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 facts research with Spark. The authors carry Spark, statistical tools, and real-world facts units jointly to coach you ways to process analytics difficulties via example.
You’ll commence with an advent to Spark and its surroundings, after which dive into styles that follow universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields akin to genomics, safeguard, and finance. when you've got an entry-level knowing of desktop studying and information, and also you application in Java, Python, or Scala, you’ll locate those styles worthwhile for engaged on your individual facts applications.
• Recommending track and the Audioscrobbler info set
• Predicting wooded area disguise with choice trees
• Anomaly detection in community site visitors with K-means clustering
• figuring out Wikipedia with Latent Semantic Analysis
• interpreting co-occurrence networks with GraphX
• Geospatial and temporal information research at the big apple urban Taxi journeys data
• Estimating monetary chance via Monte Carlo simulation
• studying genomics information and the BDG project
• studying neuroimaging info with PySpark and Thunder
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Additional info for Advanced Analytics with Spark: Patterns for Learning from Data at Scale
Txt/part-00001 Remember that textFile can accept a directory of text files as input, meaning that a future Spark job could refer to mynumbers as an input directory. The raw form of data that is returned by the Scala REPL can be somewhat hard to read, especially for arrays that contain more than a handful of elements. foreach(println) ... ,1,1,1,1,1,TRUE The foreach(println) pattern is one that we will frequently use in this book. It’s an example of a common functional programming pattern, where we pass one function (println) as an argument to another function (foreach) in order to perform some action.
XYT should still be as close to A as possible. After all, it’s all we’ve got to go on. It will not and should not reproduce it exactly. The bad news again is that this can’t be solved directly for both the best X and best Y at the same time. The good news is that it’s trivial to solve for the best X if Y is known, and vice versa. But, neither is known beforehand! 42 | Chapter 3: Recommending Music and the Audioscrobbler Data Set Fortunately, there are algorithms that can escape this catch-22 and find a decent solu‐ tion.
Sometimes, this shortcut just makes the code cryptic. The code listings use one or the other according to our best judgment. Shipping Code from the Client to the Cluster We just saw a wide variety of ways to write and apply functions to data in Scala. All of the code that we executed was done against the data inside the head array, which was contained on our client machine. Now we’re going to take the code that we just wrote and apply it to the millions of linkage records contained in our cluster and repre‐ sented by the rawblocks RDD in Spark.