By Russell Jurney
Mining tremendous info calls for a deep funding in humans and time. how are you going to ensure you're construction the best versions? With this hands-on booklet, you'll study a versatile toolset and technique for construction potent analytics purposes with Hadoop.
Using light-weight instruments similar to Python, Apache Pig, and the D3.js library, your workforce will create an agile atmosphere for exploring facts, beginning with an instance program to mine your personal electronic mail inboxes. You'll research an iterative strategy that allows you to quick switch the type of research you're doing, looking on what the information is telling you. All instance code during this booklet is out there as operating Heroku apps.
Create analytics functions through the use of the agile large facts improvement methodology
Build price out of your facts in a chain of agile sprints, utilizing the data-value stack
Gain perception through the use of numerous information buildings to extract a number of positive factors from a unmarried dataset
Visualize facts with charts, and disclose diversified facets via interactive reports
Use ancient facts to foretell the longer term, and translate predictions into action
Get suggestions from clients after every one dash to maintain your venture on the right track
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Additional resources for Agile Data Science: Building Data Analytics Applications with Hadoop
Despite the addition of MIME for multimedia attachments, email is still primarily text. Subject: Re: Lawn Content-Type: text/plain; charset=ISO-8859-1 Data Perspectives | 31 Dad, get a sack of Rye grass seed and plant it over there now. It will build up a nice turf over the winter, then die off when it warms up. Making for good topsoil you can plant regular grass in. Will keep the weeds from taking over. com We might analyze the body of the email by counting its word frequency. Once we remove noncoding common stopwords (like of and it), this looks like Figure 2-10.
Com/elasticsearch/ elasticsearch#getting-started. org/download/. 2 mkdir plugins bin/elasticsearch -f That’s it! Our local search engine is up and running! 52 | Chapter 3: Agile Tools ElasticSearch and Pig with Wonderdog Infochimps’ Wonderdog provides integration between Hadoop, Pig, and ElasticSearch. With Wonderdog, we can load and store data from Pig to and from our search engine. This is extremely powerful, because it lets us plug a search engine into the end of our data pipelines. com/infochimps-labs/wonderdog.
Conclusion As we’ve seen, viewing semistructured data according to different algorithms, struc‐ tures, and perspectives informs feature development more than normalizing and view‐ ing it in structured tables does. We’ll be using the perspectives defined in this chapter to create features throughout the book, as we climb the data-value pyramid. In the next chapter, you’ll learn how to specify schemas in our analytic stores using Apache Pig directly. Data Perspectives | 35 CHAPTER 3 Agile Tools This chapter will briefly introduce our software stack.
Agile Data Science: Building Data Analytics Applications with Hadoop by Russell Jurney