Projects for Rust Data Engineering Coursera course. Website for projects here: https://nogibjj.github.io/rust-data-engineering/
- Works with both AWS CodeCatalyst and GitHub Codespaces
- Any suggestions or feedback? Feel free file a ticket.
- Print Rust data structures:
cd print-data-structs && cargo run - Vector Fruit Salad:
cd vector-fruit-salad && cargo run - VecDeque Fruit Salad:
cd vecdeque-fruit-salad && cargo run - Linkedin List Fruit Salad:
cd linked-list-fruit-salad && cargo run - Fruit Salad CLI:
cd cli-salad && cargo run -- --number 3
- HashMap frequency counter:
cd hashmap-count && cargo run - HashMap language comparison:
cd hashmap-language && cargo run - BTreeMap language comparison:
cd BTreeMap-language && cargo run
- HashSet fruits:
cd hashset-fruit && cargo run - BTreeSet fruits:
cd btreeset-fruit && cargo run
- Binary Heap Fruit Salad with Fig Priority:
cd binaryheap-fruit && cargo run
- mutable fruit salad:
cd mutable-fruit-salad && cargo run - cli customize fruit salad:
cd cli-customize-fruit-salad && cargo run -- fruits.csvorcargo run -- --fruits "apple, pear" - data race example:
cd data-race && cargo run(will not compile because of data race)
The main differences between ciphers and encryption algorithms:
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Ciphers operate directly on the plaintext, substituting or transposing the letters mathematically. Encryption algorithms operate on the binary data representations of the plaintext.
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Ciphers typically have a small key space based on simple operations like letter mappings or transposition rules. Encryption algorithms use complex math and very large key sizes.
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Ciphers provide security through obscuring letter frequencies but are still vulnerable to cryptanalysis. Encryption algorithms rely on computational hardness assumptions.
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Ciphers only handle textual data well. Encryption algorithms can handle all binary data like images, video, etc.
In summary:
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Ciphers like homophonic substitution operate directly on textual plaintext with simple math operations and fixed small key spaces.
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Encryption algorithms like AES operate on any binary data with complex math and very large key sizes.
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Ciphers are considered obsolete for serious encryption use today due to vulnerabilities.
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Modern encryption provides provable security based on mathematical problems assumed to be computationally infeasible to solve.
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Data Race Detector: Create a multi-threaded application that attempts to produce a data race, then show how Rust's ownership rules prevent this from occurring.
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Memory Leak Preventer: Build an application that would typically suffer from memory leaks in other languages, such as a complex tree structure. Rust's automatic memory management through RAII (Resource Acquisition Is Initialization) should ensure no memory leaks occur.
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Null Pointer Safety: Create a project demonstrating how Rust's Option and Result<T, E> types are used to handle potentially null or error-producing cases safely.
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Immutable by Default: Design a system where mutability would cause bugs (for instance, a simulation with entities that should not be able to change once created) and show how Rust's immutability by default prevents these issues.
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System with Lifetimes: Show how lifetimes can prevent use-after-free bugs by building an application where objects have distinct lifetimes that must be enforced.
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No Segfault System: Create a project that would usually segfault in other languages, and demonstrate how Rust prevents this.
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Web Server: Build a small multi-threaded web server. Show how Rust's safety features prevent common bugs in concurrent programming.
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Safe FFI: Create a project that uses Rust's Foreign Function Interface (FFI) to interoperate with C libraries safely.
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Safe Transmute: Write a program that demonstrates the use of safe transmutes in Rust. This could be a good way to show how Rust can avoid undefined behavior that's common in languages like C or C++.
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Bounds Checking: Design a system that would typically have a lot of array bounds errors, then show how Rust's automatic bounds checking prevents these kinds of errors.
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Immutable Concurrency: Create a project that takes advantage of Rust's ability to share immutable data among threads without data races.
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Command Line Application: Build a command-line application that processes user input. Use Rust's strong type system and pattern matching to handle different types of input safely and cleanly
- CSV Data Processing: A tool for processing large CSV files, showcasing efficient data reading, filtering, and aggregation capabilities of Rust.
- Database Interaction: An application that interacts with a SQL database (like PostgreSQL) using Diesel, demonstrating CRUD operations, migrations, and complex queries.
- Data Visualization: A CLI tool that generates graphs and charts from input data using plotters.
