Efficient Data Science Workflows with Vaex (eBook, ePUB)
Definitive Reference for Developers and Engineers
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"Efficient Data Science Workflows with Vaex" Efficient Data Science Workflows with Vaex delivers a comprehensive exploration of modern data science challenges and introduces Vaex as an innovative solution for handling and analyzing massive datasets at scale. The book presents a compelling case for the transition from traditional in-memory tools, such as pandas and NumPy, to more advanced, out-of-core solutions that effortlessly process data far exceeding physical memory constraints. Through detailed case studies and foundational principles, readers gain a deep understanding of both the limitat...
"Efficient Data Science Workflows with Vaex"
Efficient Data Science Workflows with Vaex delivers a comprehensive exploration of modern data science challenges and introduces Vaex as an innovative solution for handling and analyzing massive datasets at scale. The book presents a compelling case for the transition from traditional in-memory tools, such as pandas and NumPy, to more advanced, out-of-core solutions that effortlessly process data far exceeding physical memory constraints. Through detailed case studies and foundational principles, readers gain a deep understanding of both the limitations of legacy approaches and the critical requirements for building robust, reproducible, and scalable data pipelines.
The book systematically guides practitioners through Vaex's architecture, emphasizing its memory mapping, lazy evaluation, and columnar data handling capabilities. Practical chapters cover everything from efficient data ingestion and preprocessing, advanced transformation techniques, and high-performance analytics to seamless machine learning workflows and interactive visualization. Special attention is given to challenging aspects such as distributed and cloud-based analysis, incorporating strategies for parallelism, cloud-native deployments, and orchestration, all while maintaining security, scalability, and performance.
Featuring real-world case studies and empirical benchmarks comparing Vaex to alternative frameworks, this book is an authoritative reference for data scientists and engineers seeking to maximize efficiency and throughput in their analytics workflows. Best practices, troubleshooting guidance, and insights into the growing Vaex ecosystem ensure that readers are equipped not only to master today's large-scale data challenges but also to contribute to and shape the future of scalable data science.
Efficient Data Science Workflows with Vaex delivers a comprehensive exploration of modern data science challenges and introduces Vaex as an innovative solution for handling and analyzing massive datasets at scale. The book presents a compelling case for the transition from traditional in-memory tools, such as pandas and NumPy, to more advanced, out-of-core solutions that effortlessly process data far exceeding physical memory constraints. Through detailed case studies and foundational principles, readers gain a deep understanding of both the limitations of legacy approaches and the critical requirements for building robust, reproducible, and scalable data pipelines.
The book systematically guides practitioners through Vaex's architecture, emphasizing its memory mapping, lazy evaluation, and columnar data handling capabilities. Practical chapters cover everything from efficient data ingestion and preprocessing, advanced transformation techniques, and high-performance analytics to seamless machine learning workflows and interactive visualization. Special attention is given to challenging aspects such as distributed and cloud-based analysis, incorporating strategies for parallelism, cloud-native deployments, and orchestration, all while maintaining security, scalability, and performance.
Featuring real-world case studies and empirical benchmarks comparing Vaex to alternative frameworks, this book is an authoritative reference for data scientists and engineers seeking to maximize efficiency and throughput in their analytics workflows. Best practices, troubleshooting guidance, and insights into the growing Vaex ecosystem ensure that readers are equipped not only to master today's large-scale data challenges but also to contribute to and shape the future of scalable data science.
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