By Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills
During this useful booklet, 4 Cloudera info scientists current a suite of self-contained styles for acting large-scale info research with Spark. The authors carry Spark, statistical equipment, and real-world information units jointly to coach you the way to procedure analytics difficulties via example.
You’ll commence with an creation to Spark and its atmosphere, after which dive into styles that follow universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields akin to genomics, safety, and finance. when you've got an entry-level knowing of laptop studying and facts, and also you application in Java, Python, or Scala, you’ll locate those styles worthwhile for engaged on your personal information applications.
• Recommending song and the Audioscrobbler facts set
• Predicting woodland conceal with determination trees
• Anomaly detection in community site visitors with K-means clustering
• figuring out Wikipedia with Latent Semantic Analysis
• studying co-occurrence networks with GraphX
• Geospatial and temporal info research at the big apple urban Taxi journeys data
• Estimating monetary probability via Monte Carlo simulation
• studying genomics information and the BDG project
• studying neuroimaging info with PySpark and Thunder
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Extra info for Advanced Analytics with Spark: Patterns for Learning from Data at Scale
This is much of the reason it is the topic of this chapter —that, and the fact that ALS is the only recommender algorithm currently imple‐ mented in Spark MLlib! Preparing the Data Copy all three data files into HDFS. This chapter will assume that the files are avail‐ able at /user/ds/. Start spark-shell. Note that this computation will take an unusually large amount of memory. If you are running locally, rather than on a cluster, for Preparing the Data | 43 example, you will likely need to specify --driver-memory 6g to have enough mem‐ ory to complete these computations.
A case class is a simple type of immutable class that comes with implementa‐ tions of all of the basic Java class methods, like toString, equals, and hashCode, which makes them very easy to use. toBoolean MatchData(id1, id2, scores, matched) } val md = parse(line) There are two things to note here: first, we do not need to specify the keyword new in front of MatchData when we create a new instance of our case class (another example of how much Scala developers hate typing). Second, our MatchData class comes with a built-in toString implementation that works great for every field except for the scores array.
Implicits work like this: if you call a method on a Scala object, and the Scala compiler does not see a definition for that method in the class definition for that object, the compiler will try to convert your object to an instance of a class that does have that method defined. In this case, the compiler will see that Java’s String class does not have a toInt method defined, but the StringOps class does, and that the StringOps class has a method that can convert an instance of the String class into an instance of the StringOps class.
Advanced Analytics with Spark: Patterns for Learning from Data at Scale by Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills