Data Analytics with Rust

Data Analytics with Rust

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Build fast, reliable data pipelines and analytics tools using Rust.

#business#developer data analysisRust

Data Analytics with Rust

Concept & Motivation

Data analytics has been dominated by Python for over a decade — and for good reason. Pandas, NumPy, and Jupyter make exploration fast and accessible. But when your analysis becomes a production pipeline, Python’s limitations surface: slow execution on large datasets, memory issues, dependency hell, and deployment complexity.

Rust offers an alternative for the production side of data analytics: blazing speed, predictable memory usage, and single-binary deployment. Libraries like Polars (which powers much of the Python data ecosystem’s performance improvements) make Rust-native analytics practical and ergonomic.

This course teaches you to build data analytics tools and pipelines in Rust — not to replace Python entirely, but to handle the work where performance, reliability, and deployment simplicity matter.

What You’ll Learn

  • Polars for DataFrames — loading, filtering, grouping, joining, and aggregating data using Rust’s fastest DataFrame library. Lazy evaluation for memory-efficient processing of large datasets
  • Data serialization with serde — reading and writing CSV, JSON, TOML, and Parquet. Strongly typed data parsing that catches format errors at load time, not mid-pipeline
  • Database analytics with SQLx — running analytical queries against SQLite and PostgreSQL from Rust, with compile-time SQL verification
  • Building ETL pipelines — extract, transform, load workflows as Rust programs. Error handling, logging, and recovery strategies for production reliability
  • Performance profiling — understanding where time goes. When parallelism helps (rayon), when it doesn’t, and how to benchmark honestly
  • CLI analytics tools — building command-line data tools with clap that your team can use without installing Rust. Single binary, zero dependencies
  • Integration patterns — reading from APIs, message queues, and file systems. Writing results to databases, dashboards, or downstream systems

Who This Is For

  • Data engineers building production pipelines who want better performance and reliability than Python scripts
  • Backend developers adding analytics capabilities to Rust services
  • DevOps and platform engineers building monitoring, log analysis, or metrics processing tools
  • Anyone maintaining slow Python data scripts who wants a path to production-grade performance

Prerequisite: basic Rust proficiency (ownership, structs, error handling). Our Introduction to Rust course provides the right foundation.

Format & Duration

2-day intensive workshop (on-site or hybrid). Day 1: Polars, serde, and data loading patterns — building a complete data transformation pipeline. Day 2: database analytics, CLI tools, performance tuning, and integration — participants build a pipeline for their own use case.

What Makes This Course Different

Most data analytics training assumes Python. Most Rust training ignores analytics. This course bridges the gap for teams that need production-grade data processing without the overhead of JVM-based big data stacks or the fragility of Python script chains.

You work with the same libraries and patterns used in our own data pipelines — Polars for DataFrames, serde for serialization, SQLx for databases. The focus is practical: by end of day 2, you have a working pipeline you can take home and extend.

Q & A

Learn more about what we do

Basic Rust knowledge is recommended — you should be comfortable with ownership, structs, and error handling. Our Introduction to Rust course (c-it-introduction-rust-1) provides the right foundation. If you've been writing Rust for a few months on your own, that works too.
Python is excellent for exploration and prototyping. Rust is for when your pipeline needs to run fast, handle large datasets without running out of memory, or deploy as a single binary without dependency management. Many teams use both: Python for ad-hoc analysis, Rust for production pipelines. This course teaches the Rust side.
We use Polars (a Rust-native DataFrame library that outperforms Pandas on most benchmarks), serde for data serialization, and SQLx for database analytics. You'll work with realistic datasets — CSV, JSON, Parquet — and build pipelines that clean, transform, aggregate, and output results.
Yes. The course focuses on building data pipeline components that integrate with existing systems — reading from databases, APIs, or file systems, processing data efficiently, and writing results back. By the end, you'll have a template for replacing slow Python scripts or fragile ETL steps with reliable Rust tools.
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Data Analytics with Rust Data Analytics with Rust Concept & Motivation What You’ll Learn Who This Is For Format & Duration What Makes This Course Different Q & A Learn more about what we do