Rust AWS SDK Setup Guide - Editor, Credentials, Cargo and Code Assist
Get your Rust environment ready for AWS SDK app development by setting up 4 key components:
Rust Editor - Install plugins like AWS Toolkit, Amazon Q and Code Whisperer for code assistance
AWS Credentials - Configure credentials locally for SDK access
Cargo Dependencies - Add AWS crates for services you want to use
Runtime Environment - Use local device, EC2 instance, Code Catalyst, etc
Walkthrough creating a simple S3 bucket listing app with SDK calls to demonstrate the environment. Cover cargo project setup, using AWS Q to understand code, and running the app with cargo run.
This video helps Rustaceans integrate the language with AWS services through the official SDK. Take advantage of Rust's speed, safety and efficiency.
Hey readers π, if you enjoyed this content, I wanted to share some of my favorite resources to continue your learning journey in technology!
Hands-On Courses for Rust, Data, Cloud, AI and LLMs π
Rust Programming Specialization: https://www.coursera.org/specializations/rust-programming
Rust for DevOps: https://www.coursera.org/learn/rust-for-devops?specialization=rust-programming
Rust LLMOps: https://www.coursera.org/learn/rust-llmops?specialization=rust-programming
Rust Fundamentals: https://www.coursera.org/learn/rust-fundamentals
Data Engineering with Rust: https://www.coursera.org/programs/duke-university-on-coursera-obsio/learn/data-engineering-rust
Python and Rust with Linux Command Line Tools: https://www.coursera.org/learn/python-rust-linux
Virtualization, Docker, and Kubernetes for Data Engineering: https://www.coursera.org/learn/virtualization-docker-kubernetes-data-engineering
Cloud Machine Learning Engineering and MLOps: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke
MLOps Tools: MLflow and Hugging Face: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke
Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python
Linux and Bash for Data Engineering: https://www.coursera.org/learn/linux-and-bash-for-data-engineering-duke
Spark, Hadoop, and Snowflake for Data Engineering: https://www.coursera.org/learn/spark-hadoop-snowflake-data-engineering
Cloud Virtualization, Containers and APIs: https://www.coursera.org/learn/cloud-virtualization-containers-api-duke
Cloud Data Engineering: https://www.coursera.org/learn/cloud-data-engineering-duke
Python Essentials for MLOps: https://www.coursera.org/learn/python-mlops-duke
DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke
Web Applications and Command-Line Tools for Data Engineering: https://www.coursera.org/learn/web-app-command-line-tools-for-data-engineering-duke
MLOps Platforms: Amazon SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Scripting with Python and SQL for Data Engineering: https://www.coursera.org/learn/scripting-with-python-sql-for-data-engineering-duke
Python and Pandas for Data Engineering: https://www.coursera.org/learn/python-and-pandas-for-data-engineering-duke
Cloud Computing Foundations: https://www.coursera.org/learn/cloud-computing-foundations-duke