Big Data Glossary by Pete Warden

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By Pete Warden

To assist you navigate the massive variety of new facts instruments on hand, this consultant describes 60 of the latest recommendations, from NoSQL databases and MapReduce ways to laptop studying and visualization instruments. Descriptions are in response to first-hand event with those instruments in a creation environment.

This convenient word list additionally encompasses a bankruptcy of keywords that support outline a lot of those device categories:

  • NoSQL Databases—Document-oriented databases utilizing a key/value interface instead of SQL
  • MapReduce—Tools that help disbursed computing on huge datasets
  • Storage—Technologies for storing information in a dispensed manner
  • Servers—Ways to hire computing energy on distant machines
  • Processing—Tools for extracting helpful details from huge datasets
  • Natural Language Processing—Methods for extracting details from human-created textual content
  • Machine Learning—Tools that immediately practice facts analyses, in response to result of a one-off research
  • Visualization—Applications that current significant information graphically
  • Acquisition—Techniques for cleansing up messy public information assets
  • Serialization—Methods to transform info constitution or item nation right into a storable structure

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It’s still fundamentally designed around the needs of frontend web applications, though, so most data processing problems aren’t a good fit for its approach. • Getting started with Elastic Beanstalk Heroku Heroku hosts Ruby web applications, offering a simple deployment process, a lot of free and paid plug-ins, and easy scalability. To ensure that your code can be quickly deployed across a large number of machines, there are some restrictions on things like access to the underlying filesystem, but in general the environment is more flexible than App Engine.

A lot of parsing time can be saved during loading and saving by storing integers and doubles in their native binary representations rather than as text strings. info native support for types that have no equivalent in JSON, like blobs of raw binary information and dates. Thrift With Thrift, you predefine both the structure of your data objects and the interfaces you’ll be using to interact with them. The system then generates code to serialize and deserialize the data and stub functions that implement the entry points to your interfaces.

What I really like, though, is the way that most of the scripts are published on the site, so new users have a lot of existing examples to start with, and as websites change their structures, popular older scrapers can be updated by the community. info CHAPTER 11 Serialization As you work on turning your data into something useful, it will have to pass between various systems and probably be stored in files at various points. These operations all require some kind of serialization, especially since different stages of your processing are likely to require different languages and APIs.

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