AWS Data Pipeline Documentation

The amount of data collected is growing as technology advances and as ease of connectivity becomes more accessible. To extract value from data, companies must move, sort, filter, reformat, analyse, and report it. Amazon's AWS Data Pipeline service is ideal for this. Refer to this blog to discover more about it and what it has to offer.

Companies and organizations have evolved over the years and are also ever-increasing leading to many data generation, transformation, and transfer. This business of collecting, analyzing, transforming, and sharing of data helps a firm grow and develop. Amazon Web Service [AWS] is the perfect destination that you can reach for dealing with data in the cloud. By using the cloud, you get to have broader access; in fact, a global one. 

AWS Data Pipeline focuses on ‘data transfer’ or transferring data from the source location to the destined destination. Using AWS Data Pipelines, one gets to reduce their costs and time spent on repeated and continuous data handling.

Following is the list of topics covered in this AWS Data Pipeline tutorial:

Now, let’s get to know AWS Data Pipeline better.

Why do we need a Data Pipeline?

The primary use of a Data Pipeline is to have an organized way of handling business data, which will reduce the time and money spent on doing the same. 

Companies face many challenges when it comes to handling data on a large scale, and here are a few of their problems:

  • Data dealt with by a company may be unprocessed too. To process these data, a company has to spend more time and money. Examples of such data are transaction histories, data from log files, demographic data, etc. All of these lead to large amounts of data, which gets difficult to manage. 
  • Not all the data received or sent are of the same file format, which leads to further complications. Data can be unstructured/compatible. Its conversion is a time-consuming and challenging process. 
  • To store data is another challenging factor for a company as the firm needs to spend money on having its data warehouse and storage space that supports cloud usage. Amazon Relational Database Service [RDS] and Amazon S3 are some large cloud-based storage spaces.
  • The most crucial trouble faced by companies is the money and time that they need to spend on maintaining, storing, processing, and transforming these large bulks of data
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One can avoid these hardships when using AWS Data Pipeline as it helps collect data from various AWS services and place it in a single location, like a hub. When all the data is stored in one place, it becomes easy to handle, maintain, and share it regularly. 

Before you make a decision, here’s a detailed study about AWS Data Pipeline, its uses, benefits, architecture, components, functions, and its method of working. 

What is AWS Data Pipeline?

AWS Data Pipeline

Amazon Web Service [AWS] Data Pipeline is a service that can be used to handle, transform, and transfer data, especially business data. This service can be automated, and the data-driven workflows can be set, to avoid mistakes and long time-consuming working hours. 

With the help of AWS Data Pipeline, you can:

  • Ensure the availability of necessary resources.
  • Create workloads that deal with complicated data processing.
  • Transfer data to any of the AWS services.
  • Provide the required pauses/breaks for each task. 
  • Manage any inter-related tasks effectively.
  • Generate a system to notify any failures during the process.
  • Transfer and transform data that are locked in on-premises data silos.
  • Get the unstructured data analyzed and can shift it to Redshift to make a few small queries.
  • Shift even the log files from AWS Log to Amazon Redshift.
  • Protect data through AWS’s Disaster Recovery protocols, where the necessary data can be restored from the backups.  

On the whole, AWS Data Pipeline is used when one needs a defined course of data sources and data sharing for processing data, as per the requirement of the users. It’s a user-friendly service which is highly used in the current business world. 

AWS Data Pipeline - Concept

  • AWS Data Pipeline deals with a data pipeline with 3 different input spaces like Redshift, Amazon S3, and DynamoDB. 
  • The data collected from these three input valves are sent to the Data Pipeline.
  • When the data reaches the Data Pipeline, they are analyzed and processed.
  • The final result of the processed data is moved to the output valve, which can either be Amazon S3 / Redshift / Amazon Redshift.

These points quote the basic concept behind AWS Data Pipeline. But is this web service effective and efficient enough? Let’s find out its benefits and importance. 

Benefits of Data Pipeline:

There are six significant benefits of AWS Data Pipeline, and they are:

  • There are predefined sets of instructions and codes which allow the user just to type the function’s name, and the task will take place. Through this, a pipeline can be created with ease.
  • AWS Data Pipeline can be used for free. Even its ‘Pro version’ is of less cost, which makes it affordable for many and both big and small users. 
  • In case your function fails in the middle / if an error occurs, AWS Data Pipeline will get that particular activity retried automatically. With its failure notifications and immediate backup data, it is easy to keep trying different functions.  This feature makes AWS Data Pipeline more reliable. 
  • AWS Data Pipeline allows its users to set their working terms rather than using only the predefined set of functions already present in the application. This characteristic ensures flexibility for the users and an effective means to analyze and process data.
  • Due to the flexibility of the AWS Data Pipeline, it is scalable to distribute work to many machines and process many files.
  • A detailed record of your full execution logs will be sent to Amazon S3, making it more transparent and consistent. You can also access your business logic’s resources for enhancing and debugging it, whenever necessary.
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Components of AWS Data Pipeline:

Four main components include various concepts that help in the working of AWS Data Pipeline. 

