Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách Hands-on Guide to Apache Spark 3 Build Scalable Computing Engines for Batch and Stream Data Processing (sách keo gáy, bìa mềm)
Thể loại:Computers - Organization and Data Processing
Năm:2023
Ngôn ngữ:english
Trang:404
This book explains how to scale Apache Spark 3 to handle massive amounts
of data, either via batch or streaming processing. It covers how to use
Spark’s structured APIs to perform complex data transformations and
analyses you can use to implement end-to-end analytics workflows. This
book covers Spark 3's new features, theoretical foundations, and
application architecture. The first section introduces the Apache Spark
ecosystem as a unified engine for large scale data analytics, and shows
you how to run and fine-tune your first application in Spark. The second
section centers on batch processing suited to end-of-cycle processing,
and data ingestion through files and databases. It explains Spark
DataFrame API as well as structured and unstructured data with Apache
Spark. The last section deals with scalable, high-throughput,
fault-tolerant streaming processing workloads to process real-time data.
Here you'll learn about Apache Spark Streaming’s execution model, the
architecture of Spark Streaming, monitoring, reporting, and recovering
Spark streaming. A full chapter is devoted to future directions for
Spark Streaming. With real-world use cases, code snippets, and notebooks
hosted on GitHub, this book will give you an understanding of
large-scale data analysis concepts--and help you put them to use. Upon
completing this book, you will have the knowledge and skills to
seamlessly implement large-scale batch and streaming workloads to
analyze real-time data streams with Apache Spark. What You Will Learn
Master the concepts of Spark clusters and batch data processing
Understand data ingestion, transformation, and data storage Gain insight
into essential stream processing concepts and different streaming
architectures Implement streaming jobs and applications with Spark
Streaming Who This Book Is ForData engineers, data analysts, machine
learning engineers, Python and R programmers
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