How to Build a AWS Data Pipeline?
Last Updated :
09 Dec, 2024
Amazon Web Services (AWS) is a subsidiary of Amazon offering cloud computing services and APIs to businesses, organizations, and governments. It provides essential infrastructure, tools, and computing resources on a pay-as-you-go basis. AWS Data Pipeline is a service that allows users to easily transfer and manage data across AWS services (e.g., S3, EMR, DynamoDB, RDS) and external sites. It supports complex data processing tasks, error handling, and data transfer, enabling reliable, scalable data workflows.
Workflow of AWS Data pipeline
To access the AWS data pipeline first we have to create an AWS account on the website.
- From the AWS webpage, we have to go to the data pipeline and then we have to select the ‘Create New Pipeline’.
- Then we have to add personal information whatever it has asked for. Here we have to select ‘Incremental copy from MYSQL RDS to Redshift.
- Then we have to write all the data which are asked in the parameters for RDS MYSQL details.
- Then arrange the Redshift connection framework.
- We have to schedule the application to run or we can access it for one time run through activation.
- After that, we have to approve the logging form. This is very useful for troubleshooting projects.
- The last step is just to activate it and we are ready to use it.
Components of AWS Data Pipeline
The AWS Data Pipeline Definition specifies on how business teams should communicate with the Data Pipeline. It contains different information:
- Data Nodes: These specify the name, position, and format of the data sources similar to Amazon S3, Dynamo DB, etc.
- Conditioning: Conditioning is the conduct that performs the SQL Queries on the databases, and transforms the data from one data source to another data source.
- Schedules: Scheduling is performed on the Conditioning.
- Preconditions: Preconditions must be satisfied before cataloging the conditioning. For illustration, if you want to move the data from Amazon S3, also precondition is to check whether the data is available in Amazon S3 or not.
- Facility: You have Resources similar to Amazon EC2 or EMR cluster.
- Conduct: It updates the status of your channel similar to transferring a dispatch to you or sparking an alarm.
- Pipeline factors: We’ve formerly bandied about the pipeline factors. It is principally how you communicate your Data Pipeline to the AWS services.
- Cases: When all the pipeline factors are collected in a channel(pipeline), also it creates a practicable case that contains the information of a specific task.
- Attempts: Data Pipeline allows, retrying the operations which are failed. These are nothing but Attempts.
- Task Runner: Task Runner is an operation that does the tasks from the Data Pipeline and performs the tasks.
Create AWS Data Pipeline: A Step-By-Step Guide
Accessing of AWS Data Pipeline involves several key steps those discussed as follows. Here we discussed an effective and streamlined workflow of data processing.
Step 1: Login to AWS Console
- Firstly, login in to your AWS Console and login with your credentials such as username and password.
Step 2: Creating a NoSQL Table Using Amazon DynamoDB
Step 3: Navigate to S3 Bucket
- After creating Database using DynamoDB create S3 Bucket and make sure both s3 and Database are in same Region
- To Create S3 Bucket Please Refer To Our article Amazon S3 – Creating a S3 Bucket
Step 4: Navigate to Data Pipeline
- After directing to the Data Pipeline page, now create a new pipeline or select an existing one from the list of pipelines displayed in the console.
Step 4: Define Pipeline Configuration
- Define the configuration of the pipeline by specifying the data sources, activities, schedules and resources that are needed and define them as per requirements.
- Configure the individual components of the pipeline by specifying the details such as input or output locations, resource requirements and processing logic.
Step 6: Activate Pipeline
- Now, activate the pipeline for initiating the workflow execution according to defined schedule or trigger conditions.
Step 7: Check Text File Delivered In S3 Bucket
- Locate to Manifest file in S3 Bucket
Pros
- It is easy to use the control panel with the structured templates which are provided for AWS databases mostly.
- It is capable of generating the clusters and the source whenever the user needs it.
- It can organize jobs when the time is scheduled.
- It is secured to access, the AWS portal controls all the systems and is organized like that only.
- Whenever any data recovery occurs it helps in recovering all the lost data.
Cons
- It is designed mainly for the AWS environment. AWS-related sources can be implemented easily.
- AWS is not a good option for other third-party services.
- Some bugs can occur while doing several installations for managing cloud computing.
- At first, it may seem difficult and one can have trouble while using these services for the starters.
- It is not a beginner-friendly service. The beginners should have proper knowledge while starting using it.
Build a AWS Data Pipeline – FAQs
Can I use AWS Data Pipeline with other AWS services?
Yes, AWS Data Pipeline integrates seamlessly with many AWS services, such as:
- Amazon S3 for storing data.
- Amazon EC2 for running processing jobs.
- Amazon Redshift for data warehousing.
- AWS Lambda for serverless data processing. It enables you to create powerful workflows that automate data processing and movement across AWS.
How do I troubleshoot issues in AWS Data Pipeline?
When troubleshooting AWS Data Pipeline, you can:
- Check the CloudWatch logs for detailed error messages and activity logs.
- Use AWS CloudTrail to track API calls and identify any access-related issues.
- Review the pipeline’s activity logs for failures or delays.
- Ensure that IAM roles and permissions are properly configured for all involved resources.
What are AWS Data Pipeline Templates and how do I use them?
AWS Data Pipeline offers pre-built templates to simplify common use cases such as moving data to and from Amazon S3, processing data with EC2, or copying data to Redshift. These templates provide a structured way to set up pipelines quickly without building from scratch. Simply choose a template, customize it to your needs, and deploy it
Can AWS Data Pipeline be used for real-time data processing?
AWS Data Pipeline is primarily designed for batch processing and scheduled workflows. However, it can be part of a real-time data processing architecture when used alongside other services like AWS Kinesis for real-time streaming or AWS Lambda for serverless processing. For true real-time processing, consider integrating these services with your pipeline
Can I create multiple AWS Data Pipelines for different workflows?
Yes, you can create multiple independent data pipelines within your AWS account. Each pipeline can have different data sources, destinations, schedules, and processing steps, allowing you to manage multiple data workflows efficiently. AWS Data Pipeline provides full flexibility for handling diverse data processing needs.