AWS Data Pipeline: Benefits & Process of Accessing Data Pipeline

The amount of data produced is escalating rapidly due to the development of technologies and ease of connectivity. Under this massive pile of data is the ‘captive intelligence.’ Organizations can utilize this to enhance and expand their business. To acquire utility from the data, the organizations need to move, sort, filter, reformat, analyze, and report it. Organizations might have to do this often and rapidly to persist firmly in the market. The Amazon service of AWS Data Pipeline is a definitive solution.

What is the AWS Data Pipeline?

AWS Data Pipeline is a web service used to define data-driven workflows. It helps in automating the movement and transformation of data. It ensures that the tasks depend on the successful completion of preceding tasks. The AWS Data Pipeline implements the logic you set up to define the criterion of your data transformations. AWS Data Pipeline involves computing power to perform most of the operations. This power originates from Amazon’s computing services like Elastic MapReduce (EMR).

Concept of AWS Data Pipeline

The concept of the AWS Data Pipeline is easy. You have a data Pipeline located on the top. You have an input store that can be Amazon S3, Dynamo DB, or Redshift. Data from these input stores is sent to Data Pipeline. The Data Pipeline sort and process the data and sends it to the output stores. The output stores can be Amazon Redshift, Amazon S3, or Redshift.

Benefits of AWS Data Pipeline

  • It is user-friendly as it provides a drag-and-drop console within the interface.
  • It is built on distributed and fault-tolerant infrastructure. If any fault occurs, it relieves the users of all activities relating to system stability and recovery.
  • It provides flexibility with features like scheduling, dependency tracking, and error handling.
  • It offers you to dispatch work efficiently to one or more machines in serial or parallel.
  • It provides complete control over the computational resources like EC2 instances or EMR reports. They execute your data pipeline logic.
  • It is inexpensive to use as billed at affordable monthly rates.

Components of AWS Data Pipeline

The AWS Data Pipeline is organized into a pipeline definition with the following components

1.    Task runners

They are installed in the computing machines that process the extraction, transformation, and load activities. They are responsible for carrying out these processes according to the defined schedule in the pipeline definition.

2.    Data nodes

They represent the type of data and the location from which the pipeline can access it. It includes both input and output data elements. It supports data nodes like DynamoDB, SQL, Amazon S3, etc.

3.    Activities

Activities define the work performed on a schedule using a computational resource and typically input and output data nodes. Such as copy, EMR, hive, hive copy, redshift copy, shell command, and SQL.

4.    Preconditions

These are conditional statements that must be satisfied before scheduling the activities. These are used for scheduling pipeline activities based on defined logic.

5.    Resources

These computational resources perform the work that a pipeline defines. These are usually the Amazon EC2 or EMR clusters.

6.    Actions

These are notifications or termination requests. When certain conditions, occur the data pipelines are customized to implement specific actions.

Need of AWS Data Pipeline

  • In an organization, data is dispersed across various sources in various formats. The dispersed data has to be processed in an actionable form to use further.
  • To keep a record of all the activities and storing the data in the existing database. Organizations have their data storehouse, cloud-based storage such as Amazon S3, Amazon RDS & database servers running on EC2 instances.
  • Managing a massive pile of data is time-consuming and expensive. A lot of investment has to be done in sorting, storing, and processing the data.

Process of Aws Data Pipeline

AWS Data Pipeline’s process is all about definitions. Before proceeding with the working of the AWS data pipeline, you should have an AWS account. You can do it based on predefined templates from AWS.

Step 1-

Go to the data pipeline and select ‘Create new pipeline’ from the AWS console. It will lead you to the pipeline configuration screen.

Step 2-

Enter the name and description of the pipeline and choose a template. For more advanced use cases, you can opt another option, using the architect application.

Step 3-

Fill in the parameters.

Step 4-

Select the schedule for the activity to run. You can also enable a one-time run on activation.

Step 5-

Enable the logging configuration. We suggest you allow it for any pipeline activity and lead the login directory to an S3 location. It can be beneficial for troubleshooting activities later. Click ‘Activate,’ and you’ve got made it.


AWS Data Pipeline provides a user-friendly interface that enables you to set up complicated workflows with a few clicks. It provides a flexible platform and proves to be an excellent asset for your organization. If you are looking to implement a fuss-free workflow, it might be better to use AWS Data Pipeline that provides excellent features and experience.