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

During this functional booklet, 4 Cloudera information scientists current a suite of self-contained styles for appearing large-scale information research with Spark. The authors carry Spark, statistical equipment, and real-world facts units jointly to educate you ways to procedure analytics difficulties via example.

You’ll begin with an advent to Spark and its atmosphere, after which dive into styles that practice universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields comparable to genomics, safeguard, and finance. in case you have an entry-level figuring out of laptop studying and data, and also you software in Java, Python, or Scala, you’ll locate those styles helpful for engaged on your individual info applications.

Patterns include:

• Recommending track and the Audioscrobbler info set
• Predicting woodland hide with selection trees
• Anomaly detection in community site visitors with K-means clustering
• realizing Wikipedia with Latent Semantic Analysis
• interpreting co-occurrence networks with GraphX
• Geospatial and temporal information research at the long island urban Taxi journeys data
• Estimating monetary probability via Monte Carlo simulation
• examining genomics info and the BDG project
• reading neuroimaging info with PySpark and Thunder

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

During this sensible publication, 4 Cloudera facts scientists current a collection of self-contained styles for appearing large-scale information research with Spark. The authors deliver Spark, statistical tools, and real-world info units jointly to educate you ways to process analytics difficulties via example.

You’ll begin with an creation to Spark and its environment, after which dive into styles that observe universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields comparable to genomics, protection, and finance. when you've got an entry-level realizing of computing device studying and statistics, and also you application in Java, Python, or Scala, you’ll locate those styles invaluable for engaged on your personal facts applications.

Patterns include:

• Recommending song and the Audioscrobbler facts set
• Predicting wooded area hide with selection trees
• Anomaly detection in community site visitors with K-means clustering
• knowing Wikipedia with Latent Semantic Analysis
• interpreting 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
• interpreting neuroimaging info with PySpark and Thunder

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

Sample text

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. Its drawback is that it takes up larger amounts of memory than its alternatives.

We need an algorithm that could provide decent recommendations to even these users. After all, every single listener must have started with just one play at some point! Finally, we need an algorithm that scales, both in its ability to build large models and to create recommendations quickly. Recommendations are typically required in near real time—within a second, not tomorrow. This example will employ a member of a broad class of algorithms called latent-factor models. They try to explain observed interactions between large numbers of users and products through a relatively small number of unobserved, underlying reasons.

The next nine values are (possibly missing) double values that represent match scores on different fields of the patient records, such as their names, birthdays, and location. • The last field is a boolean value (TRUE or FALSE) indicating whether or not the pair of patient records represented by the line was a match. Like Python, Scala has a built-in tuple type that we can use to quickly create pairs, triples, and larger collections of values of different types as a simple way to represent records.

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