Rust Libraries Every Data Engineer Should Know

Essential Rust Libraries for Analytics & Data Pipelines

In the last few years, Rust has quietly gone from “just a systems language” to a key ingredient in today’s data platforms. Polars, Arrow, Delta Lake, and even parts of Snowflake now depend on Rust under the hood.

Data engineering today demands three things:

• predictable performance

• memory-safe concurrency

• zero-copy movement of large datasets

Rust gives you all three without the overhead of GC pauses or Python bottlenecks.

Here are most popular Rust-based libraries powering today’s data engineering & analytics workloads.

1. Polars - The DataFrame Engine

Polars offers a modern, high-performance DataFrame experience, powered by Rust and used across Python, Node.js, and Rust.

• Fast and efficient query execution

• Supports both lazy and streaming workflows

• Handles large datasets with predictable memory use

• Built on Arrow’s columnar format for interoperability

This makes Polars a good fit for ETL tasks, feature engineering, local analytics, and scenarios where tools like Pandas or Spark start to feel slow or memory-intensive.

2. Apache Arrow - The Columnar Memory

Apache Arrow provides a universal columnar data format used by DuckDB, Polars, Snowflake, BigQuery, and more. Arrow is referenced by Polars.

Before Arrow, when you moved data from one system (like Apache Spark) to another (like a Python pandas DataFrame), each system would have to convert the data from its internal format into a generic format and then back again. This serialization and deserialization process is slow and inefficient.

Apache Arrow provides a common, standardized format that all major data processing tools can understand. This allows for “zero-copy” data sharing, where different applications can access the same data in memory without any need for conversion or copying, making data exchange incredibly fast.

Key Advantages of the Arrow Way

• Speed: Analytical queries are significantly faster because systems only need to read the relevant columns, saving a lot of time and effort.

• Efficient Compression: Since all the data in a column is of the same type (e.g., numbers in the “Age” column), it’s much easier to compress.This reduces memory usage and storage costs.

• Modern CPU Friendly: The columnar layout allows modern processors to perform calculations on entire chunks of data at once (a technique called vectorized processing), leading to massive speed boosts.

3. DataFusion - SQL Query Engine

DataFusion is a powerful and flexible query engine developed in Rust, designed for building high-performance, data-centric systems. It leverages the Apache Arrow in-memory columnar format, which enables efficient data processing and fast query execution. Initially a part of the Apache Arrow project, DataFusion has evolved into a versatile framework for a wide range of data engineering tasks.

At its core, DataFusion provides both a SQL and a DataFrame API, offering developers familiar interfaces for data manipulation and analysis. It boasts a comprehensive query planner, a multi-threaded, vectorized execution engine, and support for partitioned data sources.

Use cases:

• Embedded Query Engine

• ETL Pipelines

• Database Development: DataFusion serves as a foundation for creating new, high-performance database and analytics systems.A notable example is its use in InfluxDB’s storage engine.

• Analytics Tools: It can power analytics tools that require a user-friendly SQL or DataFrame interface.

4. DuckDB - OLAP for Modern Analytics

DuckDB is the “SQLite for analytics,” offering:

• columnar SQL engine

• fast Parquet/CSV/Arrow reading

• OLAP-style queries locally

Why DuckDB is Great for Modern Analytics

1. Zero Setup: You don’t need to be a database expert. You can start using it in your favorite data science tool (like Python’s Pandas or R) with a single command. It just works.

2. Runs Anywhere: Because it’s a simple file, you can use it on your laptop, in a cloud environment, or even directly in your web browser.

3. Reads Anything: You don’t have to go through the slow process of importing data. DuckDB can directly query files like CSVs, Parquet, and JSON. You can just point it at your data file and start asking questions instantly.

4. Incredibly Fast: For the types of analytical questions data scientists and analysts ask every day, DuckDB is often faster than much bigger, more complicated database systems.

5. Delta-RS - ACID Table Format

Delta Lake is an open-source storage format that runs on top of existing data lakes. Delta Lake is compatible with processing engines like Apache Spark and provides benefits such as ACID transaction guarantees, schema enforcement, and scalable data handling. You get:

ACID transactions, Schema evolution, Time travel and DML operations on object storage

Why is Delta-RS a Game Changer?

