A company runs an online marketplace web application that serves hundreds of thousands of users at peak. The company needs a scalable, near-real-time solution to share millions of financial transactions with several internal applications. Transactions must be processed to remove sensitive data before being stored in a document database for low-latency retrieval. What should a solutions architect recommend?
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Correct answer: Stream the transactions data into Amazon Kinesis Data Streams, use AWS Lambda integration to remove sensitive data from each transaction, then store the transactions data in Amazon DynamoDB, and let other applications consume the transactions data from the Kinesis data stream..
Why this is the answer
The correct solution leverages Kinesis Data Streams for scalable, near-real-time ingestion. AWS Lambda integrates directly with Kinesis Data Streams to process each transaction individually, allowing for sensitive data removal before storage. The processed data is then stored in DynamoDB for low-latency retrieval, meeting
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