By Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills
During this functional ebook, 4 Cloudera facts scientists current a suite of self-contained styles for appearing large-scale facts research with Spark. The authors carry Spark, statistical tools, and real-world info units jointly to coach you the way to method analytics difficulties via example.
You’ll begin with an advent to Spark and its atmosphere, after which dive into styles that observe universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields resembling genomics, defense, and finance. when you have an entry-level realizing of computer studying and records, and also you application in Java, Python, or Scala, you’ll locate those styles priceless for engaged on your personal info applications.
• Recommending song and the Audioscrobbler facts set
• Predicting wooded area hide with selection trees
• Anomaly detection in community site visitors with K-means clustering
• figuring out Wikipedia with Latent Semantic Analysis
• reading co-occurrence networks with GraphX
• Geospatial and temporal info research at the manhattan urban Taxi journeys data
• Estimating monetary danger via Monte Carlo simulation
• reading genomics facts and the BDG project
• interpreting neuroimaging information with PySpark and Thunder
Read Online or Download Advanced Analytics with Spark: Patterns for Learning from Data at Scale PDF
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Extra resources for Advanced Analytics with Spark: Patterns for Learning from Data at Scale
Getting Started: The Spark Shell and SparkContext | 17 The REPL and Compilation In addition to its interactive shell, Spark also supports compiled applications. We typ‐ ically recommend using Maven for compiling and managing dependencies. The Git‐ Hub repository included with this book holds a self-contained Maven project setup under the simplesparkproject/ directory to help you with getting started. With both the shell and compilation as options, which should you use when testing out and building a data pipeline?
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. More specifically still, the example in this chapter will use the Alternating Least Squares (ALS) algorithm to compute X and Y.
Again, the simplesparkproject/ directory in the GitHub repository shows you how to accomplish this. SPARK-5341 also tracks development on the capability to specify Maven repositories directly when invoking spark-shell and have the JARs from these repositories auto‐ matically show up on Spark’s classpath. Bringing Data from the Cluster to the Client RDDs have a number of methods that allow us to read data from the cluster into the Scala REPL on our client machine. first ... res: String = "id_1","id_2","cmp_fname_c1","cmp_fname_c2",...
Advanced Analytics with Spark: Patterns for Learning from Data at Scale by Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills