Wow! eBook
In-Memory Analytics with Apache Arrow: Perform fast and efficient data analytics on both flat and hierarchical structured data
Author(s): Matthew Topol (Author)
- Publisher: Packt Publishing
- Publication Date: 24 Jun. 2022
- Language: English
- Print length: 392 pages
- ISBN-10: 1801071039
- ISBN-13: 9781801071031
Book Description
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.
Table of Contents
- Getting Started with Apache Arrow
- Working with Key Arrow Specifications
- Data Science with Apache Arrow
- Format and Memory Handling
- Crossing the Language Barrier with the Arrow C Data API
- Leveraging the Arrow Compute APIs
- Using the Arrow Datasets API
- Exploring Apache Arrow Flight RPC
- Powered By Apache Arrow
- How to Leave Your Mark on Arrow
- Future Development and Plans
Editorial Reviews
Review
"Since launching as an open source project in 2016, Apache Arrow has rapidly become the de facto standard for interoperability and has accelerated in-memory processing for tabular data. We have broadened support to a dozen programming languages while expanding substantially beyond the project's initial goal of defining a standardized columnar data format to create a true multilanguage developer toolbox for creating high-performance data applications.
While Arrow has helped greatly with improving interoperability and performance in heterogeneous systems (such as across programming languages or different kinds of execution engines), it is also increasingly being chosen as the foundation for building new data processing systems and databases.
This book goes deep into the details of how the different pieces of Arrow work while highlighting the many different building blocks that could be employed by an Arrow user to accelerate or simplify their application. As the first true "Arrow book" since the project's founding, I'm happy to see it written and recommend it to new users of Arrow, as well as existing users who are looking to deepen their knowledge by learning from an expert like Matt."
--
Wes McKinney CTO and co-founder, Voltron Data co-creator, and PMC for Apache Arrow
About the Author
Matthew Topol is an Apache Arrow contributor and a principal software architect at FactSet Research Systems, Inc. Since joining FactSet in 2009, Matt has worked in both infrastructure and application development, led development teams, and architected large-scale distributed systems for processing analytics on financial data. In his spare time, Matt likes to bash his head against a keyboard, develop and run delightfully demented games of fantasy for his victims—er—friends, and share his knowledge with anyone interested enough to listen.