1. No Spark or Java Needed: The biggest advantage is that you can read and write to Delta Lake from languages like Python and Rust without any dependency on Spark or Java. This makes it much easier for data scientists, analysts, and developers who are more comfortable with Python to use the powerful features of Delta Lake.

2. High Performance: Because it’s built in Rust, Delta-RS is incredibly fast and memory-efficient.

3. Perfect for Lighter Tasks: It’s ideal for situations where a full-blown Spark cluster is overkill. This includes things like Data Exploration, Low-Latency ETL, Application Integration.

4. A Bridge to Other Tools: Delta-RS acts as a bridge, connecting Delta Lake to a growing ecosystem of modern data tools like DuckDB, Polars, and DataFusion.

6. Iceberg-RS - Table Format

Apache Iceberg is a high-performance, open-source table format designed to manage massive analytical datasets in data lakes. It acts as a smart layer on top of raw data files (like Parquet, ORC, and Avro) that brings the reliability and simplicity of traditional SQL tables to big data environments.

While it shares core features with Delta Lake, they are not the same. Both provide:

• ACID transactions to ensure data consistency.

• Schema evolution to safely change a table’s structure.

• Time travel to query historical versions of data.

• DML operations (updates, deletes, merges) on object storage.

The key difference lies in their origins and governance. Apache Iceberg was created at Netflix and is now governed by the neutral Apache Software Foundation, while Delta Lake was started by Databricks and is closely integrated with the Spark ecosystem.

Iceberg-rust is a native Rust implementation for managing Apache Iceberg tables.

Why is iceberg-rust Important?

1. Performance and Efficiency

2. No More Java Dependency: It provides a direct bridge to the Iceberg ecosystem for developers and tools in the Rust community. This is crucial for projects that are building modern data infrastructure in Rust and want to avoid the overhead and complexity of the JVM.

3. Powering the Modern Data Stack: iceberg-rust is a key building block for a new generation of data tools. It allows high-performance query engines and databases written in Rust, such as DataFusion, Databend and to seamlessly connect with and leverage the power of Iceberg.

4. Enabling New Use Cases: Because it’s a flexible library, iceberg-rust can be used in a variety of scenarios where a full Spark cluster would be impractical. This includes lightweight data processing scripts, fast data validation services, and low-latency query services that need to serve data from Iceberg tables in milliseconds.

7. SQLparser-RS - SQL Compiler

SQLparser-rs is a comprehensive, widely-used Rust library designed to parse SQL text into a structured format that a computer program can understand.

It takes a raw SQL query string (like SELECT id, name FROM users) and converts it into a logical tree-like structure known as an Abstract Syntax Tree (AST). This AST breaks down the query into its fundamental components like commands, table names, columns, and expressions which making it easy for other programs to analyze, manipulate, or execute the SQL.

Uses : Query rewriting, Static analysis, Query builders and code generation.

8. Tokio + Reqwest - The Async Data Pipelines

Tokio is an asynchronous runtime for the Rust programming language. It provides the core infrastructure to execute code concurrently. Its primary function is to manage a small number of operating system threads and use them to run a large number of independent tasks. When a task must wait for an I/O operation to complete, such as reading a network socket, Tokio suspends it and uses the thread to run other tasks. This non-blocking model allows an application to handle thousands of concurrent operations efficiently.

Reqwest is a high-level, asynchronous HTTP client library for Rust. It provides a simple and convenient API for making web requests to services and APIs. Reqwest is built on top of Tokio, leveraging its non-blocking capabilities to perform all network communication without halting program execution.

Building Data Pipelines

Together, Tokio and Reqwest are used to build high-performance data pipelines that involve fetching data from multiple network sources.

In a typical pipeline, a program needs to gather data from numerous endpoints. Using Reqwest, all of these HTTP requests can be initiated simultaneously. Tokio’s runtime then manages these concurrent requests. As each request waits for a response from the server, Tokio ensures the CPU is not idle and can work on other tasks. When a response arrives, Tokio schedules the corresponding task to process the data.