New PDF release: Advanced Analytics with Spark: Patterns for Learning from

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

During this functional booklet, 4 Cloudera facts scientists current a collection of self-contained styles for appearing large-scale facts research with Spark. The authors carry Spark, statistical tools, and real-world facts units jointly to coach you ways to method analytics difficulties through example.

You’ll commence 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, safeguard, and finance. when you've got an entry-level figuring out of computing device studying and statistics, and also you software in Java, Python, or Scala, you’ll locate those styles helpful for engaged on your personal facts applications.

Patterns include:

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

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

Sample text

When we are performing aggregations on data in the cluster, we always have to be mindful of the fact that the data we are analyzing is stored across multiple machines, and so our aggregations will require moving data over the network that connects the machines. Moving data across the network requires a lot of computational resources: including determining which machines each record will be transferred to, serializing the data, compressing it, sending it over the wire, decompressing and then deserializ‐ ing the results, and finally, performing computations on the aggregated data.

We can experiment with our parsing code in the Scala REPL. split(',') ... ,... Note that we accessed the elements of the head array using parentheses instead of brackets; in Scala, accessing array elements is a function call, not a special operator. apply(5). Structuring Data with Tuples and Case Classes | 23 We broke up the components of line using the split function from Java’s String class, returning an Array[String] that we named pieces. toBoolean Converting the id variables and the matched boolean variable is pretty straightfor‐ ward once we know about the appropriate toXYZ conversion functions.

Moving data across the network requires a lot of computational resources: including determining which machines each record will be transferred to, serializing the data, compressing it, sending it over the wire, decompressing and then deserializ‐ ing the results, and finally, performing computations on the aggregated data. To do this quickly, it is important that we try to minimize the amount of data that we move around; the more filtering that we can do to the data before performing an aggrega‐ tion, the faster we will get an answer to our question.

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