose, resulting in a steeper learning curve. It also requires more memory than Python. Compared with Python’s English-based syntax, writing code in Java is complex and unwieldy. The simplicity and clarity of Python code enables execution of the same functions as in Java but uses much fewer lines of code a.
Where each language excels
Python and Java are both highly versatile, popular programming languages. Although the decision of Python vs. Java will depend on the specific requirements of your project and the skills of the developers working on it, certain types of projects tend to benefit from one or the other. As a general rule, Java is more commonly used in enterprise-level applications. And because it has native compatibility with iOS and Android, Java is more popular with mobile developers. Python is used more frequently in data science, data analysis, and machine learning. Python is easy to learn, benefits from an extensive and open-source library ecosystem, and includes powerful visualization capabilities and strong community support, making it ideal for data professionals without an extensive programming background.
Popular Java Use Cases
Let’s look at several projects where Java is commonly used. Each of these projects highlights Java’s strengths.
Android mobile app development
Java is fundamental to mobile app development, especially apps built for Android devices, partly due to the Java Micro Edition (J2ME), a well-known cross-platform system for building Android applications. Java also has its own API and benefits from its own runtime environment, Java Runtime Environment.
Cloud and web app development
Java is incredibly useful for working with massive data sets and is often found powering key parts of cloud and web-based apps. Java’s large collection of systems, tools, and libraries makes it a popular choice for developers who build dynamic web applications. Java’s also a good fit for decentralized, cloud-based apps. It’s the language that controls some or all of the backend stack of today’s most popular apps.
Server apps for financial services
Java plays a dominant role in the financial services industry. Its robust security features and ability to handle vast amounts of data efficiently are vital when building exchange platforms, digital finance solutions, repayment and collection frameworks, and information handling frameworks. Java is also popular for creating server-side applications, facilitating a fast and reliable exchange of data between the client and the server.
Due to its efficiency, Java is useful for data engineers that need to perform large-scale data loading, transformation, and processing via ETL or ELT. Java lets engineers easily use custom business logic or change data formats for moving data between applications.
Popular Python Use Cases
Python is a true, general-purpose programming language that’s used in a wide variety of projects. The following use cases illustrate its flexibility.
Data professionals have quickly adopted Python for a range of projects. Python has a strong selection of libraries and frameworks for data manipulation and analysis, as well as deep integration with the big data technology ecosystem. Its simplicity makes it readily accessible to people without a lot of programming experience such as data scientists, researchers, and analysts. Snowflake users benefit from Snowflake’s Snowpark, which allows Python developers to write code and run it directly on Snowflake. Snowpark is designed to provide large-scale processing for Python without having to deal with any complex infrastructure management.
Python is easy to understand and use, making it an excellent fit for collaborative ML projects. Its simplicity facilitates faster model prototyping and product testing, helping teams deploy ML models in production quickly. In addition, open-source app frameworks such as Streamlit help data scientists and developers create shareable web apps with no front end development experience. Coded in Python, Streamlit is used to create beautiful apps that provide nontechnical team members an opportunity to engage with ML models, building user trust and creating a collaborative feedback cycle that accelerates the pace of innovation. In addition, with Snowpark, data scientists and developers can use Python to accelerate feature engineering efforts by using familiar programming concepts such as DataFrames and then execute these workloads directly within Snowflake.
Python is also a popular option for web development. It’s highly versatile, allowing developers their choice of several programming paradigms including both object-oriented programming (OOP) and functional programming (FP). It also has dynamic typing capabilities, so Python scripts don’t require compiling before execution. As a result, scripts are executed at runtime, resulting in less coding and easier debugging.
As a general purpose programming language, Python has found broad adoption in many different IoT use cases as it plays an integral part in the management of complex networks of connected devices. IoT networks have a wide variety of industrial applications including quality control and predictive maintenance for manufacturing and supply chain management. In healthcare, Python is powering AI-assisted medical image diagnostics, new drug development, and hospital resource management programs.
Advance Your Data Science Initiatives With Snowflake in the Language of Your Choice
Move your big data projects forward with Snowflake. With Snowflake Snowpark, build scalable pipelines, machine learning (ML) workflows, and data applications directly in Snowflake using your preferred programming languages including Python, Java, or Scala with familiar DataFrame and custom function support. Snowpark is empowering data teams to build faster and better while collaborating in new ways.