PrithivirajDamodaran · March 25, 2018 19:35
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 </head>
 <body>
 <div class="corner-ribbon top-left red shadow">Under Review</div>    
 <center> <h1> Open Data Engineering </h1> </center>
 <center> <h2> An endeavour to train industry-ready data engineers - <i> by Prithiviraj Damodaran </i> </h2> </center>
 <center> Except where otherwise noted, this website is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/3.0/deed.en_US">Creative Commons Attribution 3.0 Unported License</a>. </center>
 <h2> <font color =" blue"> Table of contents </font> </h2>

 <div class="ui-menu">
 <ul>
    
 <li class="main"> <b> Part I - Introduction: The Big data story  </b> </li>
 <li class="sub"> A brief timeline of events </li>
 <li class="sub">   Distributed systems: Basics </li>
 <li class="main"> <b> Part II - Hadoop 1.0  </b> </li>
 <li class="sub">  Doug cutting’s brain child</li>
 <li class="sub"> Hands-on: MR and HDFS Basics </li>
 <li class="main"> <b> Part III - Hadoop 2.0  </b> </li>
 <li class="sub"> YARN and the current state of Hadoop </li>
 <li class="sub"> Hands-on: MR and HDFS Advanced  </li> 
 <li class="sub">   Distributed systems: A Deep dive </li>
 <li class="main"> <b> Part IV - Data Integration </b></li>
 <li class="sub"> Principles of Data Integration</li>
 <li class="sub"> Fundamentals of Kafka SDP</li>
 <li class="sub"> Data Integration with Kafka SDP - Hands-on </li>
 <li class="sub"> Case study: Data integration</li>
 <li class="main"><b>  Part VI - Data storage and NoSQL </b> </li>
 <li class="sub"> NoSQL and storage fundamentals </li>
 <li class="sub">  NoSQL hands-on</li>
 <li class="sub">  Case study: Modelling for real-time data</li>
 <li class="main">  <b> Part V - Data Processing </b> </li>
 <li class="sub">  Principles of Data processing Spark basics</li>
 <li class="sub">  Spark basics hands-on </li>
 <li class="sub">  Advanced Spark </li>
 <li class="sub">  Advanced Spark: hands-on</li>
 <li class="sub">    Spark Streaming</li>
 <li class="sub">    Case study: Streaming and Batch processing</li>
 <li class="main"> <b>     Part VII - Data Access and querying </b> </li>
 <li class="sub">   Enabling analytics </li>
 <li class="sub"> Hive-on-spark </li>
 <li class="sub">   Apache Drill </li>
 <li class="sub">   Case study: BI and Viz </li>
 <li class="main">   <b> Part VIII - Capstone project </b> </li>
   </ul>
 </div>
    
    
 <h2> <font color =" blue"> Part 1 </font> </h2>
 <div>
    
 <ol>
    <li> <b> What led to the rise of “Big data” : Converging technology trends </b></li>
 <ul>        
 <li class="sub"> Web 2.0 </li>
 <li class="sub"> Commoditization of Hardware</li>
 <li class="sub"> Mobile revolution </li>
 <li class="sub"> Open-source movement </li>
 <li class="sub"> IoT </li>
 </ul>

    <li> <b> How Google set the “Big data” ball rolling ? </b> </li>
    <ul>
 <li class="sub"> Why Google wanted to overoil their web crawlers with new processing techniques </li>
 <li class="sub"> Enter  MapReduce : How it contrasts with older data-parallel paradigms like MPPs and why is it a giant leap in parallel
 processing </li>
 <li class="sub">  2005, Release of  Google M/R paper  first milestone and big data and the chain of events that is set on motion </li>
 <li class="sub"> Project Nutch : 2nd milestone,  Doug cutting and Mike  in yahoo built the first implementation of M/R and GFS and
 open-sourced it as Hadoop </li>
 </ul>
 <li> <b> The idea of  quasi-structured and unstructured data  vs  structured   data   </b> </li>
 <li> <b> Change in status quo: Why distributed system and parallel processing are the new normal ? </b> </li> 
 <li> <b> Open source wave - complex distributed data frameworks are accessible to all.</b> </li>
 <ul>
 <li class="sub"> The other frameworks that shaped the big data world as we know today. </li>
 <li class="sub"> Companies that made big data technology accessible </li>
 </ul>
 </ol>
 </div>   
 </body>
 </html>
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	@import url('https://fonts.googleapis.com/css?family=Noto+Sans:400,700');


