Download e-book for kindle: 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

ISBN-10: 1491912766

ISBN-13: 9781491912768

During this useful booklet, 4 Cloudera information scientists current a suite of self-contained styles for appearing large-scale facts research with Spark. The authors deliver Spark, statistical equipment, and real-world information units jointly to educate you the way to strategy analytics difficulties by way of example.

You’ll begin with an creation to Spark and its surroundings, after which dive into styles that observe universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields similar to genomics, defense, and finance. in case you have an entry-level realizing of laptop studying and data, and also you software in Java, Python, or Scala, you’ll locate those styles important for engaged on your individual facts applications.

Patterns include:

• Recommending track and the Audioscrobbler information set
• Predicting woodland disguise with determination trees
• Anomaly detection in community site visitors with K-means clustering
• figuring out Wikipedia with Latent Semantic Analysis
• examining co-occurrence networks with GraphX
• Geospatial and temporal information research at the manhattan urban Taxi journeys data
• Estimating monetary hazard via Monte Carlo simulation
• studying genomics info and the BDG project
• reading neuroimaging information with PySpark and Thunder

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Extra info for Advanced Analytics with Spark: Patterns for Learning from Data at Scale

Sample text

RDDs are a convenient way to describe the computations that we want to perform on our data as a sequence of small, independent steps. Resilient Distributed Datasets An RDD is laid out across the cluster of machines as a collection of partitions, each including a subset of the data. Partitions define the unit of parallelism in Spark. The framework processes the objects within a partition in sequence, and processes multi‐ ple partitions in parallel. parallelize(Array(1, 2, 2, 4), 4) ... RDD[Int] = ...

You or I may occasionally play a song by an artist we don’t care for, or even play an album and walk out of the room. However, listeners rate music far less frequently than they play music. A data set like this is therefore much larger, covers more users and artists, and contains more total information than a rating data set, even if each individual data point carries less information. This type of data is often called implicit feedback data because the userartist connections are implied as a side effect of other actions, and not given as explicit ratings or thumbs-up.

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.

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Advanced Analytics with Spark: Patterns for Learning from Data at Scale by Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills

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