Rust for Data Engineering

سرفصل های دوره

In this course, learn how to use Rust to build high-performance data pipelines that you can use in data engineering, ML Ops, and traditional software engineering. Rust provides safety, speed, and low-level control for systems programming, and instructor Noah Gift illustrates these aspects in the four sections of this course. Noah starts off looking at the key features of Rust, including HashMaps and vectors. He then takes a look at safety, security, and concurrency with Rust. In the third section, Noah covers popular Rust data engineering libraries and tools, and finishes the course with a look at designing data processing systems in Rust.

01 - 1. Getting Started with the Modern Rust Development Ecosystem

01 - Meet the instructor and course overview

02 - Introduction to the AI coding paradigm shift

03 - Introduction to cloud-based development environments

04 - Introduction to GitHub Copilot ecosystem for Rust

05 - Prompt engineering with GCP BigQuery SQL

06 - Introduction to AWS CodeWhisperer for Rust

07 - Using Google Bard to enhance productivity

08 - Continuous integration with Rust and GitHub actions

02 - 2. Rust Sequences and Maps

01 - Introducing Rust sequences and maps

02 - Demo Print Rust data structures

03 - Demo Vector fruit salad

04 - Demo VecDeque fruit salad

05 - Demo LinkedIn list fruit salad

06 - Demo Fruit salad CLI

07 - Demo HashMap frequency counter

08 - HashMap language comparison

03 - 3. Rust Sets, Graphs, and Miscellaneous Data Structures

01 - Analyzing UFC fighter network using graph centrality in Rust

02 - Storing unique fruits using HashSet in Rust

03 - Maintaining sorted and unique fruits using BTreeSet in Rust

04 - Creating a fig-priority fruit salad using BinaryHeap in Rust

05 - PageRank algorithm for sports data

06 - Showing shortest path with Dijkstra

07 - Detecting strongly connected components A deep dive into Kosarajus algorithm

08 - Simple charting of data structures in Rust

04 - 4. Rust Safety and Security Features

01 - Multifactor authentication

02 - Network segmentation

03 - Least privilege access

04 - Encryption

05 - Mutable fruit salad

06 - Customize fruit salad with a CLI

07 - Data race example

05 - 5. Security Programming with Rust

01 - High availability

02 - Understanding the Homophonic cipher A cryptographic technique

03 - Decoding the secrets of the Caesar cipher

04 - Building a Caesar cipher command-line interface

05 - Creating a decoder ring A practical guide

06 - Detecting duplicates with SHA-3 A data integrity tool

07 - Incident response

08 - Compliance

06 - 6. Concurrency with Rust

01 - Core concepts in concurrency

02 - Dining philosophers

03 - Web crawl Wikipedia with Rayon

04 - Intelligent chatbot with Tokio

05 - Multi-threaded deduplication with Rust

06 - Energy efficiency Python vs. Rust

07 - Concurrency stress test with a GPU

08 - Host efficiency serverless optimization problem

07 - 7. Using Rust to Manage Data, Files, and Network Storage

01 - Process CSV files in Rust

02 - Using Cargo Lambda with Rust

03 - List files on AWS EFS with Rust

04 - Use AWS S3 storage

05 - Use AWS S3 storage from Rust

06 - Write encrypted data to tables or Parquet files

08 - 8. DataFrames with Rust, Python, and Notebooks

01 - What is Colab

02 - Using Bard to enhance notebook development

03 - Exploring life expectancy in a notebook

04 - Load a DataFrame with sensitive data

05 - Using MLFlow with Databricks Notebooks

06 - End to End ML with MLFlow and Databricks

07 - Exploring global life expectancy with Polars

09 - 9. Using Rust with Cloud SDKs and CLIs for Data Engineering

01 - Cloud developer workspace advantage

02 - Onboarding to GCP with Python and Rust

03 - Using GCP Cloud Shell with Rust

04 - Learn AWS CloudShell

05 - Prototyping AI APIs with AWS CloudShell

06 - Cloud9 with CodeWhisperer

07 - Demo GCP App Engine Rust Deploy

08 - Containerized Rust Actix Microservice on AWS

10 - 10. Getting Started with Rust Data Pipelines (Including ETL)

01 - Jack and the Beanstalk data pipelines

02 - Open source data engineering Pros and cons

03 - Core components of data engineering pipelines

04 - Rust AWS step functions pipeline

05 - Rust AWS Lambda Async S3 size calculator

06 - What is Distroless

07 - Demo Deploying Rust microservices on GCP

11 - 11. Using Rust and Python for LLMs, ONNX, Hugging Face, and PyTorch Pipelines

01 - Introduction to Hugging Face Hub

02 - Rust PyTorch pre-trained model ecosystem

03 - Rust GPU Hugging Face translator

04 - Rust PyTorch high-performance options

05 - EFS ONNX Rust inference with AWS Lambda

06 - Theory behind model fine-tuning

07 - Doing fine-tuning

12 - 12. Building SQL Solutions with Rust, Generative AI, and Cloud

01 - Selecting the correct database on GCP

02 - Rust SQLite Hugging Face zero-shot classification

03 - Prompt engineering for BigQuery

04 - BigQuery to Colab pipeline

05 - Exploring data with BigQuery

06 - Using public data sets for data science

07 - Querying log files with BigQuery

08 - There is no one-size database

09 - Course conclusion

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تولید کننده: LinkedIn Learning (Lynda)