AWS Data Pipeline

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.

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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.