21CTO Guide: The Rust programming language is gradually becoming the preferred choice for many companies handling data-intensive tasks due to its outstanding performance, safety, and modern design philosophy. A typical example is Singular’s Extract platform, which outperforms traditional tools in both performance and cost-effectiveness.
Although Rust’s ecosystem is not yet as mature as Python’s, its memory safety and efficient performance give it a significant advantage in the field of data engineering.
Nowadays, the Rust programming language is expanding into new territories, including enterprise-level data pipelines.
Despite Python and Java still being the primary languages for data processing in most industries, more and more companies are beginning to recognize the unique combination of Rust in terms of performance, safety, and modern design, which is making it an innovator for data-intensive workloads.
The latest evidence comes from Singular, whose Extract platform has achieved a performance improvement of 17 times and reduced costs by up to 70% — all thanks to the strong support of Rust.
Why Rust is Crucial for Data Engineering
Rust addresses fundamental issues that have long plagued data engineering.
Most ELT platforms run on languages designed for different purposes, with Python aimed at simplicity, while Java is designed to meet enterprise needs.
Gadi Eliashiv, the CEO of Singular, stated that both languages come with significant performance overheads: unpredictable garbage collection pauses, memory-hungry runtime environments, and abstraction layers that isolate developers from the underlying hardware.
However, he pointed out that Rust eliminates these trade-offs. It offers performance comparable to the C language while ensuring memory safety, preventing a class of errors, all integrated into a modern language design that does not make developers feel left behind.
“I love this because I truly feel the power at my fingertips,” Eliashiv added.“It feels like I am writing code that could belong to the kernel level. It is incredibly efficient, and at the same time, this language is very modern.
The Memory Efficiency Revolution
Numbers speak for themselves.
Singular’s team compared their legacy Python implementation with the new Rust version’s equivalent connector code. Using Rust, they achieved a 20 times reduction in memory consumption. For a multi-tenant SaaS platform, this is a huge breakthrough, he said.“We can basically accommodate 20 times more customers on the same server, allowing us to provide significant cost savings for our clients,” Eliashiv explained. The company reports that clients have saved over 50% in costs, with some operations being up to 100 times more efficient than traditional tools.
This efficiency improvement is not just theoretical. He noted that Extract is already serving enterprise clients, including Warner Bros and EA, handling data at scales that traditional Python or Java platforms struggle to reach.
Memory Safety: The Hidden Advantage
Eliashiv’s team has extensive experience in cybersecurity and vulnerability research, giving them a unique understanding of the importance of memory safety in production systems.
“Every time someone misuses an array or structure in C or C++, they inadvertently free the same memory area twice. This is the source of vulnerabilities,” he said.
“In fact, with Rust, we don’t have to worry about this issue, which is incredible because we can write code with the same level of efficiency as C/C++ without having to worry about all these problems.”
Eliashiv stated that for data pipelines handling sensitive enterprise information, this is not just about preventing system crashes, but also about preventing memory errors that could lead to data leaks or corruption.
The Reality of Rust Development
However, the adoption of Rust is not without challenges. Unlike Python’s rich ecosystem of pre-built data connectors, Rust needs to build infrastructure from scratch.
“The initial construction of the infrastructure took some time because… there are not many people writing connectors in Rust,” Eliashiv admitted. The team invested a lot of time building the infrastructure to handle the various REST APIs and data formats they needed to support.
However, once the infrastructure is in place, development speed increases. The strict checks of the Rust compiler can catch errors at compile time rather than runtime, shortening debugging cycles and increasing confidence in code quality,” he said.
Scaling the Rust Team
Nevertheless, the talent issue remains a major barrier in the adoption process of Rust. Eliashiv’s approach is orderly — starting with the best engineers in the company to establish patterns and infrastructure, then gradually scaling up.
“We chose the best engineers in the company and formed a special team for this product, who were the first to learn Rust,” he explained. Many have backgrounds in C and C++, which made the transition easier.
As an added benefit, the strict compiler of Rust becomes a training advantage.“Unlike Python, [new developers] are very unlikely to break the code because there is a compiler that ensures you don’t make mistakes,” Eliashiv said.
“We are more confident in onboarding newcomers and giving them clear tasks.”
He added that AI coding tools like Cursor help accelerate this process, assisting developers in understanding Rust concepts and even enabling cross-functional contributions.
Beyond Data Pipelines: Rust’s Expanding Scope
The impact of the Rust programming language extends far beyond ELT platforms.
“I believe Rust has a real-time variant where you don’t have a complete standard library, but you can write ultra-efficient code,” Eliashiv said.
“I think it’s great for all these different types of use cases.”
He pointed out that,
Compiled by: Zhang Chang
Reference:
Rust Eats Python’s, Java’s Lunch in Data Engineering
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