Explores how .NET library authors can use source generators and generic programming to avoid complex runtime reflection, especially for Native AOT support.
AWS Lambda's new code editor features an improved Amazon Q Developer AI assistant for generating and debugging Lambda functions with better in-line code previews.
Announcement of Ghostty 1.0, a new open-source terminal emulator for macOS and Linux aiming to be a fast, feature-rich, and standards-compliant drop-in replacement.
A tutorial on using Htmx triggers with ASP.NET Core to dynamically update HTML elements, specifically focusing on refreshing a user avatar.
A guide to making digital accessibility easier for developers, focusing on SwiftUI, VoiceOver, Voice Control, and building an accessibility culture step-by-step.
A structured guide for reviewing a company's cloud infrastructure, workloads, and code to prioritize security improvements and establish a cloud security program.
Analysis of skepticism towards rewriting JavaScript tools in faster languages, focusing on maintainability and accessibility.
A guide to manually managing multiple Node.js versions using shell aliases and downloaded binaries, without relying on NVM or other version managers.
A tutorial on fetching data in React using only built-in Hooks like useState and useEffect, without third-party libraries.
Benchmarking performance of ASP.NET Minimal API vs classic Controllers in .NET 8 and 9 using BenchmarkDotNet.
An introduction to Apache Parquet, a columnar storage file format for efficient data processing and analytics.
Explains Parquet's columnar storage model, detailing its efficiency for big data analytics through faster queries, better compression, and optimized aggregation.
Explains the hierarchical structure of Parquet files, detailing how pages, row groups, and columns optimize storage and query performance.
Explains how Parquet handles schema evolution, including adding/removing columns and changing data types, for data engineers.
Explains encoding techniques in Parquet files, including dictionary, RLE, bit-packing, and delta encoding, to optimize storage and performance.
Explores compression algorithms in Parquet files, comparing Snappy, Gzip, Brotli, Zstandard, and LZO for storage and performance.
Explores how metadata in Parquet files improves data efficiency and query performance, covering file, row group, and column-level metadata.
A practical guide to reading and writing Parquet files in Python using PyArrow and FastParquet libraries.
Explores why Parquet is the ideal columnar file format for optimizing storage and query performance in modern data lake and lakehouse architectures.
Final guide in a series covering performance tuning and best practices for optimizing Apache Parquet files in big data workflows.
