spark-workshop/slides/00_Packt_Live_Streaming_Analytics.html at gh-pages · jaceklaskowski/spark-workshop · GitHub

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<!doctype html>
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    <title>Packt Live :: Streaming Analytics with Apache Spark</title>

    <meta name="description" content="Packt Live :: Streaming Analytics with Apache Spark">
    <meta name="author" content="Jacek Laskowski">

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          <small>Copyright ©2017 Jacek Laskowski</small>
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        <section class="intro" data-transition="zoom">
          <p>
            <img width="100%" style="background:none; border:none; box-shadow:none;" data-src="images/packt-live-capgemini-promo.png">
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            <img width="20%" style="background:none; border:none; box-shadow:none;" data-src="images/packt-live.png">
            &nbsp;
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          <h1>Streaming Analytics</h1>
          <h2>Apache Spark</h2>
          <h4><a href="https://twitter.com/jaceklaskowski">@jaceklaskowski</a> / <a href="http://stackoverflow.com/users/1305344/jacek-laskowski">StackOverflow</a> / <a href="https://github.com/jaceklaskowski">GitHub</a><br />
          Gitbooks: <a href="http://bit.ly/mastering-apache-spark">Mastering Apache Spark</a>  / <a href="http://bit.ly/spark-structured-streaming">Spark Structured Streaming</a></h4>
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              <li><b>Jacek Laskowski</b> is an independent consultant
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              <li>Contact me at <b>jacek@japila.pl</b> or follow me on twitter at <a href="https://twitter.com/jaceklaskowski">@JacekLaskowski</a> &lt;-- and learn more Spark!</li>
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            ## Objective

            Learn how **real-time streaming analytics** helps organizations <br />
            keep pace by **analyzing in-motion massive datasets** <br />
            for **advanced analytics** and powerful **interactive applications**. <br />
            *Apache Spark-way*
          </script>
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              ## Agenda

              1. [Part 1](#/agenda-part1): Familiarize yourself with **Spark SQL**
              1. [Part 2](#/agenda-part2): Understand **Spark MLlib**
              1. [Part 3](#/agenda-part3): **Structured Streaming** and **Kafka**
              1. [Part 4](#/agenda-part4): Streaming Analytics Example
            </script>
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              ## Part 1: Familiarize yourself
              ## with Spark SQL

              1. [Introduction to Spark SQL](https://jaceklaskowski.github.io/spark-workshop/slides/spark-sql.html)
              1. [Spark Architecture and Cluster Manager](https://jaceklaskowski.github.io/spark-workshop/slides/spark-core-spark-and-cluster-managers.html)
              1. [Basic Aggregation](https://jaceklaskowski.github.io/spark-workshop/slides/spark-sql-basic-aggregation.html)
              1. [Windowed Aggregation](https://jaceklaskowski.github.io/spark-workshop/slides/spark-sql-windowed-aggregation.html)
              1. [The Internals of Structured Query Execution](spark-sql-internals-of-structured-query-execution.html)
            </script>
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              ## Part 2: Understand Spark MLlib

              1. [Machine Learning with Spark MLlib](spark-mllib.html)
              1. [ML Pipelines](spark-mllib-ml-pipelines.html)
            </script>
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            <script type="text/template">
              ## Part 3: Structured Streaming and Kafka

              1. [Structured Streaming](https://jaceklaskowski.github.io/spark-workshop/slides/spark-structured-streaming.html)
              1. [Elements of Kafka](https://jaceklaskowski.github.io/spark-workshop/slides/04-apache-kafka.html)
            </script>
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              ## Part 4
              # Streaming Analytics Example
            </script>
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            ## Recap

            1. [Part 1](#/agenda-part1): Familiarize yourself with **Spark SQL**
            1. [Part 2](#/agenda-part2): Understand **Spark MLlib**
            1. [Part 3](#/agenda-part3): **Structured Streaming** and **Kafka**
            1. [Part 4](#/agenda-part4): Streaming Analytics Example
          </script>
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            # Questions?

              * Read [Mastering Apache Spark 2](https://bit.ly/mastering-apache-spark)
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