Spark: The Definitive Guide: Big Data Processing Made Simple (Paperback)
暫譯: Spark：權威指南：簡化大數據處理

Bill Chambers, Matei Zaharia

Spark: The Definitive Guide: Big Data Processing Made Simple (Paperback)

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商品描述

Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of this open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals.

You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine learning library.

  • Get a gentle overview of big data and Spark
  • Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples
  • Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames
  • Understand how Spark runs on a cluster
  • Debug, monitor, and tune Spark clusters and applications
  • Learn the power of Spark’s Structured Streaming and MLlib for machine learning tasks
  • Explore the wider Spark ecosystem, including SparkR and Graph Analysis
  • Examine Spark deployment, including coverage of Spark in the Cloud

商品描述(中文翻譯)

學習如何使用、部署和維護 Apache Spark，這本由該開源集群計算框架的創建者撰寫的綜合指南。作者 Bill Chambers 和 Matei Zaharia 強調 Spark 2.0 的改進和新功能，將 Spark 主題分解為不同的部分，每個部分都有獨特的目標。

您將探索 Spark 結構化 API 的基本操作和常見功能，以及結構化流（Structured Streaming），這是一個用於構建端到端流應用程序的新高級 API。開發人員和系統管理員將學習監控、調整和調試 Spark 的基本原則，並探索使用 MLlib（Spark 的可擴展機器學習庫）的機器學習技術和場景。

- 獲得大數據和 Spark 的簡要概述
- 通過實作範例了解 DataFrames、SQL 和 Datasets——Spark 的核心 API
- 深入了解 Spark 的低級 API、RDD 和 SQL 及 DataFrames 的執行
- 理解 Spark 如何在集群上運行
- 調試、監控和調整 Spark 集群和應用程序
- 學習 Spark 的結構化流和 MLlib 在機器學習任務中的強大功能
- 探索更廣泛的 Spark 生態系統，包括 SparkR 和圖形分析
- 檢查 Spark 的部署，包括雲端中的 Spark 覆蓋範圍

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