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大数据原理-bigdata-fundamentals

大数据原理

fundamentals

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大数据原理-bigdata-fundamentals，大数据原理-bigdata-fundamentals

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10-1

http://www.cse.wustl.edu/~jain/cse570-13/

Washington University in St. Louis

Big Data

Fundamentals

Raj Jain

Washington University in Saint Louis

Saint Louis, MO 63130

Jain@cse.wustl.edu

These slides and audio/video recordings of this class lecture are at:

http://www.cse.wustl.edu/~jain/cse570-13/

10-2

http://www.cse.wustl.edu/~jain/cse570-13/

Washington University in St. Louis

Overview

Why Big Data?

Terminology

Key Technologies: Google File System, MapReduce,

Hadoop

Hadoop and other database tools

Types of Databases

Ref: J. Hurwitz, et al., “Big Data for Dummies,”

Wiley, 2013, ISBN:978-1-118-50422-2

10-3

http://www.cse.wustl.edu/~jain/cse570-13/

Washington University in St. Louis

Big Data



Data is measured by 3V's:



Volume: TB



Velocity: TB/sec. Speed of creation or change



Variety: Type (Text, audio, video, images, geospatial, ...)



Increasing processing power, storage capacity, and networking

have caused data to grow in all 3 dimensions.



Volume, Location, Velocity, Churn, Variety,

Veracity (accuracy, correctness, applicability)



Examples: social network data, sensor networks,

Internet Search, Genomics, astronomy, …

10-4

http://www.cse.wustl.edu/~jain/cse570-13/

Washington University in St. Louis

Why Big Data Now?

Low cost storage to store data that was discarded earlier

Powerful multi-core processors

Low latency possible by distributed computing: Compute

clusters and grids connected via high-speed networks

Virtualization  Partition, Aggregate, isolate resources in any

size and dynamically change it  Minimize latency for any

scale

Affordable storage and computing with minimal man power

via clouds

 Possible because of advances in Networking

10-5

http://www.cse.wustl.edu/~jain/cse570-13/

Washington University in St. Louis

Why Big Data Now? (Cont)

Better understanding of task distribution (MapReduce),

computing architecture (Hadoop),

Advanced analytical techniques (Machine learning)

Managed Big Data Platforms: Cloud service providers, such

as Amazon Web Services provide Elastic MapReduce, Simple

Storage Service (S3) and HBase –

column oriented database.

Google’

BigQuery

and Prediction API.

Open-source software: OpenStack, PostGresSQL

10.

March 12, 2012: Obama announced $200M for Big Data

research. Distributed via NSF, NIH, DOE, DoD, DARPA, and

USGS (Geological Survey)

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