Advanced Analytics with Spark: Patterns for Learning from by Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills
By Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills
During this sensible ebook, 4 Cloudera info scientists current a collection of self-contained styles for acting large-scale facts research with Spark. The authors deliver Spark, statistical equipment, and real-world facts units jointly to educate you ways to technique analytics difficulties through example.
You’ll begin with an creation to Spark and its surroundings, after which dive into styles that practice universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields equivalent to genomics, safety, and finance. in case you have an entry-level knowing of computer studying and statistics, and also you software in Java, Python, or Scala, you’ll locate those styles helpful for engaged on your personal info applications.
• Recommending track and the Audioscrobbler info set
• Predicting woodland disguise with selection trees
• Anomaly detection in community site visitors with K-means clustering
• realizing Wikipedia with Latent Semantic Analysis
• examining co-occurrence networks with GraphX
• Geospatial and temporal info research at the long island urban Taxi journeys data
• Estimating monetary possibility via Monte Carlo simulation
• examining genomics info and the BDG project
• examining neuroimaging information with PySpark and Thunder
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Additional resources for Advanced Analytics with Spark: Patterns for Learning from Data at Scale
It’s an accessible way to introduce real-world use of Spark and MLlib, and some basic machine learning ideas that will be developed in subsequent chapters. Data Set This example will use a data set published by Audioscrobbler. fm, one of the first Internet streaming radio sites, founded in 2002. Audioscrobbler provided an open API for “scrobbling,” or recording listeners’ plays of artists’ songs. fm used this information to build a powerful music recommender engine. The system reached millions of users because third-party apps and sites could provide listening data back to the recommender engine.
In this case, we defined an anonymous function that takes a single argument called x and Bringing Data from the Cluster to the Client | 21 passes x to the isHeader function and returns the negation of the result. Note that we did not have to specify any type information for the x variable in this instance; the Scala compiler was able to infer that x is a String from the fact that head is an Array[String]. There is nothing that Scala programmers hate more than typing, so Scala has lots of little features that are designed to reduce the amount of typing they have to do.
Map(line => parse(line)) Remember that unlike the mds array that we generated locally, the parse function has not actually been applied to the data on the cluster yet. Once we make a call to the parsed RDD that requires some output, the parse function will be applied to convert each String in the noheader RDD into an instance of our MatchData class. If we make another call to the parsed RDD that generates a different output, the parse function will be applied to the input data again. This isn’t an optimal use of our cluster resources; after the data has been parsed once, we’d like to save the data in its parsed form on the cluster so that we don’t have to reparse it every time we want to ask a new question of the data.