A non-technical introduction to Big Data that conveys the core concepts and ideas of big data without giving into the hype.
Thinking Big An Introduction to Big Data
About Me Shawn Hermans Data Engineer/Scientist Technology consultant Physics, math, data geek
About this Talk Non-technical introduction to Big Data Not focused on any technology or platform Focus on concepts
Should you believe the hype?
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
Big Data Criticism Garbage in, Garbage out Ignores the role of the scientific method Lots of questions dont require large amounts of data to get good stats Privacy issues
Big Data is just another way to think about data
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
Examples Occam's razor Mind maps Law of supply and demand Never get in a land war in Asia
All models are wrong, but some are useful
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
Data Mental Models Relational Linked Object Oriented Geospatial Temporal Semantic Event Based Data as Code Bayesian Unstructured
What is Big Data?
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
According to Me Big data is the Bazaar to traditional datas Cathedral
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
Big Data Differences Relational Normalization ACID SQL/Query Structured/Schema Big Data Denormalization BASE MapReduce/Other Loosely Structured
Integrating all available data is the promise of Big Data
Why should you care?
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
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 dont you collect? What data dont you archive?
Big Data Technology
Big Data Platforms Cloud AWS Google Microsoft Hadoop Cloudera MapR Hortonworks This isnt an all inclusive list, but a sample of the big players in the space.
Big Data Stack Batch Processing Data Collection SQL/Query Search Machine Learning Serialization Security Stream Processing File Storage Resource management Online NoSQL Data Pipeline
What about data science?
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?
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
Big Data has its limits
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)
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
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
The Fourth Quadrant and the Failure of Statistics
Soothsayer Simple HTTP/JSON API for training/classifying data Lots of built in classifier statistics https://github.com/shawnhermans/soothsayer