Rust on GCP

Overview

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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.

Syllabus

Why Rust on Google Cloud

The three properties Rust gives you on Google Cloud — predictable latency, tiny containers, and honest errors — and how the GCP analytics services compose into ingest-transform-serve pipelines.

Cloud Shell, Editor, and the gcloud SDK

The browser-hosted developer environment that ships pre-wired with gcloud, plus the local CLI install for engineers who want the same tools on their own machine.

Cloud Storage Strategy for Rust Pipelines

Buckets, storage classes, and lifecycle rules — and the Rust client patterns (streaming uploads, resumable sessions, ADC) that consume them efficiently from a tokio runtime.

BigQuery for Rust Engineers

The two BigQuery APIs Rust services use — REST jobs.query for small JSON results and the Storage Read API for million-row Arrow scans — and the prompt-engineering pattern for ML.GENERATE_TEXT.

Pipelines and Compute

Volume, velocity, and cost trade-offs for big data on GCP, the three-zone (raw / transform / serving) pipeline pattern, and how GCE, GKE, Cloud Run, App Engine, and Cloud Functions each fit the trigger model.

Rust on Cloud Run and App Engine

Distroless containers, Cloud Run deploys from source or image, App Engine Flex with custom runtimes, and the Pub/Sub push pattern for idempotent ETL handlers with concurrency-bounded backpressure.

Production Discipline — Audits, CI, Secure-by-Design

cargo-audit and cargo-deny against the RustSec advisory database, the secure-by-design properties Rust enforces at compile time, GitHub Actions matrix builds, and the energy-efficiency case for Rust over Python at scale.