From 0 to 1: The Oozie Orchestration Framework

A first-principles guide to working with Workflows, Coordinators and Bundles in Oozie

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Intermediate

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4 Hours

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A first-principles guide to working with Workflows, Coordinators and Bundles in Oozie

Course Information

About this course:

Oozie is formidable because it is entirely written in XML, which is hard to debug when things go wrong. However, once you've figured out how to work with it, it's like magic. Complex dependencies, managing a multitude of jobs at different time schedules, managing entire data pipelines are all made easy with Oozie. Oozie allows you to manage Hadoop jobs as well as Java programs, scripts and any other executable with the same basic set up. It manages your dependencies cleanly and logically. Knowing the right configurations parameters which gets the job done, that is the key to mastering Oozie

The  average salary of Systems Administrator is $72,762  per year.

Course Objectives:

After completing this course, students will be able to:

  • Install and set up Oozie
  • Configure time-triggered and data-triggered Workflows
  • Configure Workflows to run jobs on Hadoop
  • Configure data pipelines using Bundles

 Audience:

 Engineers, analysts and sysadmins who are interested in big data processing on Hadoop.

Prerequisites:

Students should have basic knowledge of the Hadoop eco-system and should be able to run MapReduce jobs on Hadoop.

Suggested prerequisites courses:

 Career path:

Big Data…

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Subjects Big Data
Lab Access No
Technology Hadoop
Learning Style Self-Paced Learning
Learning Type Course
Difficulty Intermediate
Course Duration 4 Hours
Language English
VPA Discount VPA Discount

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Tom Robertson
(Data Science Enthusiast)

Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.

Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.

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