Understanding AWS Data Pipeline

Today with the advancement of technology and easy network connectivity, companies are producing a huge amount of data. Every day some millions of data are produced and stored for further processing. Companies need to sort, filter, store, analyze, and report data to get meaningful insights from it. Besides, this is a repetitive task and to do rapidly to stay ahead in the competition. AWS Data Pipeline is a perfect solution which is a kind of internet service from Amazon. A data pipeline is a set of actions that takes raw data from different sources and move data from an application to the data warehouse for storage and analysis. 

Moreover, a data pipeline includes a series of data processing steps that enables a flow of data to reach its destination. Here each step delivers an output which in return is an input for the next step. This series continues till the pipeline completes. Further, it consists of three important elements such as source, processing steps, and target. There are different tools available for storing different data formats but Data Pipeline is a feasible solution for this. It helps to integrate data from across locations and processes it at the same place. 

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Let us discuss more on the AWS data pipeline in detail further in this blog.

What is the AWS data pipeline?

AWS Data Pipeline is nothing but a web or internet service from Amazon cloud that helps to automate the data movement process. It helps to access data from multiple sources and analyze, process, transform the same from storage. Moreover, it uses different AWS services to store the data after the process such as DynamoDB, Amazon S3, AWS RDS, etc. 

Behind the data pipeline logic, the AWS data pipeline offers you a feasible drag-drop UI. This facilitates users to have full control of different computational resources. 

Moreover, the AWS data pipeline helps to manage and streamline many data-driven workflows easily. Hence, if a user specifies the parameters of his data for changing, the AWS data pipeline enforces the same logic. 

In simple words, this is an AWS service that helps users to transfer data to the AWS cloud. This is through defining, planning, and automating each task. 

AWS data pipeline is a very useful tool because it helps to transfer and change data that is spread across different AWS tools. Also, it allows users to observe the data from a single location. 

Features of AWS Data Pipeline

There are many useful features of the AWS data pipeline that allows users to deal with data workflows.

• It helps users to debug/change their data workflow logic easily for which enables full control over the computable resources. This helps to execute your business logic in a scalable way. 
• Moreover, this data pipeline is very flexible to use where a user can write his own conditions. He can also make use of the existing built-in activities that may benefit him well with good features. Such as planning, exception handling, etc. 
• This pipeline also offers support to different types of data sources starting from AWS to on-premise data. 
• The architecture of the AWS data pipeline is highly scalable, highly available, and has the fault-tolerance capability. Therefore, it actively runs and observes the different processing activities of the pipeline. 
• Moreover, the AWS data pipeline is useful to tackle different issues relating to growing data. Most of the data is unprocessed/raw and companies face to process such data which is difficult.
• Data is available in different formats which is difficult to transform and use. But this data pipeline helps in this regard very much to change and use the same data. 

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Advantages of AWS Data Pipeline

Let us look into the various advantages of the AWS data pipeline in the following points.

• The data pipeline in AWS is very easy and simple to use. It offers you a flexible drag-drop console with AWS UI to draw your pipeline easily without much effort. You also dont need to write complex business logic to build a successful pipeline. 
• Due to its reliable and distributed infrastructure with high availability, it helps you in the creation of a data pipeline. Also, in case of any fault or failure in the creation, the AWS data pipeline service restarts the activity.
• It is very flexible in nature that supports different features like planning, dependency tracking, and handling errors. 
• Moreover, a data pipeline can execute various tasks like running Amazon EMR, execution of SQL queries against the database, etc. There are also executing custom apps on the EC2 instances, etc. It allows you to create custom pipelines that help you to investigate and process your data.
• Further, this data pipeline makes it easier for dispatching the work to one or different systems in a series. 
• The data pipeline in AWS is very less expensive and built at a low monthly rate on average. So everyone can take the advantage of this data pipeline. 
• Moreover, it provides full control over the various computational resources that implement the user’s data pipeline logic. 

Hence, these are the major benefits that a user can get using the AWS data pipeline. 

