AWS Data Pipeline Tutorial-NareshIT
AWS Data Pipeline Tutorial
Keywords: aws training, aws online training, best aws training institute, aws training in Hyderabad and USA, aws training in india, computer training institute, computer training institutes
Technology has evolved, and connectivity is at its peak with the rolling out of 5G. Petabytes of data get added each second. and they are forming big data mountains used for captive intelligence, which companies can make use of for expanding and bettering their working. However, deriving a value out of the data is not that easy. You need to transfer, arrange, filter, format, do the analysis, and finally report the data for deriving some value from the data. And they have to do this often and at pace for existing in the market. However, AWS Data Pipeline does it. It’s a perfect solution for the above problem. Naresh I Technologies is the best computer training institute in Hyderabad, and it is among the top five computer training institutes in India. For one of the best aws training in India, you can contact us. And you can contact us from any part of the world.
Let’s have a look at what we are going to cover in this article. We will first have e a look at the Need for an AWS data pipeline. Then we will see what it is and what its benefits are. Then we will have a look at its components, and finally, we will have a look at the demo part for winding up our tutorial.
So, what is the requirement for the AWS Data Pipeline?
You must have noticed data is growing at an exponential rate, and the speed is very high. All the companies are now finding the management, processing, storage, and migration of the data a complicated as well time taking issue now. They are now far more complex and time-consuming than what they used to be in the past. Hence, below are listed some of the problems. The companies are facing these due to data burst that the world is registering. A large amount of Data: You will find a lot of unprocessed data. They can be like log files, IoT data, demographic data, financial data, social media data, Scientific data, and much more.
Loads of formats: You will find data in various forms. And it takes time to convert the unstructured data to a compatible format, and this process is complicated.
Various data stores: There are large numbers of data stores. Some organizations have their data warehouses, or they have cloud storage like S3, RDS, and database servers running on the EC2 instances. And they can have their data on other cloud platforms like Azure, Google Cloud, and Oracle cloud as well
The process is time-consuming, and it costs: If you sit down to manage the big data, you need a lot of time, and it’s costly as well. You need to invest a lot of money in the transformation, storage, and processing of the data.
And all of these issues make the management of data quite complex. It’s hence challenging for the companies to perform those on their own. However, AWS Data Pipeline can be quite handy. It can easily integrate the data from various AWS services and then analyze one location. So let’s explore the components of the Data Pipeline.
- What is the AWS Data Pipeline?
AWS Data Pipeline web service can help you consistently process and transfer the data in between various AWS compute and storage services, and also on-premises data sources at pre-defined time intervals. Through the AWS Data Pipeline, you can in a simple manner and efficiently access the data from various locations where they are stored. Then it does the transformation and finally does the processing at scale. And it quite precisely transfers the outcomes to the AWS services like S3, RDS, DynamoDB, and EMR. It also leverages the option to create complicated data processing workloads that are seamless, repeatable, fault-tolerant, and readily and highly available.
Now, why should you select the AWS Data Pipeline?
Benefits:
- You have complete control of the computational resources.
- You are being provided with a drag and drop console.
- It’s inexpensive in use
- It’s configured over distributed and consistent, reliable infrastructure.
- It can perform the distribution of the overall workload to just one machine or as many “machines” as required.
- It comes with support for scheduling and error handling.
Now let’s have a lot of various components of the AWS Data Pipeline and how these components work as one unit for managing your data.
AWS Data Pipeline Components:
It’s a web service which you can make use of for automating the movement and do the data transformation. You can come up with workflows that such that the tasks can be dependent on the success of the previous “tasks.” You define the data transformation parameters, and the Pipeline applies the logic which you have built.
In general, you can begin pipeline designing through the selection of the data nodes. And the data pipelines work with the compute services for transforming the data. The majority of the time, quite a lot of extra data generates while we are in this step. Hence, as an option, you can output the data nodes, where you can store the transformation of data’s outcome and can access.
