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Introduction to modern data warehouse design
Modern data warehouse design combines scalability, flexibility, and cost efficiency. This post introduces the fundamentals of data warehousing architecture, from schema models to ELT workflows, cloud-native platforms, and governance frameworks. It’s a complete primer for engineers starting in data warehousing.
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Topics Everyone Is Talking About No255
Addressing the Adding Situation: How Compilers Optimize Arithmetic • Google, Nvidia, and OpenAI: The New Power Struggle in AI • MADstack: Rust Web Stack with AI Components
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Topics Everyone Is Talking About No252
John Giannandrea to Retire from Apple in 2026 • Hidden Art: The Secret Illustrations Inside Switzerlands Maps • Claude 4.5 Opus: The Hidden Soul Document Inside a Language Model
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Best practices: avoid mutable default arguments
Mutable default arguments in Python can lead to unpredictable bugs because they are evaluated once at function definition, not at each call. This post explains why this happens, demonstrates real-world implications, and provides modern best practices, tools, and patterns to avoid these issues safely.
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Empirical: Airflow vs Prefect performance comparison
This empirical benchmark compares Apache Airflow and Prefect in real-world orchestration scenarios. Through detailed performance testing, it reveals how each handles scalability, latency, and fault recovery under heavy workloads in 2025 environments.
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Intro to model evaluation metrics
Understanding model evaluation metrics is essential for every machine learning practitioner. This post introduces key concepts such as accuracy, precision, recall, F1-score, and more—explaining when and why to use each. It also highlights modern metrics for generative and fair AI systems, and shows practical examples using popular libraries like scikit-learn and PyTorch Lightning.
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Tools: black, ruff, pre-commit, mypy
Learn how Black, Ruff, Pre-commit, and Mypy work together to automate code quality in modern Python development. This guide covers setup, configuration, and integration strategies for building consistent, type-safe, and production-grade Python workflows used by leading tech companies.
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