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Books > Computing & IT > Applications of computing > Databases > Data capture & analysis
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In-Memory Analytics with Apache Arrow - Perform fast and efficient data analytics on both flat and hierarchical structured data (Paperback)
Loot Price: R1,558
Discovery Miles 15 580
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In-Memory Analytics with Apache Arrow - Perform fast and efficient data analytics on both flat and hierarchical structured data (Paperback)
Expected to ship within 10 - 15 working days
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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 DescriptionApache 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 forThis 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.
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