Book Image

Apache Oozie Essentials

By : Jagat Singh
Book Image

Apache Oozie Essentials

By: Jagat Singh

Overview of this book

As more and more organizations are discovering the use of big data analytics, interest in platforms that provide storage, computation, and analytic capabilities is booming exponentially. This calls for data management. Hadoop caters to this need. Oozie fulfils this necessity for a scheduler for a Hadoop job by acting as a cron to better analyze data. Apache Oozie Essentials starts off with the basics right from installing and configuring Oozie from source code on your Hadoop cluster to managing your complex clusters. You will learn how to create data ingestion and machine learning workflows. This book is sprinkled with the examples and exercises to help you take your big data learning to the next level. You will discover how to write workflows to run your MapReduce, Pig ,Hive, and Sqoop scripts and schedule them to run at a specific time or for a specific business requirement using a coordinator. This book has engaging real-life exercises and examples to get you in the thick of things. Lastly, you’ll get a grip of how to embed Spark jobs, which can be used to run your machine learning models on Hadoop. By the end of the book, you will have a good knowledge of Apache Oozie. You will be capable of using Oozie to handle large Hadoop workflows and even improve the availability of your Hadoop environment.
Table of Contents (16 chapters)
Apache Oozie Essentials
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Chapter case study


We will start this chapter with a case study example. In the previous chapter, we created our first Oozie Workflow to delete a given directory; we will build on top of that.

In this chapter, our use case is as follows.

On a daily basis we get incoming data in a HDFS directory. Our Workflow comes into action to process it via a simple Pig script. If we find the directory empty, we send a mail to the support team stating we did not get any data today. This is a very common data ingestion pattern in Hadoop for file-based loads.

There are many concepts, which will be introduced by use of this example; I thought to do it this way rather than sharing the concept first and sharing the example later. Using this example, we will cover the following concepts:

  • Decision nodes

  • Expression language

  • Oozie command-line execution

Let's get started. The data ingestion pipeline for our use case can be represented as follows:

Pig Preprocess Decision node

Open Hue and go to Editor | Workflows to create...