Background: I prepared this slide deck for a couple of “Big Data 101” guest lectures I did in February 2013 at New York University’s Stern School of Business and at The New School. They’re intended for a college level, non technical audience, as a first exposure to Big Data and related concepts. I have re-used a number of stats, graphics, cartoons and other materials freely available on the internet. Thanks to the authors of those materials.
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Big Data, Big Deal? (A Big Data 101 presentation)
1. Big data, big deal?
February 2013
Matt Turck
Twitter: @mattturck
Blog: http://mattturck.com
2. Background: I prepared this slide deck for a couple of
“Big Data 101” guest lectures I did in February 2012 at
New York University’s Stern School of Business and at
The New School. They’re intended for a college
level, non technical audience, as a first exposure to Big
Data and related concepts. I have re-used a number of
stats, graphics, cartoons and other materials freely
available on the internet. Thanks to the authors of those
materials.
13. Big data is data that exceeds the
processing capacity of conventional
database systems. The data is too
big, moves too fast, or doesn’t fit the
strictures of your database
architectures. To gain value from this
data, you must choose an alternative
way to process it.
Edd Dumbill, O’Reilly
15. Big Data Landscape
Infrastructure Analytics Applications
NoSQL Databases Hadoop Related Analytics Solutions Data Visualization Ad Optimization
Publisher Marketing
NewSQL Databases
Statistical Computing Tools
Social Media
MPP Databases Management / Cluster Services
Industry Applications
Monitoring
Sentiment Analysis Analytics Services
Security
Application Service Providers
Location / People /
Big Data Search
Events
Storage
IT Analytics Data Sources
Crowdsourcing
Data Data Sources
Collection / Real- Crowdsourced SMB Analytics Marketplaces
Transport Time Analytics
Cross Infrastructure / Analytics Personal Data
Open Source Projects
Framework Query / Data Data Access Coordination / Real - Statistical Machine Cloud
Flow Workflow Time Tools Learning Deployment
Matt Turck (@mattturck) and Shivon Zilis (@shivonz)
16. A new breed of people:
Data scientists
engineering
math
nerds
nerds nerds
nerds
comp sci
hacking
awesome nerds
Credit: Hilary Mason, Bitly
17. Sexy nerds?
“Data Scientist:
The Sexiest Job of the 21st Century”
October 2012
37. Thanks!
Learn more:
NYC Data Business Meetup
meetup.com/NYC-Data-Business-Meetup/
Notes de l'éditeur
This is going to be a talk for people who love the internet.
The true story of bitly, engineering, data science, loveHow to do data science at scaleBuilding teams and keeping people happyClever tricks
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Asking questions.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.