Build production data pipelines on Google Cloud using Rust — predictable latency, single-digit-megabyte containers, and errors that fail at compile time instead of 3 a.m. This course shows engineers how to read from Cloud Storage, query BigQuery (REST jobs.query for small results, Storage Read API for million-row Arrow scans), and deploy distroless handlers to Cloud Run with sub-100 millisecond cold starts. You'll learn the gcloud CLI and Cloud Shell workflow, choose the right GCS client crate stack (google-cloud-storage, tonic, tokio), and configure Pub/Sub push subscriptions with idempotent content-hash handlers and backpressure controls. Production discipline comes through cargo-audit, cargo-deny, secure-by-design defaults, and CI gates on GitHub Actions. By the end, you'll have a working pattern for shipping a Rust ETL handler that survives at-least-once delivery, distroless image scans, and concurrent load — all on the GCP services you already pay for.

Rust on GCP
This course is part of Rust for Data Engineering Specialization

Instructor: Noah Gift
Access provided by INEFOP - Instituto Nacional de Empleo y Formación Profesional de Uruguay
Recommended experience
What you'll learn
Build a Rust handler that reads from Cloud Storage, queries BigQuery via REST or Storage Read API, and deploys to Cloud Run
Apply Pub/Sub push subscriptions with content-hash idempotency keys and concurrency × max-instances backpressure caps
Gate Rust GCP services on cargo-audit, cargo-deny, and GitHub Actions CI with secure-by-design defaults from the first commit
Skills you'll gain
- Cloud API
- Cloud Engineering
- Multi-Cloud
- MLOps (Machine Learning Operations)
- Cloud Security
- DevOps
- Cloud Computing Architecture
- Google App Engine
- Cloud Infrastructure
- Data Engineering
- Containerization
- Serverless Computing
- Cloud Platforms
- Cloud-Based Integration
- Cloud Management
- Data Pipelines
- Cloud Services
- Google Cloud Platform
Tools you'll learn
Details to know

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May 2026
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