The Age of Big Data
Kayvan Tirdad
Tirdad@Yorku.ca
Contents
1 2 3 Introduction: Explosion in Quantity of Data
Big Data Characteristics
Cost Problem (example) Importance of Big Data Usage Example in Big Data
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5 5
Contents
1 6 2 7 3 8 Some Challenges in Big Data
Other Aspects of Big Data
Implementation of Big Data Zeta-Byte Horizon Book Review
4 9
10 5
Introduction: Explosion in Quantity of Data 1946
Eniac X 6000000 =
2012
LHC 1 (40 TB/S) 640TB per Flight
Air Bus A380 - 1 billion line of code - each engine generate 10 TB every 30 min
Twitter Generate approximately 12 TB of data per day
New York Stock Exchange 1TB of data everyday storage capacity has doubled roughly every three years since the 1980s
Introduction: Explosion in Quantity of Data
Our Data-driven World
Science
Data bases from astronomy, genomics, environmental data, transportation data,
Humanities and Social Sciences
Scanned books, historical documents, social interactions data, new technology like GPS
Business & Commerce
Corporate sales, stock market transactions, census, airline traffic,
Entertainment
Internet images, Hollywood movies, MP3 files,
Medicine
MRI & CT scans, patient records,
Introduction: Explosion in Quantity of Data
Our Data-driven World - Fish and Oceans of Data
What we do with these amount of data?
Ignore
Big Data Characteristics 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
Big Data Vectors (3Vs)
"Big Data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization Gartner 2012
Big Data Characteristics
Big Data Vectors (3Vs)
- high-volume amount of data
- high-velocity Speed rate in collecting or acquiring or generating or processing of data - high-variety different data type such as audio, video, image data (mostly unstructured data)
Cost Problem (example)
Cost of processing 1 Petabyte of data with 1000 node ?
1 PB = 1015 B = 1 million gigabytes = 1 thousand terabytes - 9 hours for each node to process 500GB at rate of 15MB/S - 15*60*60*9 = 486000MB ~ 500 GB - 1000 * 9 * 0.34$ = 3060$ for single run
- 1 PB = 1000000 / 500 = 2000 * 9 = 18000 h /24 = 750 Day - The cost for 1000 cloud node each processing 1PB 2000 * 3060$ = 6,120,000$
Importance of Big Data
- Government In 2012, the Obama administration announced the Big Data Research and Development Initiative 84 different big data programs spread across six departments - Private Sector - Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes of data - Facebook handles 40 billion photos from its user base. - Falcon Credit Card Fraud Detection System protects 2.1 billion active accounts world-wide - Science - Large Synoptic Survey Telescope will generate 140 Terabyte of data every 5 days. - Large Hardon Colider 13 Petabyte data produced in 2010 - Medical computation like decoding human Genome - Social science revolution - New way of science (Microscope example)
Importance of Big Data
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Job
- The U.S. could face a shortage by 2018 of 140,000 to 190,000 people with
"deep analytical talent" and of 1.5 million people capable of analyzing data in ways that enable business decisions. (McKinsey & Co) - Big Data industry is worth more than $100 billion growing at almost 10% a year (roughly twice as fast as the software business)
Technology Player in this field
Oracle
Exadata
Microsoft
HDInsight Server
IBM
Netezza
Usage Example in Big Data
- Moneyball: The Art of Winning an Unfair Game
Oakland Athletics baseball team and its general manager Billy Beane - Oakland A's' front office took advantage of more analytical gauges of player performance to field a team that could compete successfully against richer competitors in MLB - Oakland approximately $41 million in salary, New York Yankees, $125 million in payroll that same season. Oakland is forced to find players undervalued by the market,
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- Moneyball had a huge impact in other teams in MLB And there is a moneyball movie!!!!!
