Future of Data Engineering in Rust?

Rust, although it has been around for some time, is gaining momentum in the data space, similar to the rise of Julia. It is renowned for its speed, performance, and reliability, making it an excellent choice for constructing high-performance data pipelines and applications. Some compelling advantages of Rust cannot be overlooked.

Speed and performance: Rust is a compiled language, which means it is transformed into machine code before execution making it much faster than interpreted languages like Python and R.

Reliability: Rust has a strong type system and an ownership model that together guarantee memory safety and thread safety. This means that Rust programs are less likely to crash or have other runtime errors.

Concurrency: Rust makes it easy to write concurrent and parallel programs. This is important for data engineering, business intelligence, and data science workloads, which often need to process large amounts of data in parallel.

Maintenance: Rust’s ownership model and strong type system make it easier to write and maintain code. This is because Rust can catch many potential errors at compile time, rather than at runtime.

Here are some specific examples of data science workloads where Rust is a good choice:

• Real-time data processing: Rust is a good choice for building real-time data processing pipelines. For example, Rust could be used to build a pipeline that processes streaming data from sensors or financial markets.
• Machine learning: Rust can be used to build high-performance machine learning models. For example, Rust could be used to train and deploy a machine learning model for fraud detection or image classification.
• Data visualization: Rust can be used to build interactive and high-performance data visualizations. For example, Rust could be used to build a web-based dashboard that visualizes real-time data from a variety of sources.

If your team is dealing with massive amounts of data or the performance of the code impacts the project , then Rust could be a good choice for your project. For example, Rust could be used to build a data pipeline that needs to process large amounts of data in real-time. Or, Rust could be used to build a machine learning model that needs to be deployed to a production environment.

In my opinion, Most Companies struggle with Data maturity and adaptability in the business and do not have to deal with massive data sets from the get-go, and Python would be more than sufficient to fulfill their immediate requirements. So a big overhaul in the preferred programming language doesn’t seem to be around the corner. Data space will still be dominated by Python for the foreseeable future but teams can use the interoperability features at the later stages, where teams can use Rust through wrappers or Foreign Function Interface (FFI). Rust can be called easily from C, C++, Ruby, Python, and vice-versa.

While adapting Rust, teams also should be mindful of the below.

• Learning curve: Rust has a steeper learning curve than some other popular data science languages like Python and R. This is because Rust is a systems programming language with a focus on memory safety and thread safety.
• Ecosystem: Rust’s ecosystem is not as mature as Python’s or R’s. This means that there are fewer libraries and tools available for Rust, and they may not be as well-maintained.

I hope this article gives you a quick rundown of some of the factors you need to consider before adapting to Rust. Happy to answer any other questions you might have in the comments.