Arrow Flight Github
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In-Memory Analytics with Apache Arrow
Author: Matthew Topol
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-09-30
Harness the power of Apache Arrow to optimize tabular data processing and develop robust, high-performance data systems with its standardized, language-independent columnar memory format Key Features Explore Apache Arrow's data types and integration with pandas, Polars, and Parquet Work with Arrow libraries such as Flight SQL, Acero compute engine, and Dataset APIs for tabular data Enhance and accelerate machine learning data pipelines using Apache Arrow and its subprojects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionApache Arrow is an open source, columnar in-memory data format designed for efficient data processing and analytics. This book harnesses the author’s 15 years of experience to show you a standardized way to work with tabular data across various programming languages and environments, enabling high-performance data processing and exchange. This updated second edition gives you an overview of the Arrow format, highlighting its versatility and benefits through real-world use cases. It guides you through enhancing data science workflows, optimizing performance with Apache Parquet and Spark, and ensuring seamless data translation. You’ll explore data interchange and storage formats, and Arrow's relationships with Parquet, Protocol Buffers, FlatBuffers, JSON, and CSV. You’ll also discover Apache Arrow subprojects, including Flight, SQL, Database Connectivity, and nanoarrow. You’ll learn to streamline machine learning workflows, use Arrow Dataset APIs, and integrate with popular analytical data systems such as Snowflake, Dremio, and DuckDB. The latter chapters provide real-world examples and case studies of products powered by Apache Arrow, providing practical insights into its applications. By the end of this book, you’ll have all the building blocks to create efficient and powerful analytical services and utilities with Apache Arrow.What you will learn Use Apache Arrow libraries to access data files, both locally and in the cloud Understand the zero-copy elements of the Apache Arrow format Improve the read performance of data pipelines by memory-mapping Arrow files Produce and consume Apache Arrow data efficiently by sharing memory with the C API Leverage the Arrow compute engine, Acero, to perform complex operations Create Arrow Flight servers and clients for transferring data quickly Build the Arrow libraries locally and contribute to the community Who this book is for This book is for developers, data engineers, and data scientists looking to explore the capabilities of Apache Arrow from the ground up. Whether you’re building utilities for data analytics and query engines, or building full pipelines with tabular data, this book can help you out regardless of your preferred programming language. A basic understanding of data analysis concepts is needed, but not necessary. Code examples are provided using C++, Python, and Go throughout the book.
Scaling Up with R and Apache Arrow
Analyze large datasets directly from R. Scaling Up With R and Arrow provides a guide to working efficiently with larger-than-memory datasets using the arrow R package. As data grows in size and complexity, traditional data analysis methods in R often hit technical limitations. In this book, you'll learn how to overcome these hurdles without needing to set up complex infrastructure. You'll learn about the Apache Arrow project's origins, goals, and its significance in bridging the gap between data science and big data ecosystems. You'll also learn how to leverage the arrow R package to work directly with files in various formats, such as CSV and Parquet, using familiar dplyr syntax. This book explores practical topics like data manipulation, file formats, working with larger datasets, and optimizing workflows for data in cloud storage. Advanced chapters examine user-defined functions, integration with other tools like DuckDB, and extending Arrow's capabilities to work with geospatial data. Written by developers of the Arrow R package, this guide is essential for anyone looking to scale their data processing capabilities in R.
Python for Quantum Chemistry
Quantum chemistry requires ever higher computational performance, with more and more sophisticated and dedicated Python scripts being required to solve challenging problems. Although resources for basic use of Python are widely (and often freely) available online and in literature, truly cohesive materials for advanced Python programming skills are lacking.Qiming Sun, a developer of the popular Python package PySCF, provides a comprehensive, end-to-end practical resource for researchers and engineers who have basic Python programming experiences chiefly in computational chemistry but want to take their use of the software forwards to the next level, the book provides an insightful exploration of Numpy, Pandas, and other data analysis tools. Readers will learn how to manage their Python computational projects in a professional way, with various tools and protocols for computational chemistry research and general scientific computing tasks exhibited and analysed from a technical perspective. Multiple programming paradigms including object-oriented, functional, meta-programming, dynamic, concurrent, and vector-oriented are illustrated in various technology scenarios allowing readers to properly use them to enhance their program projects. Readers will also learn how to use the presented optimization technologies to speed up their Python applications, even to the level as fast as a native C++ implementation. The applications of these technologies are then demonstrated using quantum chemistry Python applications.Python for Quantum Chemistry: A Full Stack Programming Guide is written primarily for graduate students, researchers and software engineers working primarily in the fields of theoretical chemistry, computational chemistry, condensed matter physics, material modelling, molecular simulations, and quantum computing. - End-to end guide for advanced Python programming skills and tools related to quantum chemistry research - Tackles the following questions: How can you ensure the Python runtime is manageable when the preliminary implementation becomes complicated or evolves many branches? How do I ensure that others' Python program works properly in my project? How do I make my Python project reusable for others? - Covers in depth the crucial topic of Python code optimization methods with high-performance computing technologies - Provides examples of Python applications with cutting-edge technologies such as automatic code generation, cloud computing, and GPGPU - Includes discussion of Python runtime mechanism and advanced Python technologies