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Pragmatic AI Labs via Coursera Learning path or series

Rust for Data Engineering

Overview

Rust for Data Engineering is a hands-on specialization that teaches you how to build production-grade data systems in Rust — from terminal tools and ETL pipelines to serverless functions, SQL and NoSQL data stores, and full CI/CD shipping workflows on GCP and beyond. You will move from knowing some Rust to confidently shipping a data pipeline that handles real workloads, runs in a container, and survives production. Each course is short, project-driven, and codes-along: you write real Rust against real data, with snapshot tests, observability, and reproducible builds. By the end you will have a portfolio of small but production-shaped projects — bash-to-Rust rewrites, Polars data jobs, SQL-backed services, ETL workflows, serverless handlers, RAG pipelines, TUI tools, and GCP deployments — and the engineering muscles to extend any of them into a real system at work.

Courses in this learning path or series 31

  1. Rust From Zero
  2. SQLite for Rust
  3. ETL Pipelines with Rust
  4. Linux Desktop From Zero
  5. Polars from Zero
  6. Rust Serverless
  7. Data Ethics
  8. Agile With AI
  9. Zig From Zero
  10. Rust GUI From Zero
  11. Terminal From Zero
  12. Rust on GCP
  13. Shipping Rust
  14. Claude From Zero
  15. Rust CLI From Zero
  16. Graph Algorithms with Rust
  17. MySQL From Zero
  18. Postgres From Zero
  19. RAG From Zero
  20. DuckDB From Zero
  21. Valkey From Zero
  22. Rust for Data Source Monitoring and Automation
  23. Rust DataOps: CI/CD and Containers for Data Pipelines
  24. HelixDB From Zero
  25. Design by Provable Contracts
  26. IAC from Zero
  27. TUI from Zero
  28. WASM from Zero
  29. Bash to Rust: From Zero
  30. Big O notation: Python to Rust
  31. OO: Python to Rust

Advantages and disadvantages

Advantages

  • Structured courses with graded quizzes, assignments and deadlines you can reset.
  • A shareable certificate from the university or company when you pay.
  • Financial aid is often approved for students in India (apply 15 days before you need it).
  • Self-paced: start any time.
  • A clear syllabus (31 parts) you can see before you start.

Disadvantages

  • Paid after a 7-day free trial (Coursera Plus or per course).
  • Some courses can be audited for free, but graded work and certificates need payment.

Some points apply to every course of this kind; see how we rank.

This course is paid. Here is how to take it for free

  • Free trials: You can start a 7-day free trial for many individual courses, Specializations, or a Coursera Plus subscription to test full course features. Cancel before the seventh day if you do not want to be charged.
  • Financial aid: If you cannot afford the fee for a certificate, you can apply for financial aid through the link on the course home page by filling out an application about your background and goals.

Taught by

  • Noah Gift
  • Liam Parker

Pragmatic AI Labs is in Tier 4: commercial training companies and platform-made courses of our institution ranking (58/100).

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