AWS Data Pipeline Components

The below components of AWS Data Pipeline work together to manage user data extremely.

There are different components of this data pipeline which are:-

The pipeline includes different information that mentions how business logic contacts the data pipeline. It always begins with Data Nodes to draw a simple data pipeline. 

Data Nodes

The Data Nodes within Amazon Data Pipeline define the location, name, and type of data sources like Amazon S3. Dynamo DB, RDS, SQL, etc. 

Activities

It also includes Activities which are the actions that execute the SQL Queries on the database. Also, it changes the data from one data source to another. The activities include producing Amazon EMR reports, running Hive queries, data movement, etc. 

Another component Scheduling is executed on the activities. 

Preconditions

In order to schedule activities, there are certain Preconditions that need to satisfy. These are some of the components of a data pipeline. These are like conditional statements that must be true before you run the activity. 

Here, the conditions check that whether the source data is available before a pipeline activity tries to copy. Also, it requires checking the respective data table is present or not. 

Resources

Further, there is a computational Resource that executes the task that the workflow pipeline mentions. 

Finally, Actions is the component that updates the data pipeline status. It acts when there is a certain activity that occurs like whether it is failed, success, or it became late.

Hence, this is about the different AWS data pipeline components and their uses. Let us discuss more on these pipelines. 

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How to create a data pipeline?

By following the below steps, you can easily create an AWS data pipeline.

At first, you need to sign in to the AWS Management Console.

Then you have to build the Dynamo DB Table and two S3 buckets. 

Here to create a new table, you should give the details like table name, primary key, etc.

After creating a table, you will have an Overview of that then click on the Items option and create an Item. You can add details like iD, Name, gender, etc items. 

Now your data is entered into the dynamo DB table successfully. 

Then comes the creation of Two S3 buckets where you will store the data in the first one and the second one will have the Logs. 

Now it’s time to create the Data Pipeline for which you have to move to the Data Pipeline Service. After entering the service platform you need to click the Get Started option there. 

Then you should fill in the necessary details to create the data pipeline. You can also edit the pipeline data you entered through Edit on Architect option. Here, as you enter, you can see a warning comes in TerminateAfter is Missing. 

To remove the warning, you have to add a new field with the name “TerminateAfter” within the Resources. Then after adding the field you need to click on Activate button to activate it.

Observing and Testing pipeline

Now there is an option List Pipelines under which you can see the status “Waiting for Runner”. 

Then after within a few minutes, it changes to “running”. Here, if you go to the EC2 console, then you can that Two new instances are automatically created there. This happens due to the EMR cluster started by the data pipeline.

By the end, you can easily access the S3 bucket and also locate that whether any.txt file created or not. This file contains the DynamoDB table contents so that you can download the same in a text editor. 

Now that’s all you got the idea of creating an AWS data pipeline and use it for exporting data from DynamoDB.

Use of AWS Dat Pipeline

Let us check the different uses of AWS Data Pipeline. 

Transferring ETL data to RedShift

First, you need to copy DynamoDB data to the S3 bucket, then you remodel it, run the analytics using SQL queries. Finally, you shift the data after the process to Amazon Redshift.

ETL unstructured or Raw data

You have to analyze the raw data like the logs using Hive/Pig on EMR. Now mix the data with structured data from RDS and transfer the same to Amazon RedShift.

Then load the log files of AWS log data from S3 to Amazon Redshift. After that, you can move the data for storage to the Cloud platform. 

Then, you periodically backup and recover table data to AWS S3.  

Conclusion

This is all the basic idea you need to know about AWS Data Pipeline and its usage. I hope you got to know AWS Data Pipeline in detail and its usage depends upon the frequency. There is a high frequency and low frequency of activities running on AWS. This pipeline helps many corporate users very much to sort, filter, analyze, and store data in a secure way. 

The data pipeline is useful for many reasons that process the data workflow through different activities. It also stores data for further use and supports different data sources. To know more about this segment get into AWS Training with the ITGuru platform through industry experts.