Arrow for Efficient Date and Time Handling (eBook, ePUB)
Definitive Reference for Developers and Engineers
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"Arrow for Efficient Date and Time Handling" "Arrow for Efficient Date and Time Handling" is a comprehensive guide dedicated to mastering robust, clear, and high-performance temporal data operations in modern Python applications. Addressing the unique challenges of global timekeeping, this book methodically explores limitations in Python's built-in datetime module, offering expert insights on regulatory, audit, and security imperatives, as well as requirements for concurrent and distributed systems. Through practical explanations and real-world case studies, it frames complex issues-such as ti...
"Arrow for Efficient Date and Time Handling"
"Arrow for Efficient Date and Time Handling" is a comprehensive guide dedicated to mastering robust, clear, and high-performance temporal data operations in modern Python applications. Addressing the unique challenges of global timekeeping, this book methodically explores limitations in Python's built-in datetime module, offering expert insights on regulatory, audit, and security imperatives, as well as requirements for concurrent and distributed systems. Through practical explanations and real-world case studies, it frames complex issues-such as time zone management, daylight savings adjustments, and localization-within the context of ever-evolving data ecosystems and internationalized applications.
Drawing from the architectural strengths of the Arrow library, the book delves deeply into Arrow's UTC-centric and immutable object model, fluent API, and superior interoperability with ecosystems like pandas and NumPy. Each chapter provides actionable guidance on advanced topics: from constructing, parsing, and localizing time data at scale, to implementing precise formatting, batch serializing, and optimizing for analytics workloads. With dedicated sections on temporal arithmetic, high-volume ingestion, distributed synchronization, and thread-safe patterns, practitioners are equipped to solve the most demanding problems in time-aware programming.
The volume also serves as an authoritative reference for contributing to and extending Arrow itself. Learn how to craft custom formatters and plugins, conduct rigorous testing, maintain API stability, and migrate legacy systems with confidence. Advanced use cases illuminate the nuances of securing temporal data, leveraging Arrow in event-driven frameworks, and foreseeing the future of datetime standards. Whether integrating Arrow into financial applications, IoT systems, or cloud-native architectures, this book empowers engineers, data scientists, and architects to build accurate, maintainable, and globally robust time-handling solutions.
"Arrow for Efficient Date and Time Handling" is a comprehensive guide dedicated to mastering robust, clear, and high-performance temporal data operations in modern Python applications. Addressing the unique challenges of global timekeeping, this book methodically explores limitations in Python's built-in datetime module, offering expert insights on regulatory, audit, and security imperatives, as well as requirements for concurrent and distributed systems. Through practical explanations and real-world case studies, it frames complex issues-such as time zone management, daylight savings adjustments, and localization-within the context of ever-evolving data ecosystems and internationalized applications.
Drawing from the architectural strengths of the Arrow library, the book delves deeply into Arrow's UTC-centric and immutable object model, fluent API, and superior interoperability with ecosystems like pandas and NumPy. Each chapter provides actionable guidance on advanced topics: from constructing, parsing, and localizing time data at scale, to implementing precise formatting, batch serializing, and optimizing for analytics workloads. With dedicated sections on temporal arithmetic, high-volume ingestion, distributed synchronization, and thread-safe patterns, practitioners are equipped to solve the most demanding problems in time-aware programming.
The volume also serves as an authoritative reference for contributing to and extending Arrow itself. Learn how to craft custom formatters and plugins, conduct rigorous testing, maintain API stability, and migrate legacy systems with confidence. Advanced use cases illuminate the nuances of securing temporal data, leveraging Arrow in event-driven frameworks, and foreseeing the future of datetime standards. Whether integrating Arrow into financial applications, IoT systems, or cloud-native architectures, this book empowers engineers, data scientists, and architects to build accurate, maintainable, and globally robust time-handling solutions.
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