Today’s data engineers are dealing with massive volumes and velocity of data and are continually looking to be more efficient in improving data quality and speed of access. Scala and Python are two of the most popular languages for data engineering due to their ease of use, scalability, and flexibility. In this article, we’ll explore the key differentiators of Scala vs. Python for data engineering, the role that each plays, and how Snowpark is accelerating data engineering workflows with Python and Scala.
Use Cases for Python and Scala
Although Scala and Python each have their own strengths and weaknesses, they’re often used together to accomplish data engineering tasks. Here are several examples of how Python and Scala are used in data engineering.
Big data processing
Scala and Python both have a role to play in big data processing. Scala is the primary programming language used with Apache Spark, a popular distributed computing framework used for processing big data. Scala is an ideal fit for Spark's functional programming model and is a common language for Spark-based applications. Python is also frequently used with Apache Spark. PySpark, an API for Spark, allows developers to use Python to write Spark applications.
Machine learning
Python’s simplicity, versatility, and robust library ecosystem have made it a popular choice for machine learning and artificial intelligence projects. Scala is also commonly used in machine learning. MLib, a machine learning library used for data preprocessing, model training, and making predictions, is written in Scala. Scala’s support for functional programming and interoperability with Java have contributed to its popularity in machine learning applications.
Data pipeline development
When working together in data pipelines, Scala is frequently used for the heavy lifting of processing and transforming data. Its parallel data processing ability substantially reduces the amount of time required. Python is used for tasks such as data visualization.
Real-time data processing
Scala and Python both play an important role in real-time data processing. As mentioned, Scala is the primary programming language for Apache Spark, one of the most popular distributed computing frameworks for real-time data processing workloads. Python is used in real-time processing of data collected from IoT devices and the development of real-time applications.
Data extraction
Scala and Python each offer libraries that facilitate data extraction using web scraping. Python can extract data from social media APIs, including Twitter and Facebook, while Scala can extract data from sources such as CSV files and databases.
Data visualization
Both languages include multiple libraries that support data visualization. Popular Python libraries such as Matplotlib are used for creating both static and interactive visualizations, including highly complex heatmaps and three-dimensional visualizations. Scala leverages Spark's built-in plotting capabilities and visualization libraries, including Breeze-viz.
Key Differentiators of Scala vs. Python for Data Engineering
Although Scala and Python can be used for many of the same purposes, they differ in several key ways. Understanding the differences between these languages is important for choosing the right language for your data engineering tasks. In this section, we’ll explore the key differentiators of Scala vs. Python for data engineering.
Primary purpose
Python is a versatile programming language that is user-friendly, easy to learn, and suitable for various big data projects. With a reputation as the Swiss Army knife of programming languages, it is widely used in data engineering tasks, including data wrangling, building data pipelines, and machine learning applications.
Scala is a newer programming language that has a more specific focus, scalability, that its name alludes to. This scalability makes it an excellent choice for powering big data systems. Although Scala is capable of being used in projects of any size, its primary function is in the construction of large, data-intensive, distributed applications and systems. One advantage of Scala over Python is that it allows developers to leverage the entire library ecosystem of Java and use both languages interchangeably.
Performance
Python requires an interpreter to read and execute code, an added step that necessitates additional computational resources that can negatively impact performance. Scala is a compiled language, with the written code compiled into bytecode before its run on the Java Virtual Machine (JVM). This key distinction gives Scala a significant performance edge, with speeds up to 10 times faster than Python for certain use cases, including large-scale data processing and analysis.
Scalability
Scala has a clear advantage when it comes to maintaining performance at scale. As a statically typed language, Scala’s variable types are known at compile time. This feature allows its code to execute more quickly using less memory. Since Python is a dynamically typed language, it requires the interpreter to assign variables a type at runtime based on the variable's value at the time. This bogs down performance, especially when handling large-scale data processing.
