Real-Time Big Data Analytics(Paperback)

Sumit Gupta, Shilpi

  • 出版商: Packt Publishing
  • 出版日期: 2016-02-29
  • 售價: $1,830
  • 貴賓價: 9.5$1,739
  • 語言: 英文
  • 頁數: 326
  • 裝訂: Paperback
  • ISBN: 1784391409
  • ISBN-13: 9781784391409
  • 相關分類: 大數據 Big-dataData Science
  • 下單後立即進貨 (約3~4週)

商品描述

Design, process, and analyze large sets of complex data in real time

About This Book

  • Get acquainted with transformations and database-level interactions, and ensure the reliability of messages processed using Storm
  • Implement strategies to solve the challenges of real-time data processing
  • Load datasets, build queries, and make recommendations using Spark SQL

Who This Book Is For

If you are a Big Data architect, developer, or a programmer who wants to develop applications/frameworks to implement real-time analytics using open source technologies, then this book is for you.

What You Will Learn

  • Explore big data technologies and frameworks
  • Work through practical challenges and use cases of real-time analytics versus batch analytics
  • Develop real-word use cases for processing and analyzing data in real-time using the programming paradigm of Apache Storm
  • Handle and process real-time transactional data
  • Optimize and tune Apache Storm for varied workloads and production deployments
  • Process and stream data with Amazon Kinesis and Elastic MapReduce
  • Perform interactive and exploratory data analytics using Spark SQL
  • Develop common enterprise architectures/applications for real-time and batch analytics

In Detail

Enterprise has been striving hard to deal with the challenges of data arriving in real time or near real time.

Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases.

From the beginning of the book, we will cover the basics of varied real-time data processing frameworks and technologies. We will discuss and explain the differences between batch and real-time processing in detail, and will also explore the techniques and programming concepts using Apache Storm.

Moving on, we'll familiarize you with “Amazon Kinesis” for real-time data processing on cloud. We will further develop your understanding of real-time analytics through a comprehensive review of Apache Spark along with the high-level architecture and the building blocks of a Spark program.

You will learn how to transform your data, get an output from transformations, and persist your results using Spark RDDs, using an interface called Spark SQL to work with Spark.

At the end of this book, we will introduce Spark Streaming, the streaming library of Spark, and will walk you through the emerging Lambda Architecture (LA), which provides a hybrid platform for big data processing by combining real-time and precomputed batch data to provide a near real-time view of incoming data.

Style and approach

This step-by-step is an easy-to-follow, detailed tutorial, filled with practical examples of basic and advanced features.

Each topic is explained sequentially and supported by real-world examples and executable code snippets.

商品描述(中文翻譯)

設計、處理和分析大量複雜數據的實時數據

關於本書



  • 熟悉轉換和數據庫層級的互動,並確保使用Storm處理的消息的可靠性

  • 實施解決實時數據處理挑戰的策略

  • 使用Spark SQL加載數據集,構建查詢並提供建議


本書適合對象


如果您是一位大數據架構師、開發人員或程序員,希望使用開源技術開發應用程序/框架來實現實時分析,那麼本書適合您。


您將學到什麼



  • 探索大數據技術和框架

  • 解決實時分析與批量分析的實際挑戰和用例

  • 使用Apache Storm的編程範式開發實時處理和分析數據的真實用例

  • 處理和處理實時交易數據

  • 優化和調整Apache Storm以適應不同的工作負載和生產部署

  • 使用Amazon Kinesis和Elastic MapReduce處理和流式傳輸數據

  • 使用Spark SQL進行交互式和探索性數據分析

  • 為實時和批量分析開發常見的企業架構/應用程序


詳細內容


企業一直在努力應對實時或接近實時到達的數據的挑戰。


儘管有諸如Storm和Spark等技術(還有更多)可以解決實時數據的挑戰,但使用適當的技術/框架來應對正確的業務用例是成功的關鍵。本書通過大數據實際用例的真實示例,為您提供快速設計、實施和部署實時分析所需的技能。


從本書的開始,我們將介紹各種實時數據處理框架和技術的基礎知識。我們將詳細討論和解釋批量處理和實時處理之間的差異,並且還將使用Apache Storm探索技術和編程概念。


接下來,我們將通過全面回顧Apache Spark以及Spark程序的高級架構和構建塊,進一步加深您對實時分析的理解。


您將學習如何轉換數據,從轉換中獲得輸出,並使用稱為Spark SQL的接口與Spark一起工作。


在本書的最後,我們將介紹Spark Streaming,即Spark的流式庫,並將引導您了解Lambda架構(LA),該架構通過結合實時和預計算批量數據,提供對即將到來的數據的接近實時視圖。


風格和方法


這是一本易於遵循的逐步詳細教程,充滿了基本和高級功能的實際示例。


每個主題都按順序解釋,並通過真實世界的示例和可執行的代碼片段進行支持。