A non-technical introduction to Big Data that conveys the core concepts and ideas of big data without giving into the hype.

1. Thinking Big
An Introduction to Big Data

2. About Me
Shawn Hermans
● Data Engineer/Scientist
● Technology consultant
● Physics, math, data geek

3. About this Talk
● Non-technical introduction to Big Data
● Not focused on any technology or platform
● Focus on concepts

4. Should you believe the hype?

5. ● No need for scientific method
● Predict disease outbreaks before the CDC
● Cure cancer
● Innovating healthcare
● Solve world hunger
● Bring about world peace
Big Data Promises

7. Big Data Criticism
● Garbage in, Garbage out
● Ignores the role of the scientific method
● Lots of questions don’t require large
amounts of data to get good stats
● Privacy issues

8. Big Data is just another way to think about data

9. Mental Models
“A mental model is simply a representation of
an external reality inside your head. Mental
models are concerned with understanding
knowledge about the world.”
- Farnam Street Blog

10. Examples
● Occam's razor
● Mind maps
● Law of supply and demand
● Never get in a land war in Asia

11. All models are wrong, but some are useful

12. Relational Resistance
Resistance to big data concepts, technologies,
and techniques because of belief that the
relational model is the only way to think about
data.
See also: Theory induced blindness

14. Data Mental Models
● Relational
● Linked
● Object Oriented
● Geospatial
● Temporal
● Semantic
● Event Based
● Data as Code
● Bayesian
● Unstructured

15. What is Big Data?

16. “Big data is 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.”
According to Gartner

17. According to Me
Big data is the Bazaar to
traditional data’s Cathedral

18. Cathedral and Bazaar
Traditional Data
● Clean
● Top down
● Carefully collected
● Scales vertically
● One true way
Big Data
● Disorderly
● Bottom up
● Randomly collected
● Scales horizontally
● More than one way

19. Big Data Differences
Relational
● Normalization
● ACID
● SQL/Query
● Structured/Schema
Big Data
● Denormalization
● BASE
● MapReduce/Other
● Loosely Structured

20. Integrating all available data is the promise of Big Data

21. Why should you care?

23. Information as an Asset
● Target specific customer's needs rather than
broad segments
● Just-in-time inventory management
● Evaluating demand for product
● Predict and track traffic patterns

24. Big Data and You
● What information do you have, that no one
else has?
● Can you easily integrate your data or is it
locked in silos?
● What data don’t you collect?
● What data don’t you archive?

25. Big Data Technology

26. Big Data Platforms
Cloud
● AWS
● Google
● Microsoft
Hadoop
● Cloudera
● MapR
● Hortonworks
This isn’t an all inclusive list, but a sample of
the big players in the space.

27. Big Data Stack
● Batch Processing
● Data Collection
● SQL/Query
● Search
● Machine Learning
● Serialization
● Security
● Stream Processing
● File Storage
● Resource
management
● Online NoSQL
● Data Pipeline

29. What about data science?

30. ● Data science is statistics on a Mac
● A data scientist is a statistician who lives in
San Francisco
● Person who is better at statistics than any
software engineer and better at software
engineering than any statistician.
What IS Data Science?

32. The need for Data Science
● There is a LOT of data
● Too much data for people to look at it all
● Probabilistic models help extract signal from
the noise
● Need to automate the analysis and
exploitation of data

33. Big Data has its limits

34. Black Swans and Big Data
● There are fundamental limits to prediction
● Hard to predict rare events where no prior
data exists (i.e. Black Swans)
● Complex systems often have feedback loops
(e.g. stock market)

35. What’s next?

36. Business
● Identify some
unresolved questions
● Figure out what data
could answer those
questions
● Pick the easiest and test
out your hypothesis
Getting Started
Technology
● Pick a technology you
know or want to learn
● Pick a platform
● Pick a data set and
identify some basic
problems to solve

37. My Info
Twitter: @shawnhermans
Github: github.com/shawnhermans
Blog: http://shawnhermans.github.io/ (In Progress)
Slideshare: www.slideshare.net/shawnhermans/
Quora: http://www.quora.com/Shawn-Hermans

38. Backup Slides

40. The Fourth Quadrant and the Failure of Statistics

41. Soothsayer
● Simple HTTP/JSON
API for
training/classifying
data
● Lots of built in
classifier statistics
https://github.com/shawnhermans/soothsayer