Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
 
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
 
Airflow pipelines are defined in Python, allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.

Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

The purpose of Luigi is to address all the plumbing typically associated with long-running batch processes. You want to chain many tasks, automate them, and failures will happen. These tasks can be anything, but are typically long running things like Hadoop jobs, dumping data to/from databases, running machine learning algorithms, or anything else.
 
There are other software packages that focus on lower level aspects of data processing, like Hive, Pig, or Cascading. Luigi is not a framework to replace these. Instead it helps you stitch many tasks together, where each task can be a Hive query, a Hadoop job in Java, a Spark job in Scala or Python, a Python snippet, dumping a table from a database, or anything else. It's easy to build up long-running pipelines that comprise thousands of tasks and take days or weeks to complete. Luigi takes care of a lot of the workflow management so that you can focus on the tasks themselves and their dependencies.
 
Luigi internally used at <a href="https://www.spotify.com">Spotify</a> to run
thousands of tasks every day, organized in complex dependency graphs.
Most of these tasks are Hadoop jobs. Luigi provides an infrastructure
that powers all kinds of stuff including recommendations, toplists, A/B
test analysis, external reports, internal dashboards, etc

The purpose of Luigi is to address all the plumbing typically associated with long-running batch processes. You want to chain many tasks, automate them, and failures will happen. These tasks can be anything, but are typically long running things like Hadoop jobs, dumping data to/from databases, running machine learning algorithms, or anything else.

Luigi - Python module that helps you build complex pipelines of batch jobs

Rundeck is an open source automation service with a web console, command line tools and a WebAPI. It lets you easily run automation tasks across a set of nodes.  It can schedule a job, more secure replacement of cron. It provides support for runbook automation where it can execute common set of reusable procedures.

Rundeck - Job Scheduler and Runbook Automation

Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs. Azkaban resolves the ordering through job dependencies and provides an easy to use web user interface to maintain and track your workflows. Azkaban was designed primarily with usability in mind. It has been running at LinkedIn for several years, and drives many of their Hadoop and data warehouse processes.
 
Its features include: 
<ul><li><li>Compatible with any version of Hadoop</li><li>Easy to use web UI</li><li>Simple web and http workflow uploads</li><li>Project workspaces</li><li>Scheduling of workflows</li><li>Modular and pluginable</li><li>Authentication and Authorization</li><li>Tracking of user actions</li><li>Email alerts on failure and successes</li><li>SLA alerting and auto killing</li><li>Retrying of failed jobs</li></li></ul>

Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs. Azkaban resolves the ordering through job dependencies and provides an easy to use web user interface to maintain and track your workflows. Azkaban was designed primarily with usability in mind. It has been running at LinkedIn for several years, and drives many of their Hadoop and data warehouse processes.

Azkaban - Batch workflow job scheduler created at LinkedIn to run Hadoop jobs

Oozie is a workflow scheduler system to manage Apache Hadoop jobs. Its jobs are Directed Acyclical Graphs (DAGs) of actions and triggered by time (frequency) and data availability. Oozie is integrated with the rest of the Hadoop stack supporting several types of Hadoop jobs out of the box (such as Java map-reduce, Streaming map-reduce, Pig, Hive, Sqoop and Distcp) as well as system specific jobs (such as Java programs and shell scripts).

Apache Oozie - Workflow Scheduler for Hadoop

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Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

Luigi - Python module that helps you build complex pipelines of batch jobs

Rundeck - Job Scheduler and Runbook Automation

Azkaban - Batch workflow job scheduler created at LinkedIn to run Hadoop jobs

Apache Oozie - Workflow Scheduler for Hadoop

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Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

Luigi - Python module that helps you build complex pipelines of batch jobs

Rundeck - Job Scheduler and Runbook Automation

Azkaban - Batch workflow job scheduler created at LinkedIn to run Hadoop jobs

Apache Oozie - Workflow Scheduler for Hadoop

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