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

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

During this useful ebook, 4 Cloudera information scientists current a suite of self-contained styles for appearing large-scale info research with Spark. The authors convey Spark, statistical tools, and real-world facts units jointly to coach you the way to procedure analytics difficulties by way of 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 resembling genomics, protection, and finance. when you've got an entry-level knowing of computing device studying and statistics, and also you application in Java, Python, or Scala, you'll locate those styles helpful for engaged on your individual information applications.

Patterns include:
- Recommending song and the Audioscrobbler info set
- Predicting wooded area disguise with selection 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 big apple urban Taxi journeys data
- Estimating monetary possibility via Monte Carlo simulation
- reading genomics facts and the BDG project
- examining neuroimaging information with PySpark and Thunder

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

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Set-Based Web Site Design A colleague from a company that does Web designs for many customers told us how she answers difficult usability questions: When we can't agree on how to structure the Web site, what we do is create two or three versions, with different paths and page layouts. We then do usability testing with several target users. It turns out that there is never one design that stands out above the others. Instead, we find that some features from each design are good, and some are rather poor.

Therefore, it is usually a good idea to work down a prioritized feature list from the top. In general, this strategy will accomplish the overall mission by the time the allocated resources are up. This approach to project management may seem to lead to unpredictable results, but quite the opposite is true. Once a track record of delivering working software is established, it is easy to project how much work will be done in each iteration as the project proceeds. By tracking the team velocity, you can forecast from past work how much work will probably be done in the future.

Negotiable Scope A good strategy for achieving convergence is to work on top priority items first, leaving the low priority items to fall off the to-do list. By delivering high priority features first, it is likely that you will deliver most of the business value long before the customer's wish list is completed. Here comes the tricky part. If you are working under the expectation that development is not complete until a fixed, detailed scope is achieved, then the system may indeed not converge.

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