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

Book case study


Throughout this book, we will try to solve case study that will revolve around various concepts of Oozie.

One of the main use cases of Hadoop is ETL data processing.

Suppose we work for a large consulting company and have won a project to set up a Big Data cluster inside the customer data center. On a high level, the requirements are to set up an environment that will satisfy the following flow:

  1. Get data from various sources in Hadoop (file-based loads and Sqoop-based loads).

  2. Preprocess them with various scripts (Pig, Hive, and MapReduce).

  3. Insert that data into Hive tables for use by analysts and data scientists.

  4. Data scientists then write machine learning models (Spark).

We will use Oozie as our processing scheduling system to do all the preceding tasks. Since writing actual Hive, Sqoop, MapReduce, Pig, and Spark code is not in the scope of this book, I will not dive into explaining business logic for those. So I have kept them very simple.

In our architecture, we have one landing...