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). - a contest to dine with Sarah Jessica Parker - Every single night, the team ran 66,000 computer simulations, Reddit!!! - Amazon web services
- data mining for individualized ad targeting
- Orca big-data app
- YouTube channel( 23,700 subscribers and 26 million page views) - Ace of Spades HQ
Usage Example in Big Data
Data Analysis prediction for US 2012 Election
Drew Linzer, June 2012 332 for Obama, 206 for Romney media continue reporting the race as very tight
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Nate Silvers, Five thirty Eight blog Predict Obama had a 86% chance of winning Predicted all 50 state correctly Sam Wang, the Princeton Election Consortium The probability of Obama's re-election at more than 98%
Some Challenges in Big Data
Big Data Integration is Multidisciplinary Less than 10% of Big Data world are genuinely relational Meaningful data integration in the real, messy, schema-less and complex Big Data world of database and semantic web using multidisciplinary and multi-technology methode The Billion Triple Challenge Web of data contain 31 billion RDf triples, that 446million of them are RDF links, 13 Billion government data, 6 Billion geographic data, 4.6 Billion Publication and Media data, 3 Billion life science data BTC 2011, Sindice 2011
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The Linked Open Data Ripper Mapping, Ranking, Visualization, Key Matching, Snappiness
Demonstrate the Value of Semantics: let data integration drive DBMS technology Large volumes of heterogeneous data, like link data and RDF
Other Aspects of Big Data
Six Provocations for Big Data
1- Automating Research Changes the Definition of Knowledge
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2- Claim to Objectively and Accuracy are Misleading
3- Bigger Data are not always Better data 4- Not all Data are equivalent 5- Just because it is accessible doesnt make it ethical 6- Limited access to big data creatrs new digital divides
Other Aspects of Big Data
Five Big Question about big Data:
1- What happens in a world of radical transparency, with data widely available? 2- If you could test all your decisions, how would that change the way you compete?
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3- How would your business change if you used big data for widespread, real time customization?
4- How can big data augment or even replace Management? 5-Could you create a new business model based on data?
Implementation of Big Data
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Platforms for Large-scale Data Analysis
Parallel DBMS technologies
Proposed in late eighties Matured over the last two decades Multi-billion dollar industry: Proprietary DBMS Engines intended as Data Warehousing solutions for very large enterprises
Map Reduce
pioneered by Google popularized by Yahoo! (Hadoop)
Implementation of Big Data
MapReduce
Overview:
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Parallel DBMS technologies
Popularly used for more than two decades
Research Projects: Gamma, Grace, Commercial: Multi-billion dollar industry but access to only a privileged few Relational Data Model Indexing Familiar SQL interface Advanced query optimization Well understood and studied
Data-parallel programming model An associated parallel and distributed implementation for commodity clusters Pioneered by Google Processes 20 PB of data per day Popularized by open-source Hadoop Used by Yahoo!, Facebook, Amazon, and the list is growing
Implementation of Big Data
MapReduce
Raw Input: <key, value>
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MAP
<K1, V1>
<K2,V2>
<K3,V3>
REDUCE
Implementation of Big Data
MapReduce Advantages
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Automatic Parallelization:
Depending on the size of RAW INPUT DATA instantiate multiple MAP tasks Similarly, depending upon the number of intermediate <key, value> partitions instantiate multiple REDUCE tasks
Run-time:
Completely transparent to the
programmer/analyst/user
Data partitioning Task scheduling Handling machine failures Managing inter-machine communication
Implementation of Big Data Map Reduce vs Parallel DBMS
Parallel DBMS Schema Support Indexing Declarative (SQL) MapReduce Not out of the box Not out of the box Imperative (C/C++, Java, ) Extensions through Pig and Hive
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Programming Model Optimizations (Compression, Query Optimization) Flexibility Fault Tolerance
Not out of the box Coarse grained techniques
Not out of the box
Zeta-Byte Horizon
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 zettabytes during 2012. This is 48% up from 2011
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x50 2012 2020
Wrap Up
Book Review
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The Fourth Paradigm Data-Intensive Scientific Discovery
Toney Hey, Stwart Tansley and Kristin Tolle Microsotf Press 2009
References
1.
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B. Brown, M. Chuiu and J. Manyika, Are you ready for the era of Big Data? McKinsey Quarterly, Oct 2011, McKinsey Global Institute 2. C. Bizer, P. Bonez, M. L. Bordie and O. Erling, The Meaningful Use of Big Data: Four Perspective Four Challenges SIGMOD Vol. 40, No. 4, December 2011 3. D. Boyd and K. Crawford, Six Provation for Big Data A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, September 2011, Oxford Internet Institute 4. D. Agrawal, S. Das and A. E. Abbadi, Big Data and Cloud Computing: Current State and Future Opportunities ETDB 2011, Uppsala, Sweden 5. D. Agrawal, S. Das and A. E. Abbadi, Big Data and Cloud Computing: New Wine or Just New Bottles? VLDB 2010, Vol. 3, No. 2 6. F. J. Alexander, A. Hoisie and A. Szalay, Big Data IEEE Computing in Science and Engineering journal 2011 7. O. Trelles, P Prins, M. Snir and R. C. Jansen, Big Data, but are we ready? Nature Reviews, Feb 2011 8. K. Bakhshi, Considerations for Big data: Architecture and approach Aerospace Conference, 2012 IEEE 8. S. Lohr, The Age of Big Data Thr New York times Publication, February 2012 10. M. Nielsen, Aguide to the day of big data, Nature, vol. 462, December 2009
Kayvan Tirdad
