In-Memory Analytics with Apache Arrow: Perform fast and efficient data analytics on both flat and hierarchical structured data
暫譯: 使用 Apache Arrow 進行記憶體分析:對平面和階層結構數據執行快速且高效的數據分析
Topol, Matthew
- 出版商: Packt Publishing
- 出版日期: 2022-06-24
- 定價: $1,660
- 售價: 8.0 折 $1,328
- 語言: 英文
- 頁數: 392
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1801071039
- ISBN-13: 9781801071031
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相關分類:
Data Science
立即出貨(限量) (庫存=1)
相關主題
商品描述
Process tabular data and build high-performance query engines on modern CPUs and GPUs using Apache Arrow, a standardized language-independent memory format, for optimal performance
Key Features
• Learn about Apache Arrow's data types and interoperability with pandas and Parquet
• Work with Apache Arrow Flight RPC, Compute, and Dataset APIs to produce and consume tabular data
• Reviewed, contributed, and supported by Dremio, the co-creator of Apache Arrow
Book Description
Apache Arrow is designed to accelerate analytics and allow the exchange of data across big data systems easily.
In-Memory Analytics with Apache Arrow begins with a quick overview of the Apache Arrow format, before moving on to helping you to understand Arrow's versatility and benefits as you walk through a variety of real-world use cases. You'll cover key tasks such as enhancing data science workflows with Arrow, using Arrow and Apache Parquet with Apache Spark and Jupyter for better performance and hassle-free data translation, as well as working with Perspective, an open source interactive graphical and tabular analysis tool for browsers. As you advance, you'll explore the different data interchange and storage formats and become well-versed with the relationships between Arrow, Parquet, Feather, Protobuf, Flatbuffers, JSON, and CSV. In addition to understanding the basic structure of the Arrow Flight and Flight SQL protocols, you'll learn about Dremio's usage of Apache Arrow to enhance SQL analytics and discover how Arrow can be used in web-based browser apps. Finally, you'll get to grips with the upcoming features of Arrow to help you stay ahead of the curve.
By the end of this book, you will have all the building blocks to create useful, 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 read performance by memory-mapping files with Apache Arrow
• Produce or consume Apache Arrow data efficiently using a C API
• Use the Apache Arrow Compute APIs to perform complex operations
• Create Arrow Flight servers and clients for transferring data quickly
• Build the Arrow libraries locally and contribute back to the community
Who this book is for
This book is for developers, data analysts, and data scientists looking to explore the capabilities of Apache Arrow from the ground up. This book will also be useful for any engineers who are working on building utilities for data analytics and query engines, or otherwise working with tabular data, regardless of the programming language. Some familiarity with basic concepts of data analysis will help you to get the most out of this book but isn't required. Code examples are provided in the C++, Go, and Python programming languages.
商品描述(中文翻譯)
處理表格數據並在現代 CPU 和 GPU 上使用 Apache Arrow 建立高效能查詢引擎,Apache Arrow 是一種標準化的語言獨立記憶體格式,以獲得最佳性能。
主要特點
・了解 Apache Arrow 的數據類型及其與 pandas 和 Parquet 的互操作性
・使用 Apache Arrow Flight RPC、Compute 和 Dataset API 來生成和消費表格數據
・由 Dremio(Apache Arrow 的共同創建者)審核、貢獻和支持
書籍描述
Apache Arrow 的設計旨在加速分析並輕鬆實現大數據系統之間的數據交換。
《使用 Apache Arrow 進行內存分析》首先快速概述 Apache Arrow 格式,然後幫助您理解 Arrow 的多功能性和優勢,並通過各種實際用例進行學習。您將涵蓋關鍵任務,例如使用 Arrow 增強數據科學工作流程,使用 Arrow 和 Apache Parquet 與 Apache Spark 和 Jupyter 以獲得更好的性能和無縫的數據轉換,以及使用 Perspective,這是一個開源的互動圖形和表格分析工具,適用於瀏覽器。隨著進展,您將探索不同的數據交換和存儲格式,並熟悉 Arrow、Parquet、Feather、Protobuf、Flatbuffers、JSON 和 CSV 之間的關係。除了理解 Arrow Flight 和 Flight SQL 協議的基本結構外,您還將了解 Dremio 如何使用 Apache Arrow 來增強 SQL 分析,並發現 Arrow 如何在基於網頁的瀏覽器應用中使用。最後,您將掌握 Arrow 的即將推出的功能,以幫助您保持領先地位。
在本書結束時,您將擁有所有構建模塊,以使用 Apache Arrow 創建有用、高效和強大的分析服務和工具。
您將學到的內容
・使用 Apache Arrow 庫訪問本地和雲端的數據文件
・理解 Apache Arrow 格式的零拷貝元素
・通過使用 Apache Arrow 進行記憶體映射文件來提高讀取性能
・使用 C API 高效生成或消費 Apache Arrow 數據
・使用 Apache Arrow Compute API 執行複雜操作
・創建 Arrow Flight 伺服器和客戶端以快速傳輸數據
・在本地構建 Arrow 庫並回饋社群
本書適合對象
本書適合希望從基礎開始探索 Apache Arrow 功能的開發人員、數據分析師和數據科學家。本書對於任何正在構建數據分析和查詢引擎工具的工程師,或是與表格數據打交道的工程師(無論使用何種編程語言)也將非常有用。對數據分析基本概念的熟悉將幫助您充分利用本書,但並非必需。代碼示例提供了 C++、Go 和 Python 編程語言的範例。
目錄大綱
1. Getting Started with Apache Arrow
2. Working with Key Arrow Specifications
3. Data Science with Apache Arrow
4. Format and Memory Handling
5. Crossing the Language Barrier with the Arrow C Data API
6. Leveraging the Arrow Compute APIs
7. Using the Arrow Datasets API
8. Exploring Apache Arrow Flight RPC
9. Powered By Apache Arrow
10. How to Leave Your Mark on Arrow
11. Future Development and Plans
目錄大綱(中文翻譯)
1. Getting Started with Apache Arrow
2. Working with Key Arrow Specifications
3. Data Science with Apache Arrow
4. Format and Memory Handling
5. Crossing the Language Barrier with the Arrow C Data API
6. Leveraging the Arrow Compute APIs
7. Using the Arrow Datasets API
8. Exploring Apache Arrow Flight RPC
9. Powered By Apache Arrow
10. How to Leave Your Mark on Arrow
11. Future Development and Plans