Spark for Data Science Cookbook

Padma Priya Chitturi

  • 出版商: Packt Publishing
  • 出版日期: 2016-12-23
  • 售價: $1,920
  • 貴賓價: 9.5$1,824
  • 語言: 英文
  • 頁數: 358
  • 裝訂: Paperback
  • ISBN: 1785880101
  • ISBN-13: 9781785880100
  • 相關分類: SparkData Science
  • 海外代購書籍(需單獨結帳)
    無現貨庫存(No stock available)


Key Features

  • Use Apache Spark for data processing with these hands-on recipes
  • Implement end-to-end, large-scale data analysis better than ever before
  • Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data

Book Description

Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark’s selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease.

This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark’s data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work.

What you will learn

  • Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning.
  • Solve real-world analytical problems with large data sets.
  • Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale.
  • Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package.
  • Learn about numerical and scientific computing using NumPy and SciPy on Spark.
  • Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models.

About the Author

Padma Priya Chitturi is Analytics Lead at Fractal Analytics Pvt Ltd and has over five years of experience in Big Data processing. Currently, she is part of capability development at Fractal and responsible for solution development for analytical problems across multiple business domains at large scale. Prior to this, she worked for an Airlines product on a real-time processing platform serving one million user requests/sec at Amadeus Software Labs. She has worked on realizing large-scale deep networks (Jeffrey dean's work in Google brain) for image classification on the big data platform Spark. She works closely with Big Data technologies such as Spark, Storm, Cassandra and Hadoop. She was an open source contributor to Apache Storm.

Table of Contents

  1. Big Data Analytics with Spark
  2. Tricky Statistics with Spark
  3. Data Analysis with Spark
  4. Clustering, Classification, and Regression
  5. Working with Spark MLlib
  6. NLP with Spark
  7. Working with Sparkling Water - H2O
  8. Data Visualization with Spark
  9. Deep Learning on Spark
  10. Working with SparkR



  • 使用Apache Spark進行數據處理的實踐配方

  • 比以往更好地實現端到端的大規模數據分析

  • 使用強大的庫,如MLLib、SciPy、NumPy和Pandas,從數據中獲取洞察力


Spark已成為最有前景的大數據分析引擎,適用於數據科學專業人士。Apache Spark的真正力量和價值在於其以速度和準確性執行數據科學任務的能力。Spark的賣點在於它結合了ETL、批量分析、實時流分析、機器學習、圖形處理和可視化。它讓您輕鬆應對原始非結構化數據集帶來的複雜性。



  • 探索數據挖掘、文本挖掘、自然語言處理、信息檢索和機器學習等主題。

  • 使用大數據集解決現實世界的分析問題。

  • 使用Spark等分佈式系統上的分析工具應對數據科學挑戰(適用於迭代算法),提供內存處理和更靈活的大規模數據分析。

  • 使用Spark MLLib套件在真實數據集上進行分類、回歸和推薦等算法的實踐。

  • 使用NumPy和SciPy在Spark上進行數值和科學計算。

  • 在Spark中使用預測模型標記語言(PMML)進行統計數據挖掘模型。


Padma Priya Chitturi是Fractal Analytics Pvt Ltd的分析主管,擁有超過五年的大數據處理經驗。目前,她是Fractal的能力開發部門的一員,負責解決大規模商業領域的分析問題的解決方案開發。在此之前,她在Amadeus Software Labs為航空公司產品工作,該產品在實時處理平台上每秒提供一百萬個用戶請求。她曾在大數據平台Spark上實現大規模深度網絡(Google Brain中Jeffrey Dean的工作)進行圖像分類。她密切與Spark、Storm、Cassandra和Hadoop等大數據技術合作。她曾是Apache Storm的開源貢獻者。


  1. 使用Spark進行大數據分析

  2. 使用Spark進行棘手的統計分析

  3. 使用Spark進行數據分析

  4. 聚類、分類和回歸

  5. 使用Spark MLlib進行工作

  6. 使用Spark進行自然語言處理

  7. 使用Sparkling Water - H2O進行工作

  8. 使用Spark進行數據可視化

  9. 在Spark上進行深度學習

  10. 使用SparkR進行工作