Pipeline Definition: This deals with the rules and procedures involved in communicating business logic with the Data Pipeline. This definition has the following information:

  • The name, its stored location, and its data source’s format are all listed and are called “Data Nodes.” RedshiftDataNode, DynamoDBDataNode, S3DataNode, and SqlDataNode are all supported by AWS Data Pipeline.
  • When the SQL Queries are put to action on the databases, they tend to change the data source of the data, called “Activities”.
  • When the Activities are scheduled, they are called “Schedules.”
  • To have an action/activity performed in the AWS Data Pipeline, the user needs to meet the requirements / ‘Preconditions’. This action has to be done before scheduling the Activities.
  • The EMR cluster and Amazon EC2 are called ‘Resources’ because they are vital for the working of the AWS Data Pipeline.
  • Any status update regarding your AWS Data Pipeline is sent to you through employing an alarm or notification, and these are called ‘Actions.’

Pipeline: There are three main components for a Pipeline, and they are:

  • The components involved in establishing communication between the user’s Data Pipeline and the AWS Services are in this category.
  • Instances refer to the compilation of pipeline components which has the instructions for performing a particular task.
  • Attempts deal with the ‘retry’ option that an AWS Data Pipeline offers to its users in case of a failed operation.

Task Runner: As the name suggests, this application focuses on polling various tasks, present in the Data Pipeline to perform/run them. 

Precondition: This refers to a set of statements that define specific conditions that have to be met before a particular activity or action occurs in the AWS Data Pipeline.

Apart from these, there are particular objects that AWS Data Pipeline uses, and they are:

  • The “ShellCommandActivity” is used to detect errors from the input log files.
  • The input log file is stored in the S3 bucket, which is present in the “S3DataNode” input object.
  • For the output, the “S3DataNode” object contains the required S3 bucket. 
  • AWS Data Pipeline uses “Ec2 Resource” to execute an activity. If the file size is large, then you can use an EMR cluster.

What are the prerequisites for setting up AWS Data pipeline?

A precondition refers to a set of predefined conditions that must be met/be true before running an activity in the AWS Data Pipeline. The two types of such prerequisites are:

System-managed preconditions: As the name suggests, AWS Data Pipeline takes care of meeting the preconditions before starting an activity instead of waiting for the user to do it. 

  • DynamoDBDataExists
  • DynamoDBTableExists
  • S3KeyExists and
  • S3PrefixNotEmpty

These are the different system-managed preconditions.

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User-managed preconditions: You can use ‘runsOn’ / ‘workerGroup’ applications to specify the preconditions you want to have before running a function in the AWS Data Pipeline. However, you can derive ‘workerGroup’ when you perform an activity that meets the precondition set by you.

  • Exists and
  • ShellCommandPrecondition

These  2 are the different types of User-managed preconditions. 

Task to be Completed Before Using AWS Data Pipeline:

NOTE: Make sure you finish these tasks before you start creating an AWS Data Pipeline. 

Make a Sign-up:

Having an AWS account is mandatory to avail the services provided by AWS, including AWS Data Pipeline. Follow the below instructions to create an AWS account.

Go to from any of your web browsers.

There’ll be a list of instructions displayed on your screen which needs to be followed. 

The last step is to get a phone call with a verification code that needs to be entered on the phone keypad. 

Draft the Needed IAM Roles [CLI or API Only]: 

IAM Roles are important for AWS Data Pipeline as they enlist the actions and resources the Pipeline can access, and only they can be used. If in case you are already familiar with these, then make sure to update your existing version of IAM roles. But if you are new to all of these, then create the IAM roles manually.

  • Open in your web browser. 
  • Select ROLES and click “Create New Role.” 
  • Enter the role’s name as either “DataPipelineDefaultRole” or “DataPipelineDefaultResourceRole”.
  • To determine the role’s type go to “Select Role Type.” Press SELECT from the “AWS Data Pipeline” for the IAM’s default role and the IAM’s Resource role, press SELECT from the “Amazon EC2 Role for Data Pipeline”.
  • From the “Attach Policy” page, you should select “AWSDataPipelineRole” for the default role and “AmazonEC2RoleforDataPipelineRole” for the resource role and then press NEXT STEP.
  • Finally, click CREATE ROLE form the “Review” page. 

