Big Data Glossary by Pete Warden PDF

By Pete Warden

ISBN-10: 1449314597

ISBN-13: 9781449314590

To assist you navigate the big variety of new facts instruments on hand, this consultant describes 60 of the newest suggestions, from NoSQL databases and MapReduce methods to computing device studying and visualization instruments. Descriptions are in response to first-hand adventure with those instruments in a creation environment.

This convenient thesaurus additionally contains a bankruptcy of key phrases that support outline lots of those device categories:

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

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Extra resources for Big Data Glossary

Example text

Info CHAPTER 10 Acquisition Most of the interesting public data sources are poorly structured, full of noise, and hard to access. I probably spend more time turning messy source data into something usable than I do on the rest of the data analysis processes combined, so I’m very thankful that there are multiple tools emerging to help. Google Refine Google Refine is an update to the Freebase Gridworks tool for cleaning up large, messy spreadsheets. It has been designed to make it easy to correct the most common errors you’ll encounter in human-created datasets.

You still define a schema for your data and the interfaces you’ll use, but instead of being held separately within each program, the schema is transmitted alongside the data. That makes it possible to write code that can handle arbitrary data structures, rather than only the types that were known when the program was created. This flexibility does come at the cost of space and performance efficiency when encoding and decoding the information. Avro schemas are defined using JSON, which can feel a bit clunky compared to more domainspecific IDLs, though there is experimental support for a more user-friendly format known as Avro IDL.

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. It generates efficient code for a wide variety of languages, and under the hood offers a lot of choices for the underlying data format without affecting the application layer.

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Big Data Glossary by Pete Warden


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