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AWS Data Pipeline Tutorial

What is AWS?

AWS stands for Amazon Web Services. It is a cloud computing platform that provides versatile, dependable, scalable, user-friendly, and cost-effective cloud computing solutions.

AWS is a comprehensive computing platform provided by Amazon. The platform is built using a combination of infrastructure as a service (IaaS), platform as a service (PaaS), and packaged software as a service (SaaS) solutions.

This blog on AWS Data Pipeline will provide you with a thorough understanding of the following:

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Alright!! So, let’s get started with the AWS Data Pipeline Tutorial

What is AWS Data Pipeline?

AWS Data Pipeline is a web service that allows you to process and transport data between AWS computing and storage services, as well as on-premises data sources, at predefined intervals.

With AWS Data Pipeline, you can easily access data from wherever it is stored, transform and process it at scale, and efficiently transfer the results to AWS services such as Amazon RDS, Amazon EMR, Amazon S3, and Amazon DynamoDB.

It enables you to create complex data processing workloads that are fault-tolerant, repeatable, and highly available.

What is AWS Data Pipeline?

For example, you can create a Data Pipeline that extracts event data from a data source on a daily basis and then runs an Amazon EMR (Elastic MapReduce) on the data to generate EMR reports.

A tool like AWS Data Pipeline is required because it allows you to transport and convert data that is dispersed across several AWS products while also allowing you to monitor it from a single spot.

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Features of AWS Data Pipeline

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AWS Data Pipeline Components

AWS Data Pipeline is a web service that allows you to automate the transport and transformation of data. You may create data-driven workflows in which tasks are reliant on the successful completion of preceding activities.

You can determine the parameters of your data transformations, and AWS Data Pipeline enforces the logic you’ve defined.

Basically, you always start constructing a pipeline with the data nodes. The data pipeline is then used in combination with computation services to transform the data.

Normally, a large amount of additional data is created throughout this procedure. So, optionally, you can have output data nodes where the results of data transformation can be stored and accessible.

Data Nodes: A data node determines the location and kind of data that a pipeline activity utilizes as input or output in the AWS Data Pipeline. It supports data nodes such as

Now, explore a real-world example to better understand the other components.

Use Case: Collect data from various data sources, analyze it using Amazon Elastic MapReduce (EMR), and create weekly reports.

In this use case, we are developing a pipeline to harvest data from data sources such as Amazon S3 and DynamoDB in order to do EMR analysis daily and provide weekly data reports.

The terms that are highlighted are now known as activities. We can optionally add preconditions for these actions to run.

Activities: An activity is a pipeline component that describes the task to be completed on time utilizing a computing resource and often input and output data nodes. Activities include the following:

Preconditions: Preconditions are pipeline components that include conditional statements that must be true before an action can be performed.

Resources: A resource is a computing resource that executes the task specified by a pipeline activity.

Finally, there is a component known as actions.

Actions: Actions are the steps taken by a pipeline component when specific events occur, such as success, failure, or late activities.

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AWS Pipeline VS AWS Glue

Differences AWS Pipeline AWS Glue
Infrastructure Management   AWS Data Pipeline is not serverless in the same way that Glue is. It starts and controls the lifetime of EMR clusters and EC2 instances used to run your tasks. AWS Glue is serverless, meaning there is no infrastructure to manage for developers. In Glue’s Apache Spark environment, scaling, provisioning, and configuration are all completely controlled.
Operational Methods   AWS Data Pipeline allows you to make data transformations using APIs and JSON, however, it only supports DynamoDB, SQL, and Redshift. AWS Glue supports Amazon S3, Amazon RDS, Redshift, SQL, and DynamoDB, as well as built-in transformations.
Compatibility   AWS Data Pipeline is not limited to Apache Spark and lets you utilize other engines such as Pig, Hive, and others. AWS Glue to execute your ETL operations in a serverless Apache Spark environment using its virtual resources.

Benefits of AWS Data Pipeline

AWS Data Pipeline Pricing

AWS Data Pipeline costs a monthly fee of $1 per pipeline if it is operated more than once per day and $0.68 per pipeline if it is run once or less per day. You should also pay for EC2 and any other resources that you use.


AWS Data Pipeline is a better substitute for implementing ETL operations without the requirement for a separate ETL infrastructure. The crucial thing to remember here is that ETL should only use AWS components. The experience of using AWS may be rewarding. It may greatly assist enterprises in automating data flow and transformation.

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