By Russell Jurney
Mining great facts calls for a deep funding in humans and time. how are you going to determine you're development definitely the right versions? With this hands-on ebook, you'll examine a versatile toolset and technique for construction potent analytics functions with Hadoop.
Using light-weight instruments equivalent to Python, Apache Pig, and the D3.js library, your crew will create an agile atmosphere for exploring information, beginning with an instance software to mine your personal e mail inboxes. You'll examine an iterative procedure that allows you to speedy switch the type of research you're doing, looking on what the knowledge is telling you. All instance code during this publication is accessible as operating Heroku apps.
Create analytics purposes through the use of the agile colossal info improvement methodology
Build price out of your information in a chain of agile sprints, utilizing the data-value stack
Gain perception through the use of numerous information constructions to extract a number of gains from a unmarried dataset
Visualize information with charts, and reveal varied points via interactive reports
Use ancient information 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
Read Online or Download Agile Data Science: Building Data Analytics Applications with Hadoop PDF
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Additional resources for Agile Data Science: Building Data Analytics Applications with Hadoop
When our tools are too complex, we start to focus on the tools themselves, and not on our data, our users, and new applications to help them. 37 An effective stack enables collaboration by teams that include diverse sets of skills such as design and application development, statistics, machine learning, and distributed systems. The stack outlined in this book is not definitive. It has been selected as an example of the kind of end-to-end setup you should expect as a developer or should aim for as a platform engineer in order to rapid‐ ly and effectively build analytics applications.
We’ll be using S3 in place of the Hadoop Distributed FileSystem (HDFS) for this purpose. HDFS sets the standard for bulk storage, and without it, big data would not exist. There would be no cheap place to store vast amounts of data where it can 38 | Chapter 3: Agile Tools be accessed with high I/O throughput for the kind of processing we do in Agile Big Data. • Distributed document stores are multinode stores using document format. In Agile Big Data, we use them to publish data for consumption by web applications and other services.
Avro allows us to access our data in a common format across languages and tools. Figure 3-2. com/p/snappy/. Using a package manager to do so is recommended. com/andrix/python-snappy. With python-snappy installed, Avro for Python should install without problems. py install To install using pip or easy_install: pip install avro easy_install avro Testing Try writing and reading a simple schema to verify that our data works (see Example 3-1): [bash]$ python Example 3-1. avro Now verify that we can read records back (Example 3-2).