In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example.

You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find these patterns useful for working on your own data applications.

Patterns include:

• Recommending music and the Audioscrobbler data set
• Predicting forest cover with decision trees
• Anomaly detection in network traffic with K-means clustering
• Understanding Wikipedia with Latent Semantic Analysis
• Analyzing co-occurrence networks with GraphX
• Geospatial and temporal data analysis on the New York City Taxi Trips data
• Estimating financial risk through Monte Carlo simulation
• Analyzing genomics data and the BDG project

• Analyzing neuroimaging data with PySpark and Thunder

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• 你将从介绍Spark及其生态系统开始，然后深入研究将常见技术--分类、协同过滤和异常检测等应用于基因组学、安全和金融等领域的模式。如果你对机器学习和统计学有初步了解，并使用Java、Python或Scala编程，你会发现这些模式对你自己的数据应用很有用。
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• 模式包括。
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• 推荐音乐和Audioscrobbler数据集
• 用决策树预测森林覆盖率
• 用K-means聚类法检测网络流量的异常情况
• 用潜在语义分析理解维基百科
• 用GraphX分析共同发生网络
• 对纽约市出租车行程数据进行地理空间和时间数据分析
• 通过蒙特卡洛模拟估计金融风险
• 分析基因组学数据和BDG项目
• 用PySpark和Thunder分析神经影像数据