#cplusplus #arrow

https://github.com/apache/arrow

#rust #arrow #dataframe #datafusion #distributed #java #jvm #kotlin #kubernetes #scala #spark

https://github.com/ballista-compute/ballista

#cplusplus #arrow

Apache Arrow is a tool that helps big data systems process and move data quickly. It uses an efficient in-memory format to represent different types of data, allowing for fast communication between processes and different environments. Arrow has libraries in many programming languages like C++, Python, Java, and more, making it versatile. It also includes features like zero-copy memory sharing and support for various file formats. Using Apache Arrow can speed up your analytics tasks and make handling large datasets easier.

https://github.com/apache/arrow

#javascript #arrow_functions #es2015 #es2016 #es2017 #es2018 #es6 #eslint #javascript #linting #naming_conventions #style_guide #style_linter #styleguide #tc39

This guide provides rules for writing clean and consistent JavaScript code. It advises using const and let instead of var for variable declarations, preferring arrow functions over traditional function expressions, and using template strings for string manipulation. It also recommends using object destructuring, array spreads, and default parameters in functions. The guide emphasizes the importance of proper spacing, indentation, and the use of semicolons. Additionally, it covers best practices for classes, modules, and control statements, and encourages thorough testing and performance optimization. Following these guidelines helps ensure that your code is readable, maintainable, and efficient.

https://github.com/airbnb/javascript

#rust #arrow #dataframe #dataframe_library #dataframes #out_of_core #polars #python #rust

Polars is a powerful tool for working with data that is very fast and efficient. It supports multiple programming languages like Rust, Python, Node.js, and R. Here are the key benefits Polars is extremely fast, making it one of the best performing solutions available.
- **Multi-threaded and SIMD** It optimizes queries to make them run efficiently.
- **Handling Large Data** You can install Polars easily using pip for Python or through other package managers for other languages.
- **Comprehensive Documentation**: There are detailed user guides, documentation, and community support available.

Overall, Polars helps you work with large datasets quickly and efficiently, making it a valuable tool for data analysis.

https://github.com/pola-rs/polars