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AWS Data Pipeline

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Prerita Agarwal
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23 Jul, 2024 @ 01:30 PM

Introduction

In today’s world, technological advancements are increasing day by day. We are surrounded by technology and the data around us. Almost every technology works with data by analyzing the data and delivering the related content to the user.

And because of ease of connectivity and advancement in technologies, the amount of data generated and stored is skyrocketing. But how do companies give us the best result from this mountain of data? The answer is by using captive intelligence. Companies need to sort, analyze, move, and filter the data to select to derive its value.

They have to do this task repeatedly and speedily, so is there any way to reduce this repetition? The answer is yes, and we can automatically use AWS data pipelines to do this task. We will learn all about the AWS data pipeline while moving further in the blog so let’s get on with our topic without wasting anytime further.

Must read, Amazon Hirepro

AWS Data Pipeline and its Components

AWS Data Pipeline is a web service provided by Amazon. In this, you can define specific workflows or processes so that they only proceed after completing the previous task. Simply the output of the last task will be the input of the new task. In this, you will define and set the parameters and logic, and AWS Data Pipeline will use your logic to proceed with further tasks.

Need for AWS Data Pipeline

As we have seen, the data is growing exponentially and, in some scenarios, even faster than that. So if we perform manual or old operations, they will not perform according to our expectations. So we need AWS Data Pipelines to make sure we can tackle the following reasons or problems that most companies face nowadays.

  1. The bulk amount of data: There is a lot of unprocessed and raw data. The companies store the data from the servers, log files for the operations performed on the data, transactions history data, and a lot more data. To handle this amount of massive data, we need  AWS Data Pipeline.
  2. Variety of formats: The unprocessed or raw data can be available in multiple configurations. To sort that data and perform the operations will consume a lot of time and cost. So AWS Data Pipeline can reduce our efforts and time.
  3. Cost and Time: To manage all the data, store it, collect it, analyze it, and other operations will take so much time and cost, but AWS Data Pipeline can do this automatically.
  4. Different Data Storage Centers: Companies can Store data on various databases. Like some companies have their database, while others use others, like Amazon S3, to gather all the data from different sources and perform operations. It is not cost-friendly, but AWS Data Pipeline will do it regularly and cost-effectively.

Benefits of AWS Data Pipeline

In this blog section, we will discuss the benefits of AWS Data Pipeline that make it preferable to use.

  1. Flexible: It provides us with a different range of features like dependency pursuit, planning, and error handling. So it is flexible in performing various activities alone.
  2. Reliable: AWS Data Pipeline is reliable based on distributed and accessible infrastructure. It can handle failures and faults. If it detects any fault or failure in your daily activity, it mechanically retries the activity.
  3. Scalable: AWS Data Pipeline makes it simple to send work to at least one or more machines in parallel or serial. Process 1,000,000 files as quickly as one file with Amazon Data Pipeline's adaptable design.
  4. Economical: AWS pipeline is very cost-effective and can be subscribed to at a low monthly rate. And you can use it for free if you use it with AWS Free charge.
  5. Transparent: You have full management over the resources that execute your business logic Amazon S3 contains the full list of execution logs.

Components of AWS Data Pipeline

The AWS Data Pipeline web service allows you to automate data transportation and transformation. You can create data-driven workflows in which activities are reliant on prior tasks being completed successfully. AWS Data Pipeline enforces the logic you've set up by defining the parameters of your data transformations.

Choosing the data nodes is the first step in creating a pipeline. The data pipeline then transforms the data using computational services. During this process, a lot of supplementary data is usually generated. You can have output data nodes as an option, where the results of changing the data can be stored and accessible.

Data Nodes

In the AWS Data pipeline, a data node defines the type and location of the pipeline's data for input and output. It supports various data nodes like

  • S3DataNode
  • SqlDataNode
  • DynamoDBDataNode
  • RedShiftDataNode

Activities.

It is one of the main components of the AWS Data Pipeline. It defines the work on a schedule using typical output and input nodes and computational resources. Some examples of activities are:

  • Running Hive queries
  • Generating Amazon EMR reports
  • Moving data from one location to another

Preconditions

It contains the conditional statement that only allows activities to run and process if they are true:

  • If or not a respective database exists.
  • Checks if the data is present or not.

Resources

It performs the work specified by the activity component of the AWS Data Pipeline.

  • An Amazon EMR cluster performs the work defined by the pipeline.
  • An EC2 instance performs the work defined by activity.

Actions

These are the specific steps taken by the activity components to finish a specific task like a failure, success, or late activity.

  • Send an SNS notification to a topic-based failure or success.
  • Trigger the cancellation of unfinished or pending resource, activity, or data node.
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AWS Data Pipeline Features

There are various features and concepts present in AWS Data Pipeline. We will discuss all of them in this blog section.

