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Answer: Use Amazon Kinesis for real-time data streaming, AWS Glue for ETL processing, and Amazon Redshift for data warehousing. Configure Kinesis to capture data in real-time, Glue to process data as it arrives, and Redshift to store and analyze the processed data.
Option B is the most suitable choice for the given scenario. Amazon Kinesis can handle high throughput and low latency for real-time data streaming. AWS Glue can process large volumes of data efficiently, and Amazon Redshift is a powerful data warehousing solution for storing and analyzing processed data. Configuring Kinesis to capture data in real-time, Glue to process data as it arrives, and Redshift to store and analyze the processed data ensures high throughput and low latency.
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You are tasked with designing an ETL pipeline for a large e-commerce company that needs to process millions of transactions daily. The pipeline should be able to handle high throughput and low latency. Which AWS services would you integrate to create this pipeline, and how would you configure them to meet the requirements?
A
Use AWS Glue for ETL processing, AWS Lambda for serverless computing, and Amazon S3 for data storage. Configure Glue to run on a schedule and Lambda to trigger on new data arrival.
B
Use Amazon Kinesis for real-time data streaming, AWS Glue for ETL processing, and Amazon Redshift for data warehousing. Configure Kinesis to capture data in real-time, Glue to process data as it arrives, and Redshift to store and analyze the processed data.
C
Use AWS Data Pipeline for scheduling and workflow management, AWS Lambda for serverless computing, and Amazon DynamoDB for data storage. Configure Data Pipeline to schedule Lambda functions based on dependencies, and Lambda to process data stored in DynamoDB.
D
Use Amazon S3 for data storage, AWS Step Functions for workflow management, and AWS Glue for ETL processing. Configure Step Functions to manage the workflow, Glue to process data stored in S3, and trigger the workflow based on a schedule.
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