Security
Statically typed languages align with the principles of type safety, a series of built-in controls designed to prevent type errors. Scala is a statically typed language and supports quick bug and compile-time error detection. Statically typed languages can identify potential type errors before they’re incorporated into the program and, as a result, are considered more secure than dynamically typed languages such as Python. Python does have a high degree of type safety built in, but because it’s a dynamically typed language, coding errors and bugs are harder to catch.
Concurrency
Concurrency supports better memory management and more efficient data processing by allowing several tasks to be executed simultaneously. Using Scala, developers can write code with multiple concurrency primitives to support running several tasks in parallel. In contrast, Python does not support concurrency. As a result, it requires additional time and compute resources whenever a new code is deployed.
Learning curve
It’s relatively easy to pick up the basics of Scala. But mastering its more advanced features requires a significant time investment. Python, however, has syntax that closely resembles the English language, making it a user-friendly language that prioritizes readability. For this reason, Python is the first language that many data engineers learn.
Community support
One of the key differentiators between Scala and Python for data engineering is the developer communities behind them. Here, Python has a clear advantage. It’s an older, more established language with a robust developer community. With an extensive collection of online resources, users from beginner to advanced can easily find support for their projects. Python’s large collection of frameworks and libraries allows data engineers and developers to work more efficiently. In contrast, Scala is a newer language with fewer use cases. Although Scala’s developer community and collection of frameworks and libraries are steadily growing, they are still much smaller.
Using Scala and Python Together for Data Engineering
By combining Scala and Python, data engineers get the best of both worlds, taking advantage of the strengths of each language to create a more powerful and versatile solution than would be possible with just one. An example of this synergy is ScalaPy, an API that facilitates interoperability between Scala and Python. With this API, developers can use Python libraries in Scala. In addition, with cross-platform interpreter embedding, developers can integrate Python into existing JVM applications or compile directly to native code.
Another option for pairing these two languages is Snowpark. Snowpark is a developer framework that brings native SQL, Python, Java, and Scala support to Snowflake for fast and collaborative pipeline development. With Snowpark, data engineers can write custom code in multiple languages, including Scala and Python, and run it directly within Snowflake. This makes it possible to perform data transformations, machine learning, and other data engineering tasks using Snowflake's cloud data platform.
How Snowpark Streamlines Scala and Python Development
Snowpark simplifies the development of data engineering applications. Using Snowpark, data engineers can use their language of choice, including Scala, Python, and Java, all within a single Snowflake development environment. Thanks to its unified interface, Snowpark can reduce the complexity and overhead of developing data engineering applications, freeing data engineers to focus on development, not managing operational complexity.
DataFrame API
Write queries and data transformations using familiar DataFrames. Operations are converted to SQL to scale out processing in Snowflake.
User Defined Functions (UDFs)
Execute native Java or Python code directly in Snowflake, including custom business logic or trained machine learning models. Leverage the embedded Anaconda repository for effortless access to thousands of open-source libraries.
Stored procedures
Operationalize and orchestrate your DataFrame operations and custom code to run on a desired schedule and at scale—all natively in Snowflake.
Reliable performance
Snowflake eliminates data silos created by legacy on-premises and cloud applications, allowing you to integrate and analyze data sets that were previously out of reach. Store and access your structured, semi-structured, and unstructured data in one location and gain seamless access to external data with similar scale and speed.
On-demand scalability
Snowflake was built from the ground up for the cloud. It is not bound by the limitations of a legacy on-premises solution ported to the cloud. Its multi-cluster shared data architecture separates compute from storage, enabling customers to elastically scale up and down automatically or on the fly.
Zero administration and management responsibilities
Snowflake handles maintenance, administration, and a host of other automated services so you don’t have to; no knobs to turn, nothing to tweak. In addition, Snowflake’s security and governance features were baked into the platform from day one, including end-to-end data encryption in transit and at rest.
Advancing Data Engineering Processes with Scala and Python
Scala and Python each occupy an important place in data engineering. Both programming languages have unique strengths, helping data engineers complete tasks quickly and efficiently. And when used in Snowpark, these two dynamic languages accelerate the pace of innovation in big data development projects.