By Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills
During this sensible e-book, 4 Cloudera facts scientists current a collection of self-contained styles for acting large-scale facts research with Spark. The authors carry Spark, statistical tools, and real-world facts units jointly to coach you the way to technique analytics difficulties through example.
You’ll commence with an creation to Spark and its environment, after which dive into styles that follow 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 knowing of laptop studying and data, and also you application in Java, Python, or Scala, you’ll locate those styles beneficial for engaged on your individual information applications.
• Recommending tune and the Audioscrobbler facts set
• Predicting wooded area hide 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 long island urban Taxi journeys data
• Estimating monetary threat via Monte Carlo simulation
• interpreting genomics information and the BDG project
• reading neuroimaging facts with PySpark and Thunder
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Extra resources for Advanced Analytics with Spark: Patterns for Learning from Data at Scale
As you become a seasoned user of Spark and Scala for data analysis, it’s likely that you will reach a point where you begin to build tools and libraries that are designed to help other analysts and data scientists apply Spark to solve their own problems. At that point in your development, it would be helpful to pick up additional books on Scala, like Programming Scala by Dean Wampler and Alex Payne, and The Scala Cook‐ book by Alvin Alexander (both from O’Reilly). 38 | Chapter 2: Introduction to Data Analysis with Scala and Spark CHAPTER 3 Recommending Music and the Audioscrobbler Data Set Sean Owen De gustibus non est disputandum.
There are two ways to create an RDD in Spark: • Using the SparkContext to create an RDD from an external data source, like a file in HDFS, a database table via JDBC, or a local collection of objects that we create in the Spark shell. • Performing a transformation on one or more existing RDDs, like filtering records, aggregating records by a common key, or joining multiple RDDs together. RDDs are a convenient way to describe the computations that we want to perform on our data as a sequence of small, independent steps.
Foreach(println) ... "id_1","id_2","cmp_fname_c1","cmp_fname_c2","cmp_lname_c1",... It looks like our isHeader method works correctly; the only result that was returned from applying it to the head array via the filter method was the header line itself. But of course, what we really want to do is get all of the rows in the data except the header rows. There are a few ways that we can do this in Scala. length ... length ... res: Int = 9 Anonymous functions in Scala are somewhat like Python’s lambda functions.
Advanced Analytics with Spark: Patterns for Learning from Data at Scale by Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills