Define and Process Data Pipelines in Hadoop with Apache Falcon

Apache Falcon is a framework for simplifying data governance and pipeline processing


Apache Falcon simplifies the configuration of data motion with: replication; lifecycle management; lineage and traceability. This provides data governance consistency across Hadoop components.


In this tutorial we will walk through a scenario where email data lands hourly on a cluster. In our example:

  • This cluster is the primary cluster located in the Oregon data center.
  • Data arrives from all the West Coast production servers. The input data feeds are often late for up to 4 hrs.

The goal is to clean the raw data to remove sensitive information like credit card numbers and make it available to our marketing data science team for customer churn analysis.

To simulate this scenario, we have a pig script grabbing the freely available Enron emails from the internet and feeding it into the pipeline.


  • A cluster with Apache Hadoop 2 configured
  • A cluster with Apache Falcon configured

The easiest way to meet the above prerequisites is to download the HDP Sandbox

After downloading the environment, confirm that Apache Falcon is running. Below are the steps to validate that:

  1. if Ambari is not configured on your Sandbox, go and enable Ambari.
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  2. Once Ambari is enabled, navigate to Ambari at, login with username and password of admin and admin respectively. Then check if Falcon is running.<Display Name>
  3. If Falcon is not running, start Falcon:

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Steps for the Scenario

  1. Create cluster specification XML file
  2. Create feed (aka dataset) specification XML file
    • Reference cluster specification
  3. Create the process specification XML file
    • Reference cluster specification – defines where the process runs
    • Reference feed specification – defines the datasets that the process manipulates

We have already created the necessary xml files. In this step we are going to download the specifications and use them to define the topology and submit the storm job.

Staging the component of the App on HDFS

In this step we will stage the pig script and the necessary folder structure for inbound and outbound feeds on the HDFS:

First download this zip file called to your local host machine.

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Navigate using your browser to the Hue – File Browser interface at to explore the HDFS.

Navigate to /user/ambari-qa folder like below:

Now we will upload the zip file we just downloaded:

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This should also unzip the zip file and create a folder structure with a folder called falcon .

Staging the specifications

SSH in to the VM:

ssh root@ -p 2222;

The password is hadoop

From the SSH session, first we will change our user to ambari-qa. Type:

su ambari-qa

Go to the users home directory:

cd ~

Download the topology, feed and process definitions:


Unzip the file:

unzip ./

Change Directory to the folder created:

cd falconChurnDemo/

Submit the entities to the cluster:

Cluster Specification

Cluster specification is one per cluster.

See below for a sample cluster specification file.

Back to our scenario, lets submit the ‘oregon cluster’ entity to Falcon. This signifies the primary Hadoop cluster located in the Oregon data center.

falcon entity -type cluster -submit -file oregonCluster.xml

Then lets submit the ‘virginia cluster’ entity to Falcon. This signifies the backup Hadoop cluster located in the Virginia data center

falcon entity -type cluster -submit -file virginiaCluster.xml

If you view the XML file you will see how the cluster location and purpose has been captured in the XML file.

Feed Specification

A feed (a.k.a dataset) signifies a location of data and its associated replication policy and late arrival cut-off time.

See below for a sample feed (a.k.a dataset) specification file.

Back to our scenario, let’s submit the source of the raw email feed. This feed signifies the raw emails that are being downloaded into the Hadoop cluster. These emails will be used by the email cleansing process.

falcon entity -type feed -submit -file rawEmailFeed.xml

Now let’s define the feed entity which will handle the end of the pipeline to store the cleansed email. This feed signifies the emails produced by the cleanse email process. It also takes care of replicating the cleansed email dataset to the backup cluster (virginia cluster)

falcon entity -type feed -submit -file cleansedEmailFeed.xml


A process defines configuration for a workflow. A workflow is a directed acyclic graph(DAG) which defines the job for the workflow engine. A process definition defines the configurations required to run the workflow job. For example, process defines the frequency at which the workflow should run, the clusters on which the workflow should run, the inputs and outputs for the workflow, how the workflow failures should be handled, how the late inputs should be handled and so on.

Here is an example of what a process specification looks like:

Back to our scenario, let’s submit the ingest and the cleanse process respectively:

The ingest process is responsible for calling the Oozie workflow that downloads the raw emails from the web into the primary Hadoop cluster under the location specified in the rawEmailFeed.xml It also takes care of handling late data arrivals

falcon entity -type process -submit -file emailIngestProcess.xml

The cleanse process is responsible for calling the pig script that cleans the raw emails and produces the clean emails that are then replicated to the backup Hadoop cluster

falcon entity -type process -submit -file cleanseEmailProcess.xml

Schedule the Falcon entities

So, all that is left now is to schedule the feeds and processes to get it going.

Ingest the feed

falcon entity -type feed -schedule -name rawEmailFeed

falcon entity -type process -schedule -name rawEmailIngestProcess

Cleanse the emails

falcon entity -type feed -schedule -name cleansedEmailFeed

falcon entity -type process -schedule -name cleanseEmailProcess


In a few seconds you should notice that that Falcon has started ingesting files from the internet and dumping them to new folders like below on HDFS:

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In a couple of minutes you should notice a new folder called processed under which the files processed through the data pipeline are being emitted:

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We just created an end-to-end data pipeline to process data. The power of the Apache Falcon framework is its flexibility to work with pretty much any open source or proprietary data processing products out there.


Rizwan Mian
December 3, 2014 at 12:36 pm

Works as suggests on the tin.

September 26, 2014 at 12:43 pm

Very fascinating, quite promising and spectacular!!

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Try this tutorial with :

These tutorials are designed to work with Sandbox, a simple and easy to get started with Hadoop. Sandbox offers a full HDP environment that runs in a virtual machine.