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Best practices for reproducible, modular notebooks
This article explores best practices for making notebooks reproducible and modular, focusing on environment management, automation, testing, and CI/CD integration. It presents a detailed guide with code examples, architecture diagrams, and modern tools that empower engineering teams to treat notebooks as reliable, maintainable, and production-ready artifacts.
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Empirical comparison of algorithms
This in-depth article explores empirical benchmarking of algorithms in 2025, highlighting advanced statistical rigor, reproducibility techniques, and modern tooling. It includes examples from sorting and machine learning domains, code samples, pseudographic visualizations, and insights into industry-standard frameworks like Ray, Spark, and MLPerf for real-world performance evaluation.
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Introduction to ETL and ELT patterns
ETL and ELT are core data integration patterns that define how organizations move, transform, and analyze information. This post introduces both approaches, their architectures, trade-offs, and modern tooling, helping data engineers understand when to apply each and how to align them with modern cloud-native practices.
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Using timeit and perf to benchmark Python code
This guide explores the practical use of Python’s built-in timeit module and the powerful perf library for accurate benchmarking. Learn how to perform reproducible, statistically robust performance testing using both tools, interpret their results, and integrate them into modern engineering workflows.