Strategies for scaling and optimizing Apache Iceberg data compaction jobs, including parallelism, checkpointing, and failure recovery.
A guide to speeding up Python code using four practices: efficiency, compilation, parallelism, and process, achieving a 330x speedup.
Explores core principles of scalable data engineering, including parallelism, minimizing data movement, and designing adaptable pipelines for growing data volumes.
A guide to using the Ray library for easy parallel processing and distributed computing in Python applications.
Optimizing Mandelbrot set calculations using SIMD instructions in Rust for faster single-core performance and reduced computational costs.
Explains parallel task execution in Swift using GCD, Operation Queues, and the new structured concurrency API with practical code examples.
Explores techniques to significantly improve MongoDB aggregation performance using parallel processing and sharding on distributed clusters.
A tutorial on using Swift's structured concurrency and async/await to run tasks in parallel safely and efficiently.
Explains concurrency and parallelism in Go using goroutines and channels, with practical code examples.
A preview of PostgreSQL 11's key new features, including usability improvements, safer column additions, and performance enhancements like JIT compilation.
An exploration of concurrency fundamentals, starting from basic concepts like threads and locks to build a foundation for writing faster, more understandable programs.
Explains a common mistake when using Scala Futures in for-comprehensions and provides a solution to ensure parallel execution.
An analysis of Butler Lampson's 1999 predictions on computer science, comparing what worked then to the state of technology in 2015.