Security in AWS Data Pipeline

AWS has security as its highest priority for cloud services. All the AWS customers get the advantages from different network architectures, and data centers made to fulfill various organizations' requirements. Responsibility is a two-way thing. One is on the server, and the other is on the server:

  • Security in the cloud: The user is also responsible for the AWS service. You are responsible for other factors like the responsibility of your sensitive data, applicable laws and regulations, and your company’s requirements.
  • Security of the cloud: This is the security provided by AWS. It is responsible for protecting the infrastructure where the AWS cloud is stored and running. It verifies the third party that is using the data.

Pipeline Expression and Functions

This section will learn about the syntax for using function and expression in the pipelines and different data types.

  1. DateTime

AWS Data pipeline supports date and time written in the format "YYYY-MM-DDTHH:MM:SS"

. Below is the example of the Date and time in the above format

"startDateTime": "2021-11-17T22:29:30"
  1. Numeric

AWS Data Pipeline supports both floating and integer point values.

  1. Object reference

Objects in the AWS Data Pipeline can be the current object, the name of another object defined elsewhere in the pipeline, or an object that lists the current object in a field addressed by the node keyword.

  1. Period

It indicates the time by which a scheduled event will run. It can run for a minimum of 15 minutes or a maximum of 3 years.

"Period": "5 hours"
  1. String 

While using string, make sure to surround them by (“), and to escape characters in the string, you can use the backslash character (\). And only single lines string is supported.

"Id": "Data Object"

Creating a Pipeline

The AWS Data Pipeline dashboard includes numerous templates, including pre-configured pipeline definitions. You may quickly get started with AWS Data Pipeline by using templates. You can also use parametrized values to generate templates. This enables you to define pipeline objects using parameters and pre-defined properties. Then, within the pipeline, you can utilize a tool to create values for a specific purpose. You can reuse pipeline definitions with different values this way. We need to follow the steps below to create and schedule a pipeline:

  1. Open the AWS Data Pipeline console.
  2. Click either Create Pipeline or Get started now.
  3. Please enter all the required descriptions for the pipeline, like its name, etc.
  4. Choose build using a template to select a template or select build using an architect to create and edit nodes.
  5. Now select the period to run the pipeline, either regular or on schedule.
  6. Select the option for IAM Roles. You can either select the default to assign its default values or select the custom option.
  7. Choose Activate or Edit in Architect.

Accessing AWS Data Pipeline

There are many interfaces available to access AWS Data Pipelines. Here is the list of some of them with an explanation of how to use them.

  • AWS SDKs: They provide language-specific APIs. They take care of connection details such as handling request retries, calculating signatures, and error handling.
  • AWS Command Line Interface: It gives the command to a vast set of AWS services, and it is supported by multiple operating systems like Windows, Linux, and macOS.
  • Query-API: You can use HTTPS to call low-level API. It is one of the most direct ways to access AWS Data Pipeline.
  • AWS Management Console: As the name suggests, it provides an interface to access the AWS Data Pipeline.

Frequently Asked Questions

What is the need to use AWS Data Pipeline?

It reduces the maintenance and development efforts required to manage your daily data operations.

Can a user use and supply his activities?

Yes, he can provide it by using Shell Command Activity.

Is there any default number of pipelines present for a new user?

Yes, for a new user, by default, a user can only access 100 pipelines.

Is there any limitation on the content that we can put on a single pipeline?

Yes, you can only put 100 objects in a single pipeline.

Conclusion

In this article, we have extensively discussed AWS Data Pipeline with a proper introduction, its features, need of pipeline, benefits of the pipeline, components of pipeline, features of AWS Data Pipeline, its security, different data types present in it, with step by step explanation of creating a pipeline.

We hope this blog has helped you enhance your knowledge of AWS Data Pipeline. If you want to know more about Amazon AWS, its benefits, features, and reasons you should use it and be certified in it, you must refer to this blog here. You will get a complete idea about all the features, benefits, and reasons you should be certified in Amazon AWS.

If you want to learn about Amazon Personalize, you should visit this blog. Here, you will get a complete idea about its features, use cases, and benefits.

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 “Happy Coding!”

Topics covered
1.
Introduction
2.
AWS Data Pipeline and its Components
2.1.
Need for AWS Data Pipeline
2.2.
Benefits of AWS Data Pipeline
2.3.
Components of AWS Data Pipeline
2.3.1.
Data Nodes
2.3.2.
Activities.
2.3.3.
Preconditions
2.3.4.
Resources
2.3.5.
Actions
3.
AWS Data Pipeline Features
3.1.
Security in AWS Data Pipeline
3.2.
Pipeline Expression and Functions
3.3.
Creating a Pipeline
3.4.
Accessing AWS Data Pipeline
4.
Frequently Asked Questions
4.1.
What is the need to use AWS Data Pipeline?
4.2.
Can a user use and supply his activities?
4.3.
Is there any default number of pipelines present for a new user?
4.4.
Is there any limitation on the content that we can put on a single pipeline?
5.
Conclusion