What is AWS Data Pipeline? How its Works? and it’s Components

Everyone is getting online these days – businesses and people alike. This has brought in a data revolution, turning data into a priceless asset. A lot of data is being generated and consumed, which has a lot of potential for businesses. According to WEF, the amount of data generated daily is estimated to reach a whopping 463 Exabyte by 2025 globally.

Having realised that, businesses have started collating a ton of data to make informed business decisions. But the amount of data and organisation needed to turn that data into tangible knowledge has proved to be a major roadblock. Amazon, with its AWS Data Pipeline Service, has an answer to this dilemma.

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AWS Data Pipeline – What is it?

AWS data pipeline is a web service that addresses the problem of unmanageability of data, which runs into hundreds and thousands of gigabytes for a single organisation. It automates repetitive data handling tasks with the help of data-driven workflows.

Data can be reliably moved around and transformed into a legible format for further processing and analysis. Thus, the data flow from one point to another gets processed and reaches its destination, all according to a predefined chain of data dependencies, operations, and a given schedule.

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What are the Issues Addressed by AWS Data Pipeline?

1. Unmanageability of Bulk Data – Huge data becomes unmanageable, especially when one needs to perform operations on it daily. By scheduling all the regular tasks, the AWS Data pipeline makes it easier for the developers to handle data.

2. Exponentially Increasing Resource Requirements – Without the AWS Data pipeline, the cost of handling terabytes of data often surpasses the benefits of handling and processing that data.

3. Assemble the Data Coming in all Sorts of Formats – It has always been difficult to make sense of data when you have to combine data coming in from different sources in different formats. AWS solves the issue by facilitating the easy transformation of data.

4. Varied and Separated Data Storages – Collating data from various data storages is a cumbersome task. AWS data Pipeline integrates various sources of data storage, like the company’s own data warehouses, with various cloud services, making data more mobile and portable than it was ever in the past.

It is as a solution to these issues that the AWS data pipeline has gained a lot of popularity lately. It has both contributed to and benefitted from AWS’s market share of 31%, as reported by canalys reports, which is the highest among all the cloud services providers. To know more about its real-world applications, please refer to this informative upGrad Blog.

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SAAS Business – What is So Different?

1. Pipeline Definition

Data Nodes- The starting point of a pipeline is a data node. It represents the data we are using. Thus the type of data node being used depends on the AWS services like Amazon S3, RDS, etc., being used for storage purposes.

Precondition- A precondition is an optional sanity check which can be performed either on a data node or an activity. It is essentially like if-else conditions in computer programs. If the test runs successfully, only then the required operation is allowed. 

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Activity- An activity is any operation that a pipeline performs on the data according to the pipeline definition. All queries, scripts, and other jobs, come under this category.

Resources: Resources like Amazon EC2, EMR, etc., which are used to perform all the tasks.

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2. Task Runner

It checks the status of various tasks and runs them according to the pipeline definition.

How Does it Work?

First, the user has to define the data sources from which the data needs to be collected. Then, the schedule of the tasks, along with the data operations that have to be regularly performed, is also defined. Such definitions are contained in the pipeline definition. Amazon EC2 instances implement the activities defined in the given pipeline definition.

In-Demand Software Development Skills

Developers can use the AWS data pipeline to collect the data, perform backups, change formats, use transformations, and run custom scripts, converting the data into a state where it is easy to run analysis and reach conclusions. This happens regularly as per the schedule defined by the user. This reduces wastage of resources and addresses the inefficiency in the data operations when done with regular human intervention.

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Conclusion

Due to the benefits it brings, AWS, and thus AWS Data Pipeline, has been gaining solid ground in the job market. According to a report by virtualisation and Cloud Review, AWS job postings have seen a massive jump of 236.06% between October 2015 to October 2019, and it is nowhere near its saturation. This increasing popularity has resulted in the inclusion of AWS as an integral part of the curriculum of the Executive Post Graduation Program and Master Courses in Data Science and Machine learning, being offered by upGrad, in collaboration with IIIT-Bangalore and IIT Madras. Join today and see your career soar.