Data Nodes: In the AWS Data Pipeline, the data node describes the location and the data type which the Pipeline activity makes use of as output or input. The Pipeline supports the data nodes as below:
- DynamoDBDataNode
- SqlDataNode
- S3DataNode
- RedShiftDataNode
Now, let’s have a look at one real-time example for understanding each of the other components.
Use case: We will be collecting the data from various data sources, perform the Amazon EMR analysis, and finally come up with a weekly report.
Below, we will be designing a Pipeline for extracting the data from the data sources such as S3, DynamoDB, and perform the EMR analysis each day and finally come up with the weekly reports based on data
You need to know the activities. And we have an option of adding the preconditions to these activities.
Activities: It’s a component of Pipeline that defines the work required to perform based on schedule with the help of the computational resources, and it typically inputs and outputs the data nodes. Various examples of activities are as below:
- Transferring the data from one location to the other
- Executing Hive queries
- generating EMR reports.
Preconditions: It’s the pipeline component that contains the conditional statements which must be “true” for the activity to run.
- It checks whether the source data is present before the Pipelines activity attempts to duplicate it.
- Whether or whether not the corresponding database table is there or not.
Resources: The resource happens to be the computational resource that performs the work which the Pipeline activity states.
- An EC2 instance which does the task described by the Pipeline activity
- EMR cluster which does the task as described by the PA
And lastly, there is a component called actions.
Actions: The actions happen to be the steps that the pipeline component executes when an event takes place, such as a failure, success, or certain untimely activities.
- It sends an SNS notification to a topic based on failure, success, or untimely activity.
- Triggers the pending or not finished activity cancellation, resource, or the data node.
Now we have the basic knowledge of the AWS Data Pipeline and all its components. Let’s now have a look at how it works.
Demo:
In the demo part, we will see how we can copy the content of a DynamoDB table to the S3 Bucket. The AWS Data Pipelines triggers the actions for launching the EMR cluster through multiple EC2 instances. You need to make sure that you terminate these after the experimentation is complete, as it can incur a cost. The EMR cluster takes the data from the DynamoDB and then writes it on the “S3 bucket.”
Making an AWS Data Pipeline:
Step 1: You need to create a DynamoDB table with the help of data that you can pick for testing.
Step 2: Now make the “S3 bucket” for copying the data in the DynamoDB table.
Step 3: Now access the AWS Data Pipeline from the management console and finally click on Get Started for creating the data pipeline.
Step 4: Now make the data Pipeline You need to provide a “suitable name” for the “Pipeline” and an “elaborate description.” Now state the destination and the source data node paths. Mention the “DynamoDB read throughput ratio” also. Let it be “0.25.” And select a region. Then schedule the data pipeline and finally activate. You can set IAM roles as default and disable the logging.
Step 5: This is for monitoring and testing. Inside the list Pipelines, you will see the status: “Waiting for the runner.”
Step 6: In a few minutes, you will see the status as “Running.” And now, if you will follow the “EC2 console,” then you will find two fresh instances made automatically. It is since the “EMR cluster” is triggered by the “Pipeline.”
Step 7: Once you have finished, you need to access the “S3 bucket” and then find whether the .txt file was created or not. It has the content of the DynamoDB table. You need to download it and then open it in the text editor.
Hence, now you are familiar with how you can use “Data Pipeline” for data export from DynamoDB. And in the same manner, through vice versa with source-destination, you can import from S3 the DynamoDB data.
It completes our tutorial.
You can contact Naresh I Technologies for your aws online training. We provide aws training in Hyderabad and USA, and in fact, you can contact us from any part of the world through our phone or online form on our site. Just fill it and submit, and one of our customer care executives will be contacting you. And what else you get:
- Freedom to choose from AWS online training and classroom training.
- Chance to study from one of the best faculties and one of best aws training institute in India.
- Nominal fee affordable for all
- Complete training for all AWS services based on the certification you select
- Training for tackling all the nitty-gritty of AWS.
- Both theoretical and practical training.
- And a lot more is waiting for you.
You can contact us anytime for your aws training, and from any part of the world. Naresh I Technologies caters to one of the best aws training in India.
