/spark-workshop

Apache Spark™ and Scala Workshops

Primary LanguageHTMLApache License 2.0Apache-2.0

Apache Spark™ and Scala Workshops

This repository contains the materials (i.e. agendas, slides, demo, exercises) for Apache Spark™ and Scala workshops led by Jacek Laskowski.

  • Have you ever thought about learning Apache Spark™ or Scala?
  • Would you like to gain expertise in the tools used for Big Data and Predictive Analytics but you don't know where to start?
  • Do you know the basics of Apache Spark™ and have been wondering how to reach the higher levels of expertise?
  • Are you considering a Apache Spark™ Developer Certification from companies like Databricks, Cloudera, Hortonworks or MapR?

If you answered YES to any of the questions above, I have good news for you! Join one of the following Apache Spark™ workshops and become a Apache Spark™ pro.

  1. Advanced Apache Spark for Developers Workshop (5 days)
  2. Spark Structured Streaming Workshop (Apache Spark 2.3)
  3. Spark and Scala (Application Development) Workshop
  4. Spark Administration and Monitoring Workshop
  5. Spark and Scala Workshop for Developers (1 Day)

You can find the slides for the above workshops and others at Apache Spark Workshops and Webinars page.

No prior experience with Apache Spark or Scala required.

CAUTION: The workshops are very hands-on and practical, and certainly not for faint-hearted. Seriously! After 5 days your mind, eyes, and hands will all be trained to recognize the patterns where and how to use Spark and Scala in your Big Data projects.

Apache Spark™ Workshop Setup

git clone the project first and execute sbt test in the cloned project's directory.

$ sbt test
...
[info] All tests passed.
[success] Total time: 3 s, completed Mar 10, 2016 10:37:26 PM

You should see [info] All tests passed. to consider yourself prepared.

Docker Image

Execute the following command to have a complete Docker image for the workshop.

NOTE: It was tested on Mac OS only. I assume that -v in the command will not work on Windows and need to be changed to appropriate environment settings.

docker run -ti -p 4040:4040 -p 8080:8080 -v "$PWD:/home/spark/workspace" -v "$HOME/.ivy2":/home/spark/.ivy2 -h spark --name=spark jaceklaskowski/docker-spark

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