Download Advanced Analytics with Spark: Patterns for Learning from by Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills PDF

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By Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills

During this sensible publication, 4 Cloudera facts scientists current a collection of self-contained styles for acting large-scale info research with Spark. The authors deliver Spark, statistical equipment, and real-world info units jointly to educate you the way to technique analytics difficulties through example.

You’ll begin with an creation to Spark and its environment, after which dive into styles that practice universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields corresponding to genomics, safety, and finance. when you have an entry-level realizing of computer studying and facts, and also you application in Java, Python, or Scala, you’ll locate those styles worthy for engaged on your individual facts applications.

Patterns include:

• Recommending song and the Audioscrobbler info set
• Predicting wooded area conceal with selection trees
• Anomaly detection in community site visitors with K-means clustering
• realizing Wikipedia with Latent Semantic Analysis
• reading co-occurrence networks with GraphX
• Geospatial and temporal facts research at the ny urban Taxi journeys data
• Estimating monetary hazard via Monte Carlo simulation
• examining genomics info and the BDG project
• studying neuroimaging info with PySpark and Thunder

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Read 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

Example text

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. fm in 2005 can be found online as a com‐ pressed archive. Download the archive, and find within it several files.

More specifically, this example will use a type of matrix factorization model. Mathe‐ matically, these algorithms treat the user and product data as if it were a large matrix A, where the entry at row i and column j exists if user i has played artist j. A is sparse: most entries of A are 0, because only a few of all possible user-artist combinations actually appear in the data. They factor A as the matrix product of two smaller matri‐ ces, X and Y. They are very skinny—both have many rows because A has many rows and columns, but both have just a few columns (k).

When take is called, it accesses the cached elements of cached instead of recomputing them from their dependencies. Spark defines a few different mechanisms, or StorageLevel values, for persisting RDDs. MEMORY), which stores the RDD as unserialized Java objects. When Spark estimates that a partition will not fit in memory, it simply will not store it, and it will be recomputed the next time it’s needed. This level makes the most sense when the objects will be referenced frequently and/or require low-latency access, because it avoids any serialization over‐ head.

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