Efficient Data Science Workflows With Vaex
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Efficient Data Science Workflows with Vaex
"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.
Introduction to Text Analytics
Author: Emily Ohman
language: en
Publisher: SAGE Publications Limited
Release Date: 2024-11-30
Clear, nuanced introduction to digital text mining and data analysis specifically for students in digital humanities and computational social science.
Data Science
Author: Dr.N.Rathina Kumar
language: en
Publisher: Leilani Katie Publication
Release Date: 2025-03-23
Dr.N.Rathina Kumar, Assistant Professor, Department of Artificial Intelligence and Data Sciences, SNS College of Engineering, Coimbatore, Tamil Nadu, India. Prof. Mrunalini U. Buradkar, Assistant Professor, Department of Electronics & Telecommunication Engineering, St. Vincent Pallotti College of Engineering & Technology, Nagpur, Maharashtra, India. Dr.P.Rama, Assistant Professor, Department of Computing Technologies, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chengalpattu, Tamil Nadu, India. Dr.Krishna Murthy Inumula, Associate Professor, Symbiosis Institute of International Business (SIIB), Symbiosis International (Deemed University), Pune, Maharashtra, India.