- Web Scraper: A multi-threaded web scraper that fetches and parses data from several web pages concurrently.
- REST API Consumer: An application that interacts with a REST API to fetch, process, and visualize data.
- Log Parser: A tool to parse and analyze server log files. It can extract meaningful information and statistics and provide insights about the server performance.
- File System Analyzer: An application that provides insights about disk usage, like the
ducommand in Unix. - Real-Time Twitter Analysis: A real-time tweet analysis tool that uses Twitter Stream API to fetch tweets and analyze them (for example, performing sentiment analysis).
- Stock Market Analyzer: An application that fetches stock market data from a free API and performs various analyses.
- Text Analytics: A text analytics library that provides functionalities like sentiment analysis, named entity recognition, etc.
- Delta Lake Interaction: A project demonstrating interaction with Delta Lake for processing large amounts of data.
- AWS SDK usage: A project demonstrating the use of AWS SDK in Rust for tasks such as accessing S3 buckets, performing operations on DynamoDB, etc.
- Data Processing with Polars: A project demonstrating how to perform large-scale data processing with the Polars library in Rust.
- Kafka Producer/Consumer: An application that produces and consumes messages from Kafka.
- gRPC Microservices: A basic microservices setup using gRPC, demonstrating how Rust can be used for backend development.
- Apache Arrow usage: A project demonstrating how to use Apache Arrow for columnar data processing in Rust.
- Parquet File Processing: An application that reads and writes Parquet files, demonstrating how Rust can be used for efficient data engineering tasks.
- Data Engineering with TiKV: A project demonstrating how to use TiKV, a distributed transactional key-value database built in Rust.
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Rust-based ETL Pipeline: Develop an ETL (Extract, Transform, Load) pipeline using various Rust libraries to process and transfer data between different storage systems.
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Web Scraper with Rust: Build a concurrent web scraper that can efficiently scrape large amounts of data from web pages.
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Rust REST API Server: Design a REST API server in Rust that serves data from a database. Use the Diesel ORM for database interactions.
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Real-time Data Streaming with Rust: Implement a real-time data streaming application, processing streams of data in a concurrent manner.
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Rust-based Data Lake: Use the Delta Lake Rust API to create a data lake solution. Implement CRUD operations on the data lake.
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Big Data Processing with Rust and Apache Arrow: Use Apache Arrow to perform efficient in-memory big data processing.
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Rust and AWS SDK: Use the AWS SDK for Rust to interact with AWS services such as S3 and DynamoDB.
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gRPC Service in Rust: Implement a gRPC service in Rust that performs CRUD operations on a database.
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Log Analyzer: Create a log analyzer that can process large log files concurrently and provide useful insights from logs.
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Distributed Systems with Rust: Create a simple distributed system using Rust's concurrency features. This could be a simple key-value store or a message-passing system.
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Rust and GraphQL: Implement a GraphQL API in Rust using libraries like Juniper.
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Data Serialization with Rust: Use libraries like serde to perform data serialization and deserialization in various formats (JSON, XML, etc.)
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Rust and Kafka: Use Rust to interact with Kafka, implementing a producer and consumer system.
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Data Validation Service: Create a service that validates input data based on predefined rules. This could be a web service or a library that other services can use.
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Rust and Machine Learning: Use Rust machine learning libraries to implement a simple prediction model. You could use the data processed in the ETL pipeline or the data lake for this.
In this lab you will gain experience extending an existing Rust project by forking and modifying a simple command-line tool.
Steps
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Fork the repository at https://github.com/nogibjj/rust-data-engineering
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Clone your forked repository
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Navigate to one of the command-line tool projects
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Make a small modification to the tool such as:
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Adding a new command line argument
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Supporting additional input file formats
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Adding more processing logic
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Changing output formatting
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Run
cargo buildto compile your changes -
Run
cargo runto test your modified tool -
Commit your changes and push to your forked repository
Deliverable
Submit a link to your forked repository showing the code changes.
Goals
This hands-on lab provides experience with:
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Forking and cloning a Rust project
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Modifying existing Rust code
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Running
cargo buildandcargo run -
Version control with git
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Making a pull request (optional)
Each subdirectory project uses this style to make it easy to test and run
format:
cargo fmt --quiet
lint:
cargo clippy --quiet
test:
cargo test --quiet
run:
cargo run
all: format lint test run