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<h1 id="big-data">Big Data</h1>
<h2 id="prerequisites">Prerequisites</h2>
<ul>
<li>Basics of Linux File systems.</li>
<li>Basic understanding of System Design.</li>
</ul>
<h2 id="what-to-expect-from-this-course">What to expect from this course</h2>
<p>This course covers the basics of Big Data and how it has evolved to become what it is today. We will take a look at a few realistic scenarios where Big Data would be a perfect fit. An interesting assignment on designing a Big Data system is followed by understanding the architecture of Hadoop and the tooling around it. </p>
<h2 id="what-is-not-covered-under-this-course">What is not covered under this course</h2>
<p>Writing programs to draw analytics from data.</p>
<h2 id="course-contents">Course Contents</h2>
<ol>
<li><a href="https://linkedin.github.io/school-of-sre/big_data/intro/#overview-of-big-data">Overview of Big Data</a></li>
<li><a href="https://linkedin.github.io/school-of-sre/big_data/intro/#usage-of-big-data-techniques">Usage of Big Data techniques</a></li>
<li><a href="https://linkedin.github.io/school-of-sre/big_data/evolution/">Evolution of Hadoop</a></li>
<li><a href="https://linkedin.github.io/school-of-sre/big_data/evolution/#architecture-of-hadoop">Architecture of hadoop</a><ol>
<li>HDFS</li>
<li>Yarn</li>
</ol>
</li>
<li><a href="https://linkedin.github.io/school-of-sre/big_data/evolution/#mapreduce-framework">MapReduce framework</a></li>
<li><a href="https://linkedin.github.io/school-of-sre/big_data/evolution/#other-tooling-around-hadoop">Other tooling around hadoop</a><ol>
<li>Hive</li>
<li>Pig</li>
<li>Spark</li>
<li>Presto</li>
</ol>
</li>
<li><a href="https://linkedin.github.io/school-of-sre/big_data/evolution/#data-serialisation-and-storage">Data Serialisation and storage</a></li>
</ol>
<h1 id="overview-of-big-data">Overview of Big Data</h1>
<ol>
<li>Big Data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, techniques and frameworks.</li>
<li>Big Data could consist of<ol>
<li>Structured data</li>
<li>Unstructured data</li>
<li>Semi-structured data</li>
</ol>
</li>
<li>Characteristics of Big Data:<ol>
<li>Volume</li>
<li>Variety</li>
<li>Velocity</li>
<li>Variability</li>
</ol>
</li>
<li>Examples of Big Data generation include stock exchanges, social media sites, jet engines, etc.</li>
</ol>
<h1 id="usage-of-big-data-techniques">Usage of Big Data Techniques</h1>
<ol>
<li>Take the example of the traffic lights problem.<ol>
<li>There are more than 300,000 traffic lights in the US as of 2018.</li>
<li>Let us assume that we placed a device on each of them to collect metrics and send it to a central metrics collection system.</li>
<li>If each of the IOT devices sends 10 events per minute, we have 300000x10x60x24 = 432x10^7 events per day.</li>
<li>How would you go about processing that and telling me how many of the signals were “green” at 10:45 am on a particular day?</li>
</ol>
</li>
<li>Consider the next example on Unified Payments Interface (UPI) transactions:<ol>
<li>We had about 1.15 billion UPI transactions in the month of October, 2019 in India.</li>
<li>If we try to extrapolate this data to about a year and try to find out some common payments that were happening through a particular UPI ID, how do you suggest we go about that?</li>
</ol>
</li>
</ol>
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