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PRATIBHA : TECHNICAL PAPER PRESENTATION

Deepak Kumar Prakriti 3rd year, EEE 3rd year, CSE PCE PCE

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WHAT IS BIG DATA?

AVAILABILITY OF DATA

STRUCTURED & UNSTRUCTURED

INDUCTIVE STATISTICS

CONCURRENCY CONTROL

COST EFFECTIVE

How big is the Big Data?

What is big today maybe not big tomorrow

- Any data that can challenge our current technology in some manner can consider as Big Data - Volume- Communication- Speed of Generating- Meaningful Analysis

An example of big data might be petabytes (1,024 terabytes) or exabytes (1,024 petabytes) of data consisting of billions to trillions of records of millions of people -- all from different sources

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BIG DATA VECTORS (3VS)

HIGH VOLUME

HIGH VELOCITY

HIGH VARIETY

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ARCHITECTURE

CATEGORIES OF BIG DATA USE CASE

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MAPREDUCE

Raw Input: <key, value>Raw Input: <key, value>

MAPMAP

<K2,V2><K2,V2><K1, V1><K1, V1> <K3,V3><K3,V3>

REDUCE REDUCE

Zeta-Byte Horizon

2012 2020

x50

As of 2009, the entire World Wide Web was estimated to contain close to 500 exabytes. This is a half zettabyte the total amount of global data is expected to grow to 2.7 zettabyte during 2012. This is 48% up from 2011

BIG DATA TECHNOLOGIES

BIG TABLE

BUSINESS INTELLIGENCE

CLOUD COMPUTING

DATA MART

DATA WAREHOUSE

DYNAMO

ETL

GOOGLE FILE SYSTEM

METADATA

DISTRIBUTED SYSTEM

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INSTANCES OF APPLICATION OF BIG DATA

GOVERNMENT

PRIVATE SECTOR

SCIENCE

Usage Example of Big Data(US 2012 Election)

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predictive modeling mybarackobama.com drive traffic to other campaign sites Facebook page (33 million "likes")YouTube channel (240,000 subscribers and 246 million page views).Every single night, the team ran 66,000 computer simulations, Read it.Amazon web services

data mining for individualized ad targetingOrca big-data appYouTube channel( 23,700 subscribers and 26 million page views)

Ace of Spades HQ

BRANDS USING BIG DATA

ORACLE EXADATA

MICROSOFT HDINSIGHT SERVER

IBM NETEZZA

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WHAT IT ENSURES?

CREATING TRANSPARENCY

ENABLING EXPERIMENTATION TO DISCOVER NEEDS, EXPOSE VARIABILITY, AND IMPROVE PERFORMANCE

SEGMENTING POPULATIONS TO CUSTOMIZE ACTIONS

REPLACING/SUPPORTING HUMAN DECISION MAKING WITH AUTOMATED ALGORITHMS

INNOVATING NEW BUSINESS MODELS, PRODUCTS, AND SERVICES

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CHALLENGES

CAPTURE MATUARITY IF TECHNOLOGY STORAGE SHARING TRANSFER VISUALIZATION

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COMPARISON

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TRANSFORMATIONS IT MAY BRING ABOUT

MEDICINE

SECURITY

URBAN PLANNING

CONSUMER PRODUCTS

ELECTIONS

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CRITIQUES

EROSION OF PRIVACY

PRONE TO ERRORS

LESS TIME TO CLEAN DATA

PIVOT –POINT MOMENT FOR MARKETTING & SALES

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SECURITY LOOP HOLES

VULNARABLE TO HACKING PERSONAL DATABASE AT RISK

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Proposed Solutions

ENCRYPTION AUTHORIZED PERMISSION

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SYNOPSIS

NEW EVOLVING ECOSYSTEM.

LINK BETWEEN IT AND PRODUCTIVITY

COMPETATIVE WEAPON

TACKLES COST AND DATA INTEGRATION

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Bibliography

en.wiki.com Insight & Publication IBM website

Webopedia McKinsey Quarterly, Oct 2011, McKinsey Global

Institute