Which AWS service would you use for creating a serverless data processing pipeline?

Which AWS service would you use for creating a serverless data processing pipeline?

• A. AWS Batch
• B. AWS Lambda
• C. Amazon EMR
• D. Amazon RDS

Answer: (B)

Using AWS Lambda for creating a serverless data processing pipeline is an effective choice because it allows you to run code in response to events without the need for managing servers. Lambda automatically scales execution in response to incoming requests, so it can handle varying workloads efficiently and cost-effectively. In a data processing context, you can set up triggers from various data sources—like changes in S3 buckets or messages in an Amazon Kinesis stream—that invoke your Lambda functions to process the data in real-time or near real-time. This serverless architecture enables you to focus on writing the logic for your data processing tasks without needing to provision or manage the underlying infrastructure. Other options like AWS Batch, Amazon EMR, and Amazon RDS are not inherently serverless. AWS Batch processes jobs in managed batch computing environments but still requires some resource management. Amazon EMR, designed for big data frameworks, also involves clusters that require provisioning and management. Lastly, Amazon RDS is a managed relational database service that necessitates database instance management and doesn't fit the serverless model.

Using AWS Lambda for creating a serverless data processing pipeline is an effective choice because it allows you to run code in response to events without the need for managing servers. Lambda automatically scales execution in response to incoming requests, so it can handle varying workloads efficiently and cost-effectively.

In a data processing context, you can set up triggers from various data sources—like changes in S3 buckets or messages in an Amazon Kinesis stream—that invoke your Lambda functions to process the data in real-time or near real-time. This serverless architecture enables you to focus on writing the logic for your data processing tasks without needing to provision or manage the underlying infrastructure.

Other options like AWS Batch, Amazon EMR, and Amazon RDS are not inherently serverless. AWS Batch processes jobs in managed batch computing environments but still requires some resource management. Amazon EMR, designed for big data frameworks, also involves clusters that require provisioning and management. Lastly, Amazon RDS is a managed relational database service that necessitates database instance management and doesn't fit the serverless model.