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Thinking Big An Introduction to Big Data

Author: shawn-hermans

Post on 11-Aug-2014



Data & Analytics

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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)
  • Whats next?
  • 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: Blog: (In Progress) Slideshare: Quora:
  • Backup Slides
  • The Fourth Quadrant and the Failure of Statistics
  • Soothsayer Simple HTTP/JSON API for training/classifying data Lots of built in classifier statistics