Difference between ETL and ELT Pipeline

Introduction

This article will be a deep guide for Beginners in Apache Oozie. Apache Oozie is a workflow scheduler system for managing Hadoop jobs. It enables users to plan and carry out complex data processing workflows while handling several tasks and operations throughout the Hadoop ecosystem. Users of Oozie can describe dependencies between various jobs and activities, designate the sequence in which they should be executed, and handle problems and retries. It supports many Hadoop- technologies, including Pig, Hive, Sqoop, and Hadoop MapReduce. Oozie offers an API for interacting with other tools and systems and a web-based interface for managing and monitoring processes. Apache Oozie is an effective tool for planning and coordinating significant data operations in Hadoop.

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Learning Objectives:

In this article, you will:

1. Understand the basics of Apache Oozie.
2. How Apache Oozie was created and its evolution through time.
3. What is the component included in Apache Oozie?
4. What are its key features?
5. The components and workflow of Apache Oozie.

This article was published as a part of the Data Science Blogathon.

Table of Contents

1. Definition and Overview
2. History and Evolution of Oozie
3. Main Components of Apache Oozie
4. Key Features of Oozie
5. Components of Oozie
6. Oozie Workflow: Building and Designing a Simple Workflow
7. Conclusion

Definition and Overview

An open-source workflow scheduling tool, Apache Oozie helps handle and organize data processing tasks across Hadoop-based infrastructure.

Users can create, plan, and control workflows that contain a coordinated series of Hadoop jobs, Pig scripts, Hive searches, and other operations. Oozie can handle task dependencies, manage retry mechanisms, and support a variety of workflow types, including simple and sophisticated processes.

Overall, Oozie provides a flexible and adaptable platform for constructing data pipelines in Hadoop systems while facilitating the management and scheduling of significant data processing processes.

History and Evolution of Oozie

Yahoo initially created Apache Oozie in 2008 as a tool for privately managing Hadoop operations. Later, in 2011, it was made available as an open-source undertaking run by the Apache Software Foundation.

Oozie has had a lot of updates and improvements since then to improve its performance and functionality. For example, Oozie 3.2, launched in 2012, provided additional capabilities like support for Java actions and sub-workflows and Hadoop 2.x support.

For managing and scheduling massive data processing processes, Oozie is a critical Hadoop ecosystem component frequently used in production settings. Its community has expanded, with developers contributing to its continual development and advancements.

To help users create more complicated workflows and handle a broader range of data processing jobs, Oozie has recently been integrated with other Hadoop ecosystem products like Apache Spark and Apache Flink.

Main Components of Apache Oozie

The Oozie Workflow Manager and Oozie Coordinators are the two main workflow management components of Apache Oozie.

• The Oozie Workflow Manager manages and executes workflows and sequences of actions that must be conducted in a specific order. The Workflow Definition Language (WDL), an Extensible Markup Language (XML)-based language, defines workflows. The WDL outlines the order in which activities must be carried out, the input and output data required by each action, and their interdependencies. In addition to managing dependencies between actions and handling errors, the Workflow Manager parses the WDL and carries out the steps in the predetermined order.

• Oozie Coordinators are responsible for organizing and overseeing repeating workflows. The Coordinator Application Language (CAL), an XML-based language, defines coordinators. Coordinators describe a schedule for running workflows, the data input for each instance of the workflow, and dependencies between the cases of the process. The Coordinator operates periodically and generates workflow instances by the plan and supplied data.

The Workflow Manager and Coordinators work together to create a robust system for controlling and carrying out complicated workflows in Hadoop environments. With a RESTful API for programmatic control, Oozie offers a web-based graphical user interface for managing workflows and coordinators.

Source: cloudduggu

Key Features of Oozie

Apache Oozie is a powerful tool for managing and scheduling significant data processing activities due to its many essential features. These features include, among others:

• Oozie allows users to create, organize, and carry out workflow collections of tasks or actions.

• Oozie supports the scheduling of repeating processes using coordinators, which lets users provide a schedule for when workflows will execute.

• Management of dependencies between tasks and workflows is supported by Oozie, ensuring that activities are executed in the proper order and that workflows are correctly completed.

• Oozie is built on a modular, extensible architecture that enables users to customize and extend its features.

• Oozie is highly scalable and designed for large-scale data processing tasks in distributed computing environments.

