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

Data pipelines


In real Big Data projects, the Coordinators are scheduled tasks that are part of the data pipeline. For example, get data from some system and process it (this forms one Coordinator), and then another sub process can send the processed data to a database (this forms another Coordinator). Finally, both of them are abstracted to form Bundle. To think in terms of how to solve your job using Oozie, start by drawing the job Workflow on a whiteboard/paper. Then discuss with your team how you can create unit abstractions to run individually and in isolation.

Check out the following example.

The database has a record of daily rainfall in Melbourne. We import that data to Hadoop using a regular Coordinator job (Coordinator 1). Using another scheduled job, we send the results back to the database as shown in the following figure:

Data pipelines

Note

Exercise: Take the preceding example and make one Bundle that processes our rainfall data in the first Coordinator (using Pig script) and sends...