	body{
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	position: absolute;
	top: 25px;
	left: -50px;
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	line-height: 50px;
	letter-spacing: 1px;
	color: #f0f0f0;
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	</style>
	</head>
	<body>
	<div class="corner-ribbon top-left red shadow">Under Review</div>
	<center> <h1> Open Data Engineering </h1> </center>
	<center> <h2> An endeavour to train industry-ready data engineers - <i> by Prithiviraj Damodaran </i> </h2> </center>
	<center> Except where otherwise noted, this website is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/3.0/deed.en_US">Creative Commons Attribution 3.0 Unported License</a>. </center>
	<h2> <font color =" blue"> Table of contents </font> </h2>

	<div class="ui-menu">
	<ul>

	<li class="main"> <b> Part I - Introduction: The Big data story </b> </li>
	<li class="sub"> A brief timeline of events </li>
	<li class="sub"> Distributed systems: Basics </li>
	<li class="main"> <b> Part II - Hadoop 1.0 </b> </li>
	<li class="sub"> Doug cutting’s brain child</li>
	<li class="sub"> Hands-on: MR and HDFS Basics </li>
	<li class="main"> <b> Part III - Hadoop 2.0 </b> </li>
	<li class="sub"> YARN and the current state of Hadoop </li>
	<li class="sub"> Hands-on: MR and HDFS Advanced </li>
	<li class="sub"> Distributed systems: A Deep dive </li>
	<li class="main"> <b> Part IV - Data Integration </b></li>
	<li class="sub"> Principles of Data Integration</li>
	<li class="sub"> Fundamentals of Kafka SDP</li>
	<li class="sub"> Data Integration with Kafka SDP - Hands-on </li>
	<li class="sub"> Case study: Data integration</li>
	<li class="main"><b> Part VI - Data storage and NoSQL </b> </li>
	<li class="sub"> NoSQL and storage fundamentals </li>
	<li class="sub"> NoSQL hands-on</li>
	<li class="sub"> Case study: Modelling for real-time data</li>
	<li class="main"> <b> Part V - Data Processing </b> </li>
	<li class="sub"> Principles of Data processing Spark basics</li>
	<li class="sub"> Spark basics hands-on </li>
	<li class="sub"> Advanced Spark </li>
	<li class="sub"> Advanced Spark: hands-on</li>
	<li class="sub"> Spark Streaming</li>
	<li class="sub"> Case study: Streaming and Batch processing</li>
	<li class="main"> <b> Part VII - Data Access and querying </b> </li>
	<li class="sub"> Enabling analytics </li>
	<li class="sub"> Hive-on-spark </li>
	<li class="sub"> Apache Drill </li>
	<li class="sub"> Case study: BI and Viz </li>
	<li class="main"> <b> Part VIII - Capstone project </b> </li>
	</ul>
	</div>


	<h2> <font color =" blue"> Part 1 </font> </h2>
	<div>

	<ol>
	<li> <b> What led to the rise of “Big data” : Converging technology trends </b></li>
	<ul>
	<li class="sub"> Web 2.0 </li>
	<li class="sub"> Commoditization of Hardware</li>
	<li class="sub"> Mobile revolution </li>
	<li class="sub"> Open-source movement </li>
	<li class="sub"> IoT </li>
	</ul>

	<li> <b> How Google set the “Big data” ball rolling ? </b> </li>
	<ul>
	<li class="sub"> Why Google wanted to overoil their web crawlers with new processing techniques </li>
	<li class="sub"> Enter MapReduce : How it contrasts with older data-parallel paradigms like MPPs and why is it a giant leap in parallel
	processing </li>
	<li class="sub"> 2005, Release of Google M/R paper first milestone and big data and the chain of events that is set on motion </li>
	<li class="sub"> Project Nutch : 2nd milestone, Doug cutting and Mike in yahoo built the first implementation of M/R and GFS and
	open-sourced it as Hadoop </li>
	</ul>
	<li> <b> The idea of quasi-structured and unstructured data vs structured data </b> </li>
	<li> <b> Change in status quo: Why distributed system and parallel processing are the new normal ? </b> </li>
	<li> <b> Open source wave - complex distributed data frameworks are accessible to all.</b> </li>
	<ul>
	<li class="sub"> The other frameworks that shaped the big data world as we know today. </li>
	<li class="sub"> Companies that made big data technology accessible </li>
	</ul>
	</ol>
	</div>
	</body>
	</html>