By Russell Jurney

ISBN-10: 1449326927

ISBN-13: 9781449326920

Mining enormous information calls for a deep funding in humans and time. how will you ascertain you're development definitely the right versions? With this hands-on e-book, you'll research a versatile toolset and method for development potent analytics purposes with Hadoop.

Using light-weight instruments resembling Python, Apache Pig, and the D3.js library, your crew will create an agile atmosphere for exploring information, beginning with an instance program to mine your personal e-mail inboxes. You'll study an iterative process that allows you to speedy switch the type of research you're doing, counting on what the knowledge is telling you. All instance code during this booklet is offered as operating Heroku apps.

Create analytics functions by utilizing the agile giant info improvement methodology
Build worth out of your facts in a sequence of agile sprints, utilizing the data-value stack
Gain perception by utilizing a number of information buildings to extract a number of positive aspects from a unmarried dataset
Visualize info with charts, and reveal assorted facets via interactive reports
Use ancient information to foretell the long run, and translate predictions into action
Get suggestions from clients after each one dash to maintain your venture on the right track

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Extra resources for Agile Data Science: Building Data Analytics Applications with Hadoop

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We don’t know the schema until we’re ready to store, and when we do, there is little use in specifying it externally to our Pig Publishing Data with MongoDB | 51 code. This is but one part of the stack, but this property helps us work rapidly and enables agility. Speculative Execution and Hadoop Integration We haven’t set any indexes in MongoDB, so it is possible for copies of entries to be written. To avoid this, we must turn off speculative execution in our Pig script. execution false Hadoop uses a feature called speculative execution to fight skew, the bane of concurrent systems.

Run Pig in local mode (instead of Hadoop mode) via -x local and put logfiles in /tmp via -l /tmp to keep from cluttering your workspace. pig, flows our data through filters to clean it, and then projects, groups, and counts it to determine sent counts (Example 3-5). Example 3-5. txt /* Load the emails in avro format (edit the path to match where you saved them) using the AvroStorage UDF from Piggybank */ messages = LOAD '/me/Data/test_mbox' USING AvroStorage(); /* Filter nulls, they won't help */ messages = FILTER messages BY (from IS NOT NULL) AND (tos IS NOT NULL); /* Emails can be 'to' more than one person.

Each line of a Pig Latin script specifies some transformation on the data, and these transformations are executed stepwise as data flows through the script. Data Processing with Pig | 47 Figure 3-6. Dataflow through a Pig Latin script 48 | Chapter 3: Agile Tools Publishing Data with MongoDB To feed our data to a web application, we need to publish it in some kind of database. While many choices are appropriate, we’ll use MongoDB for its ease of use, document orientation, and excellent Hadoop and Pig integration (Figure 3-7).

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Agile Data Science: Building Data Analytics Applications with Hadoop by Russell Jurney

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