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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
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<meta name="description" content="Course homepage for CS 489 Big Data Infrastructure (Winter 2017) at the University of Waterloo">
<meta name="author" content="Jimmy Lin">
<title>Big Data Infrastructure</title>
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<li class="active"><a href="index.html">Overview</a></li>
<li><a href="organization.html">Organization</a></li>
<li><a href="syllabus.html">Syllabus</a></li>
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<div style="float: right"/><img src="images/waterloo_logo.png"/></div>
<h1>Big Data Infrastructure<br/><small>CS 489/698 (Winter 2017)</small></h1>
</div>
<p>
<b>Time:</b> Tuesdays and Thursdays, 1:00-2:20pm<br/>
<b>Location:</b> AL 124<br/>
<b>Instructor:</b> <a href="https://cs.uwaterloo.ca/~jimmylin/">Jimmy Lin</a><br/>
<b>TAs:</b> Libo Gao, Kareem El Gebaly, Ripul Jain</br>
<b>Piazza:</b> <a href="http://piazza.com/uwaterloo.ca/winter2017/cs489698/home">course link</a> — use for general questions</br>
<b>Email:</b> [email protected] (will reach instructor and TAs) — use <i>only</i> for personal concerns<br/>
</p>
<div style="float: right; padding-left: 20px; padding-bottom: 20px"/><img src="images/stack.png"/></div>
<p>Over the past few years, we have seen the emergence of "big data":
disruptive technologies that have transformed commerce, science, and
many aspects of society. These developments are enabled by
infrastructure that allows us to distribute computations across
hundreds or even thousands of commodity servers. One important advance
that has made all this possible is the development of abstractions for
data-intensive computing that allow programmers to reason about
computations at a massive scale, hiding low-level details such as
synchronization, data movement, and fault tolerance.</p>
<p><b>What is this course about?</b> This course provides an
introduction to big data infrastructure for analytics. The focus is
algorithm design and "thinking at scale": we will cover data mining
and machine learning techniques as applied to text, graphs, and
relational data. Most of the course will be taught in a combination of
MapReduce and Spark, two representative dataflow abstractions for
large-scale data analysis, although we will introduce alternative
abstractions such as bulk-synchronous parallel and streaming models
as well.</p>
<p>One might break down the "big data" stack in the manner shown on
the right. At the bottom resides the execution infrastructure, which
is responsible for coordinating computations across a cluster
(examples include MapReduce and Spark). In the middle resides
analytics infrastructure, which implements data mining and machine
learning algorithms on top of the execution infrastructure (an example
would be MLlib in Spark). At the top are the tools data scientists use
to generate insights, built on top of the analytics
infrastructure. This course focuses on the middle part — by the
end of the course, you will be able to implement basic data mining and
machine learning algorithms that can operate at scale. Of course,
effective algorithm design requires understanding the execution
infrastructure (below) and what the algorithms are used for (above),
so we will cover the broader context as well.</p>
<p style="padding-top: 20px">
<a href="https://github.com/lintool/bigdata-2017w/" class="btn btn-primary btn-large">Fork me on Github!</a>
</p>
<p style="padding-bottom: 100px"/>
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