• Oozie offers a web-based graphical user interface and RESTful API for controlling and monitoring workflows and coordinators.

• Creating complex data processing pipelines is made possible by Oozie’s integration with other Hadoop ecosystem technologies like Pig, Hive, and MapReduce.

• Oozie provides a complete management and scheduling tool for Hadoop environments’ massive data processing operations.

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Components of Oozie

Apache Oozie is a powerful tool for managing and scheduling significant data processing activities due to its many essential features. These features include, among others:

• Workflow management: Oozie allows users to create, organize, and carry out workflow collections of tasks or actions.

• Oozie supports the scheduling of repeating processes using coordinators, which lets users provide a schedule for when workflows will execute.

• Dependency management: Management of dependencies between tasks and workflows is supported by Oozie, ensuring that activities are executed in the proper order and that workflows are correctly completed.

• Extensible architecture: Oozie is built on a modular, extensible architecture that enables users to customize and extend its features.
• Scalability: Oozie is highly scalable and designed for large-scale data processing tasks in distributed computing environments.

• Monitoring and management: Oozie offers a web-based graphical user interface and RESTful API for controlling and monitoring workflows and coordinators.

• Integration with Hadoop ecosystem: Creating complex data processing pipelines is possible through Oozie’s integration with other Hadoop ecosystem technologies like Pig, Hive, and MapReduce.

Oozie provides a complete management and scheduling tool for Hadoop environments’ massive data processing operations.

Oozie Workflow: Building and Designing a Simple Workflow

To build and design a simple workflow in Oozie, follow these steps:

• Establish the workflow: The workflow should first be created using the Workflow Definition Language (WDL). The WDL outlines the order in which activities must be carried out, the input and output data required by each action, and their interdependencies.

Here’s an example of a simple WDL that performs a word count on a text file:

<workflow-app xmlns="uri:oozie:workflow:0.5" name="word-count">
    <start to="word-count-action"/>
    <action name="word-count-action">
        <map-reduce>
            <job-tracker>${jobTracker}</job-tracker>
            <name-node>${nameNode}</name-node>
            <configuration>
                <property>
                    <name>mapred.mapper.class</name>
                    <value>org.apache.hadoop.mapred.lib.IdentityMapper</value>
                </property>
                <property>
                    <name>mapred.reducer.class</name>
                    <value>org.apache.hadoop.mapred.lib.IdentityReducer</value>
                </property>
                <property>
                    <name>mapred.input.dir</name>
                    <value>/user/hadoop/input</value>
                </property>
                <property>
                    <name>mapred.output.dir</name>
                    <value>/user/hadoop/output</value>
                </property>
            </configuration>
        </map-reduce>
        <ok to="end"/>
        <error to="fail"/>
    </action>
    <kill name="fail">
        <message>Workflow failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
    </kill>
    <end name="end"/>
</workflow-app>
#import csv#import csv

• Define the activities: Provide the actions that will be carried out during the workflow in the WDL. Oozie supports various action types, including custom Java actions, Hadoop MapReduce jobs, Pig scripts, and Hive queries.

In the example above WDL, the action is a MapReduce job that counts the words in a text file.

• Configure the workflow: In the WDL, configure the workflow by specifying the input and output data for each action and any other configuration parameters required by the action.

In the example WDL above, the input data for the MapReduce job is a text file located in /user/Hadoop/input, and the output data is written to /user/Hadoop/output.

• Once the WDL has been defined, please submit it to Oozie using either the web console or the Oozie CLI.

• Send the workflow in Use the Oozie CLI or online portal to send the workflow to Oozie.

Conclusion

To conclude, Apache Oozie is an essential tool for organizing and carrying out intricate operations in Hadoop. Many companies are using Apache Oozie as their main tool. Users can plan and coordinate different Hadoop tasks and processes with Oozie, specifying their dependencies and execution priorities. This enables effective data processing and analysis while supplying error handling and monitoring features. Oozie offers a user-friendly web interface, compatibility with many Hadoop- technologies, and simple system and tool integration APIs. Ultimately, Oozie helps businesses manage and coordinate their big data workflows more effectively, boosting output, data processing, and analysis effectiveness.

Source: Enlyft

Key takeaways

• Initially, we have seen the definition and overview

• History and Evolution of Oozie and understanding its workflow manager and coordinators

• And its key features of Oozie

• At last, we saw the components and workflow of Oozie

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