AWS Data Pipeline Tutorial
Updated on 28th Sep, 22 13 Views

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 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?

The web service AWS Data Pipeline enables you to process and move data across AWS compute and storage services, as well as on-premises data sources, at set intervals.

  • You can quickly retrieve data from wherever it is housed, alter it, analyze it at scale, and then send the results to AWS services like Amazon RDS, Amazon EMR, Amazon S3, and Amazon DynamoDB.
  • With its help, you can develop fault-tolerant, predictable, and easily deployable complicated data processing workloads.
What is AWS Data Pipeline?

As an illustration, you might create a data pipeline that automatically collects event data from a source of data and utilizes it to run an Amazon EMR (Elastic MapReduce) to generate EMR reports.

It is necessary to utilize a tool like AWS Data Pipeline because it enables you to transport and modify data that is dispersed among several AWS solutions while also enabling you to monitor it from a single location.

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AWS Data Pipeline Components

  • An online solution called AWS Data Pipeline enables you to automate data transmission and transformation.
  • You may design data-driven workflows where tasks depend on the success of earlier actions being completed.
  • The parameters of your data conversions are up to you, and AWS Data Pipeline upholds the established logic.
  • Basically, the data nodes are where you always begin building a pipeline.
  • The data is subsequently transformed using the data pipeline in conjunction with computing services.
  • Throughout this process, a lot of extra data is often produced.
  • In order to store and make accessible the outcomes of data transformation, output data nodes are optional.
  • Data Nodes:

In the AWS Data Pipeline, a data node identifies the location and type of data that a pipeline activity will use as input or output.

It enables data nodes such as follows:

  • S3DataNode
  • SqlDataNode
  • DynamoDBDataNode
  • RedshiftDataNode

To further comprehend the other components, let’s examine a real-world scenario;

  • Use Case:
    • Gather information from numerous sources, use Amazon Elastic MapReduce (EMR) to analyze it, and provide weekly reports.
    • In order to do daily EMR analysis and deliver weekly data reports, we are creating a pipeline to harvest data from data sources including Amazon S3 and DynamoDB.
    • The words in the highlighted section are now referred to as activities. Prerequisites for carrying out these tasks are optional.
  • Activities:

Using a computational resource and often input and output data nodes, an activity is a pipeline component that explains the work to be accomplished on time.

Activities include the following:

  • Making Amazon EMR reports
  • Executing Hive queries
  • Data transfer from one site to another
  • Preconditions:
    • Preconditions are pipeline elements that include conditional assertions that must be true before an action is carried out.
    • Make sure the source data is there before attempting to transfer it.
    • If a certain database table is present or not.
  • Resources:
    • The job indicated by a pipeline activity is carried out by a resource, which is a computer resource.
    • A pipeline activity-compliant EC2 instance that finishes the tasks it was assigned.
    • A cluster of Amazon EMR servers that finishes the tasks listed in a pipeline activity.

The component known as actions is the last one.

  • Actions:

When certain events, such as success, failure, or late activities, occur, a pipeline component will take action.

  • Based on an activity’s success, failure, or tardiness, send the subject an SNS notification.
  • Allow the cancellation of an ongoing or unfinished activity, resource, or data node.

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AWS Data Pipeline Pricing

  • AWS Data Pipeline has a $1 per pipeline monthly payment if used more usually than once per day and $0.68 per pipeline if it is run once or less per day.
  • Additionally, you must purchase EC2 and any additional services you employ.

Benefits of AWS Data Pipeline

  • This feature offers a drag-and-drop console within the AWS UI.
  • It makes it simple to divide work between one or more machines, in serial or parallel mode.
  • The scalable, highly flexible infrastructure of AWS Data Pipeline is tailored for fault-tolerant activity execution.
  • AWS Data Pipeline With a little monthly subscription, it is a cheap service.
  • It gives you total control over the computational resources that are used to perform your data pipeline logic.
  • It features functions including dependency tracking, scheduling, and error handling

AWS Pipeline VS AWS Glue

DifferencesAWS PipelineAWS Glue
Infrastructure ManagementWhen compared to Glue, AWS Data Pipeline is not serverless. EMR clusters and EC2 instances required to carry out your tasks are started and maintained by it throughout their lives.Since  AWS Glue is serverless, developers don’t need to worry about managing infrastructure. Scaling, provisioning, and configuration are all fully managed in the Apache Spark environment provided by Glue.
Operational MethodsThe only databases that AWS Data Pipeline supports are DynamoDB, SQL, and Redshift, although you may change data using APIs and JSON.Amazon S3, Amazon RDS, Redshift, SQL, DynamoDB, and built-in transformations are all supported by AWS Glue..
CompatibilityAWS Data Pipeline allows you to use other engines in addition to Apache Spark, including Pig, Hive, and others.Your ETL processes will be carried out by AWS Glue using the virtual resources of Apache Spark in a serverless environment.

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Features of AWS Data Pipeline

  • Since AWS Data Pipeline gives you total control over the computational resources used to run and execute your business logic, troubleshooting and changing your data processing logic is a straightforward procedure.
  • It is designed with high fault tolerance and availability. It can therefore effectively execute, track, run, and monitor your processing processes.
  • It is remarkably adaptive.
  • You can develop your own conditions or activities and/or use the pre-existing ones to take use of platform features like scheduling, error handling, and many more. so forth.
  • It supports a broad variety of data sources, including on-premises and AWS.
  • It enables you to define activities like SQLActivity, which executes a SQL query on a database, HiveActivity, which executes a Hive query on an EMR cluster, PigActivity, which executes a Pig script on an EMR cluster, and EMRActivity, which executes an EMR cluster to assist you in processing or transforming your data in the cloud.

Conclusion

When it comes to carrying out ETL operations without the need for a separate ETL infrastructure, AWS Data Pipeline is a preferable option.

Here, it’s crucial to be aware that only AWS Data Pipeline should be used for ETL. It would be advantageous to leverage AWS Data Pipeline.

It may substantially help businesses automate data flow and transformation.

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