The Nucleus Research team forecasts that in 2023 there will be generated high volumes of time-series data that many organizations will struggle to organize and manage. Thus, the business world is in urgent need of fast and secure data processing and analytics solutions that reside on next-gen programming. This is why the Rust programming language is rapidly gaining traction in data processing, machine learning, and big data communities.
Data scientists actively embrace Rust as it scales well with multicore processors, is memory safe without garbage collection, and has good parallelism and concurrency built-in. To form an unbiased opinion about Rust, you should read the comparative article about Rust vs Go popularity, which also includes a comparison of these languages in the data science context.
Let’s explore why choosing Rust for data processing is beneficial today and what this language has to offer that Python or other typical data processing programming languages couldn’t so far.
Current data processing and big data challenges
There are a number of challenges that the current data processing and big data market is experiencing. Below are some of those that lie on the surface for the industry in general. But, of course, each individual data engineering project may have its own challenges.
Scalability. One of the biggest challenges facing the data processing industry today is scalability. With more and more data being generated every day, companies need to be able to process all this information quickly, efficiently, and cost-effectively. It isn’t enough for systems to perform millions of transactions in seconds; they also need to be able to scale up as more users come online and use them simultaneously.
For instance, with Python, one of the most common languages for data engineering projects, it can be difficult to scale when dealing with large volumes of data. This is due to Python’s lack of support for multi-threaded applications.
Security. Another major challenge is security. Companies must ensure that their systems remain secure at all times — not only against external threats such as hackers but also against internal ones like employees who may have access to confidential information that they shouldn’t have access to in the first place.
Processing speed. Businesses need fast results when they are working with large amounts of data. This means that they cannot afford to wait for their machines or software programs to complete their tasks before they can proceed to the next step in their workflow process. If companies do not have access to faster machines or software programs that can handle large amounts of data efficiently then they will lose out on potential revenue streams as well as opportunities for growth within their respective industries.
Typically, Python, Java, or Scala are the go-to programming languages used for tackling all of the above challenges and ensuring that a data application is high-performant, scalable, and foolproof. But what if we tell you that Rust isn’t only capable of solving lots of challenges much quicker but also with much less risk, bugs, and overall development frustrations? See for yourself in the next section and make sure to check out a Rust data engineering book to learn more about the language in this context.
Rust is winning a place in the sun in the data engineering world
Like no other programming language out there, Rust can show lots of not-so-obvious errors and bugs during the compile time. Developers don’t have to search for those mistakes manually and don’t have to nervously wait until production runtime to only see that their code fails. No unexpected behavior — that’s what Rust is all about. And such a blessing is exactly the reason why so many data engineers start giving Rust a try.
Let’s take a look at each of the challenges from the section above and see what weapons Rust has in its arsenal to tackle them.
Rust and scalability. When the load on your application increases, the performance can degrade, making your application unusable. Application scalability is intertwined with concurrency, as it’s ultimately about doing more tasks within a certain time period. Rust has excellent concurrency support so that you can write multithreaded code that’s safe, correct, and scalable.
Many other languages have support for concurrency built in (such as Go), but Rust takes it a step further by allowing for asynchronous programming patterns that can help to reduce latency on both client and server-side requests.
Rust and security. Rust is now being used to build secure systems because it provides a safe system programming environment, which means that it has no data races or memory leaks. Thus, with Rust, you can write more reliable and secure code and software.
Rust and data processing speed (latency). Rust can eliminate the common causes of latency: memory allocation failures and runtime errors like segmentation faults. Rust also allows developers to write less code than other languages and maintain a high level of performance.
Using Rust for big data projects can significantly elevate the development process. Even though this language still isn’t widely used in data science, it sure has a big future ahead as long as more and more companies unleash its potential. Just as Apache Spark, the globally known open-source data processing tool, launched a project completely written in Rust, called Vega. It’s still a work in progress but the performance promises are intriguing.
Rust tools for big data processing
In this section, we’d like to list a few data processing and data streaming tools, written entirely in Rust.
Fluvio. It’s a data streaming platform that was built for running real-time applications. Fluvio is extremely high-performance and easily programmable.
Yata. This is a library written in Rust for high-performance technical analysis. It uses the majority of common technical analysis methods.
Weld. It’s a runtime program mostly written in Rust for data-intensive applications. Weld is there to significantly optimize the performance of such applications.
Polars. It’s an extremely fast data frame also mostly written in Rust. This library has no dependencies and is lightweight and can perfectly work either with Rust or Python.
The Rust language gives you more flexibility when developing any data processing solution. Make time to thoroughly explore the language as well as diverse Rust use cases to witness its powers in full swing.
Tips on how to start a data project in Rust
Coming to some practical matters, let’s discuss how you can start a project in Rust and succeed already in the first attempt.
- Learn the basics first
Before you start working on your own project, make sure you know how to do basic things with Rust. This includes setting up an environment and installing tools like cargo (a package manager) and rustup (a compiler). You should also check out the documentation for nightly releases and stable releases, as well as the cheatsheet for quick reference.
- Set up best practices
Once you’ve got the basics down, it’s time to set up best practices for your project. This will make your life easier in the future because it will allow other people on your team to understand what they need to do when they want to contribute code or suggest changes in their PRs (pull requests).
- Think about what problem you’re solving
The next step is figuring out what problem you’re trying to solve with data processing. The answer might be something like “I want my customers’ orders placed in the last hour” or “I want a list of all customers who haven’t paid their bill”. If you don’t know what problem you’re trying to solve with data processing, it will be difficult to use Rust appropriately.
In short, Rust is very well-equipped to become the language of choice for big data applications. It provides a safety net through its ownership and borrowing rules, which will make it easier for end users — many of whom are not programmers — to write their own big data solutions in Rust. The wide variety of available tools is another point in favor of Rust. And finally, Rust’s error-handling model enables programmers to tackle errors in ways that speed up the development process without any significant reduction in code quality, which is extremely important in data engineering.