Mandatory 'Passrole' Permission and Policy for Predefined IAM Roles:

Ensure that the “Action”:” iam:PassRole” permission is predefined to both DataPipelineDefaultRole and DataPipelineDefaultResourceRole and to any custom roles required for accessing AWS Data Pipeline. You can also create a joint group with all the AWS Data Pipeline users and provide a managed policy called “AWSDataPipeline_FullAccess,” which will grant the “Action”:” iam:PassRole” permission to all its users without much delay and effort. 

Create Custom IAM Roles and Create an Inline Policy with the IAM Permissions: 

As a substitute for the task mentioned above, you can create two types of custom roles for AWS Data Pipeline with an inline policy that has the IAM permission for both the roles. The first type of custom role should be similar to “DataPipelineDefaultRole” and should be used for using Amazon EMR clusters. The second type should support Amazon EC2 in AWS Data Pipeline and can be identical to that of “DataPipelineDefaultResourceRole.” Now, generate the inline policy with the “Action”:” iam:PassRole” for the CUSTOM_ROLE.

How to Create an AWS Data Pipeline:

You can create an AWS Data Pipeline either through a template or through the console manually. 

  • Go to  and open the AWS Data Pipeline console.
  • Choose the required ‘region’ from the ‘navigation bar.’ Even when the region is different from that of the location, it does not matter.
  • If in case the region you selected is new, the console will display an ‘introduction’ where you should press ‘Get started now.’ But if the region you selected is old, the console will view the list of pipelines you have in that region and start a new pipeline, select ‘Create new pipeline.’
  • Enter your Pipeline name in the ‘Name’ column and add the description of your Pipeline in the DESCRIPTION column. 
  • To determine the source, choose “Build using a template” from which you should select “Getting started using ShellCommandActivity.”
  • The ‘Parameters’ column will open now. All you have to do is to have the default values of both ‘S3 input folder’ and ‘Shell command to run’ remain the same. Now press the icon that seems like a folder near the ‘S3 output folder’, choose the buckets and folders you need, and then press SELECT.
  • For scheduling the AWS Data Pipeline, you can either leave the default values, which makes the Pipeline run for every 15 minutes in an hour. Otherwise, you can choose “Run once on pipeline activation” to have this performed automatically. 
  • Under the column of ‘Pipeline Configuration,’ you need not change anything, but you can have the ‘logging’ either enabled or disabled. Form under the ‘S3 location for logs’ section, choose any one of your folders/ buckets, and press SELECT.
  • Make sure to set the IAM roles to ‘Default’ under the ‘Security/Access’ column. 
  • After ensuring everything is right, click ACTIVATE. If you need to add new preconditions or modify the existing Pipeline, select ‘Edit in Architect’.

Congratulations! You have successfully created an AWS Data Pipeline. 

Instructions to monitor the running AWS Data Pipeline:

  • You can view/monitor the working of AWS Data Pipeline on the ‘Execution details’ page, which will automatically be displayed on your screen after you start running your Pipeline. 
  • Now press UPDATE / F5 to refresh and receive the current status updates. In case of no currently running ‘runs’, check if the scheduled start is covered by ‘Start (in UTC)’ and the end of the Pipeline by ‘End (in UTC)’ after which you can press UPDATE.
  • When ‘FINISHED’ appears as the status of every object, it means that the Pipeline completed the scheduled tasks.
  • If there are incomplete tasks in your Pipeline, go to its ‘Settings’ and try to troubleshoot it.

How can I view the generated output?

  • Open Amazon S3 console and go to your ‘bucket / folder’.
  • There’ll be four subfolders with a name as “output.txt” and will be present only when the Pipeline runs for every 15 minutes in an hour.  

How can I delete a pipeline?

  • Go to the ‘List Pipelines’ page.
  • Choose the Pipeline you want to delete. 
  • Press ACTIONS and then click DELETE.
  • A confirmation dialogue box will be displayed. You will have to press DELETE to delete the Pipeline. 

These are the different steps involved in creating, monitoring, and deleting an AWS Data Pipeline. 


AWS Data Pipeline is a web server that provides services to collect, monitor, store, analyze, transform, and transfer data on cloud-based platforms. By using this Pipeline, one tends to reduce their money spent and the time-consumed in dealing with extensive data. With many companies evolving and growing at a rapid pace every year, the need for AWS Data Pipeline is also increasing. Be an expert in AWS Data Pipeline and craft a successful career for yourself in this competitive, digital business world. 

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Last updated: 03 Apr 2023
About Author

Prasanthi is an expert writer in MongoDB, and has written for various reputable online and print publications. At present, she is working for MindMajix, and writes content not only on MongoDB, but also on Sharepoint, Uipath, and AWS.

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