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.
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Following is the list of topics covered in this AWS Data Pipeline tutorial:
Now, let’s get to know AWS Data Pipeline better.
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:
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.
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:
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
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.
There are six significant benefits of AWS Data Pipeline, and they are:
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:
Pipeline: There are three main components for a Pipeline, and they are:
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:
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.
These are the different system-managed preconditions.
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.
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 https://cutt.ly/dyDpNKC 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.
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.
You can create an AWS Data Pipeline either through a template or through the console manually.
Congratulations! You have successfully created an AWS Data 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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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.