Getting Started with AWS Data Pipeline - AWS Data Pipeline
Create the PipelineMonitor the Running PipelineView the OutputDelete the Pipeline

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Getting Started with AWS Data Pipeline

AWS Data Pipeline helps you sequence, schedule, run, and manage recurring data processing workloads reliably and cost-effectively. This service makes it easy for you to design extract-transform-load (ETL) activities using structured and unstructured data, both on-premises and in the cloud, based on your business logic.

To use AWS Data Pipeline, you create a pipeline definition that specifies the business logic for your data processing. A typical pipeline definition consists of activities that define the work to perform, and data nodes that define the location and type of input and output data.

In this tutorial, you run a shell command script that counts the number of GET requests in Apache web server logs. This pipeline runs every 15 minutes for an hour, and writes output to Amazon S3 on each iteration.

Prerequisites

Before you begin, complete the tasks in Setting up for AWS Data Pipeline.

Pipeline Objects

The pipeline uses the following objects:

ShellCommandActivity

Reads the input log file and counts the number of errors.

S3DataNode (input)

The S3 bucket that contains the input log file.

S3DataNode (output)

The S3 bucket for the output.

Ec2Resource

The compute resource that AWS Data Pipeline uses to perform the activity.

Note that if you have a large amount of log file data, you can configure your pipeline to use an EMR cluster to process the files instead of an EC2 instance.

Schedule

Defines that the activity is performed every 15 minutes for an hour.

Create the Pipeline

The quickest way to get started with AWS Data Pipeline is to use a pipeline definition called a template.

To create the pipeline

  1. Open the AWS Data Pipeline console at https://console.aws.amazon.com/datapipeline/.

  2. From the navigation bar, select a region. You can select any region that's available to you, regardless of your location. Many AWS resources are specific to a region, but AWS Data Pipeline enables you to use resources that are in a different region than the pipeline.

  3. The first screen that you see depends on whether you've created a pipeline in the current region.

    1. If you haven't created a pipeline in this region, the console displays an introductory screen. Choose Get started now.

    2. If you've already created a pipeline in this region, the console displays a page that lists your pipelines for the region. Choose Create new pipeline.

  4. In Name, enter a name for your pipeline.

  5. (Optional) In Description, enter a description for your pipeline.

  6. For Source, select Build using a template, and then select the following template: Getting Started using ShellCommandActivity.

  7. Under the Parameters section, which opened when you selected the template, leave S3 input folder and Shell command to run with their default values. Click the folder icon next to S3 output folder, select one of your buckets or folders, and then click Select.

  8. Under Schedule, leave the default values. When you activate the pipeline the pipeline runs start, and then continue every 15 minutes for an hour.

    If you prefer, you can select Run once on pipeline activation instead.

  9. Under Pipeline Configuration, leave logging enabled. Choose the folder icon under S3 location for logs, select one of your buckets or folders, and then choose Select.

    If you prefer, you can disable logging instead.

  10. Under Security/Access, leave IAM roles set to Default.

  11. Click Activate.

    If you prefer, you can choose Edit in Architect to modify this pipeline. For example, you can add preconditions.

Monitor the Running Pipeline

After you activate your pipeline, you are taken to the Execution details page where you can monitor the progress of your pipeline.

To monitor the progress of your pipeline

  1. Click Update or press F5 to update the status displayed.

    Tip

    If there are no runs listed, ensure that Start (in UTC) and End (in UTC) cover the scheduled start and end of your pipeline, and then click Update.

  2. When the status of every object in your pipeline is FINISHED, your pipeline has successfully completed the scheduled tasks.

  3. If your pipeline doesn't complete successfully, check your pipeline settings for issues. For more information about troubleshooting failed or incomplete instance runs of your pipeline, see Resolving Common Problems.

View the Output

Open the Amazon S3 console and navigate to your bucket. If you ran your pipeline every 15 minutes for an hour, you'll see four time-stamped subfolders. Each subfolder contains output in a file named output.txt. Because we ran the script on the same input file each time, the output files are identical.

Delete the Pipeline

To stop incurring charges, delete your pipeline. Deleting your pipeline deletes the pipeline definition and all associated objects.

To delete your pipeline

  1. On the List Pipelines page, select your pipeline.

  2. Click Actions, and then choose Delete.

  3. When prompted for confirmation, choose Delete.

If you are finished with the output from this tutorial, delete the output folders from your Amazon S3 bucket.