Big Data Glossary by Pete Warden

By Pete Warden

To assist you navigate the big variety of new info instruments to be had, this advisor describes 60 of the latest recommendations, from NoSQL databases and MapReduce techniques to computer studying and visualization instruments. Descriptions are in accordance with first-hand event with those instruments in a creation environment.

This convenient word list additionally encompasses a bankruptcy of key phrases 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 aid allotted computing on huge datasets
  • Storage—Technologies for storing facts in a allotted means
  • Servers—Ways to hire computing strength on distant machines
  • Processing—Tools for extracting beneficial details from huge datasets
  • Natural Language Processing—Methods for extracting details from human-created textual content
  • Machine Learning—Tools that instantly practice information analyses, in response to result of a one-off research
  • Visualization—Applications that current significant information graphically
  • Acquisition—Techniques for cleansing up messy public facts assets
  • Serialization—Methods to transform facts constitution or item country right into a storable structure

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It’s aimed at users who are comfortable with a spreadsheet interface rather than traditional developers, so it’s not possible to use it as part of a custom solution, but does offer ideas on how to make your data processing application accessible to those sort of ordinary users. info Tinkerpop A group of developers working on open source graph software, Tinkerpop has produced an integrated suite of tools. A bit like the LAMP stack for graph processing, they’re designing a set of services that work well together to perform common operations like interfacing to specialized graph databases, writing traversal queries, and exposing the whole system as a REST-based server.

EC2 for poets Google App Engine With Google’s App Engine service, you write your web-serving code in either Java, a JVM language, or Python, and it takes care of running the application in a scalable way so that it can cope with large numbers of simultaneous requests. Unlike with EC2 or traditional web hosting, you have very limited control over the environment your code is running in. This makes it easy to distribute across a lot of machines to handle heavy loads, since only your code needs to be transferred, but it does make it tough to run anything that needs flexible access to the underlying system.

As a user, you select elements on an example page that contain the data you’re interested in, and the tool then uses the patterns you’ve defined to pull out information from other pages on a site with a similar structure. For example, you might want to extract product names and prices from a shopping site. With the tool, you could find a single product page, select the product name and price, and then the same elements would be pulled for every other page it crawled from the site. info fact that most web pages are generated by combining templates with information retrieved from a database, and so have a very consistent structure.

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