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Answer: Use Amazon S3 for data storage and AWS Lambda for data processing, supporting various data formats through custom processing logic
In this scenario, using Amazon S3 for data storage and AWS Lambda for data processing would be the most suitable choice. Amazon S3 can handle large volumes of data and supports various data formats. By using AWS Lambda, you can implement custom processing logic to handle the different data formats generated by the social media application. This approach provides flexibility and scalability for handling diverse data types in the data ingestion pipeline.
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You are designing a data ingestion pipeline for a social media application that generates a large volume of user-generated content, including text, images, and videos. The pipeline should be able to handle high data volumes and support various data formats. Which AWS service would you recommend for this scenario, and how would you design the pipeline to handle the different data formats?
A
Use Amazon S3 for data storage and AWS Lambda for data processing, supporting various data formats through custom processing logic
B
Use Amazon DynamoDB for data storage and AWS Lambda for data processing, using DynamoDB's native support for different data formats
C
Use Amazon Kinesis for data ingestion and processing, supporting various data formats through Kinesis Data Analytics
D
Use Amazon Redshift for data storage and AWS Glue for data processing, using Redshift's support for different data formats
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