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深圳快乐彩: Big Data Processing with Apache Spark

| by Srini Penchikala Follow 36 Followers , reviewed by Charles Humble Follow 897 Followers on Feb 23, 2018

About the Author

深圳风采开奖号码 www.ljvch.cn Srini Penchikala currently works as Software Architect at a financial services organization in Austin, Texas. He has over 20 years of experience in software architecture, design and development. Srini is currently authoring a book on NoSQL Database Patterns topic. He is also the co-author of "Spring Roo in Action" book from Manning Publications. He has presented at conferences like JavaOne, SEI Architecture Technology Conference (SATURN), IT Architect Conference (ITARC), No Fluff Just Stuff, NoSQL Now and Project World Conference. Srini also published several articles on software architecture, security and risk management, and NoSQL databases on websites like InfoQ, The ServerSide, OReilly Network (ONJava), DevX Java, java.net and JavaWorld. He is a Lead Editor for NoSQL Databases community at InfoQ.

Apache Spark is an open-source big-data processing framework built around speed, ease of use, and sophisticated analytics.

Spark has several advantages compared to other big-data and MapReduce technologies like Hadoop and Storm. It provides a comprehensive, unified framework with which to manage big-data processing requirements for datasets that are diverse in nature (text data, graph data, etc.) and that come from a variety of sources (batch versus real-time streaming data).

Spark enables applications in HDFS clusters to run up to a hundred times faster in memory and ten times faster even when running on disk.

In this mini-book, the reader will learn about the Apache Spark framework and will develop Spark programs for use cases in big-data analysis. The book covers all the libraries that are part of Spark ecosystem, which includes Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Spark GraphX.

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Real-Time Data Management with Apache Spark
Learn how Apache Ignite and GridGain can simplify many Apache Spark tasks, including stream ingestion, data preparation and storage, stream processing, state management, streaming analytics, and machine learning.
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Table of Contents:

  • Part 1: Overview        
  • Part 2: Spark SQL                
  • Part 3: Spark Streaming       
  • Part 4: Spark Machine Learning           
  • Part 5: spark.ml Data Pipelines        
  • Part 6: Graph Data Analytics with Spark GraphX     
  • Part 7: Emerging Trends in Data Science 

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