Stanford Seminar - Big Data Is -At Least- Four Different Problems

Overview

This course covers the meaning of Big Data, the challenges posed by big volume, velocity, and variety of data, and the tools and techniques used in data science and complex analytics. The course discusses traditional solutions like ETL, the future of Hadoop, Spark, R, and Map-Reduce, as well as the importance of data integration at scale. The intended audience for this course is individuals interested in understanding the complexities of Big Data and learning about the various approaches and technologies used in handling large datasets.

Syllabus

Introduction.
The Meaning of Big Data - 3 V's.
Big Volume - Little Analytics.
The Big Disruption.
Data Science Template.
Complex Analytics on Array Data - An Accessible Example.
Array Answer.
st option).
Map-Reduce.
The Future of Hadoop.
nd option -- 2015).
rd option).
th option).
The Future of Complex Analytics, Spark, R, and .....
Big Velocity - 2nd Approach.
In My Opinion.....
Possible Storm Clouds.
Big Variety.
Traditional Solution -- ETL.
And there is NO Global Data Model.
Why Integrate Silos?.
Why is Data Integration Hard?.
Data Integration (Curation) AT SCALE is a VERY Big Deal.
A Bunch of Startups With New Ideas.
To Achieve Scalability.....
Data Lakes.
Take away.