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AWS Certified Solutions Architect - Associate · AWS
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C. Place activity data in an Amazon S3 bucket. Configure Amazon S3 to run an AWS Lambda function on the data as the data arrives in the S3 bucket. S3 + Lambda provides serverless ingestion, no servers to manage. S3 can store petabytes of data cheaply. Querying the data can be done with Amazon Athena, which allows SQL queries directly on S3 objects. Fully managed → least operational overhead. Supports highly available ingestion, as S3 is multi-AZ and Lambda scales automatically.
Why Option A works best: Amazon Kinesis Data Streams provides a durable, scalable, and highly available ingestion layer. Data is streamed to an Amazon S3 bucket, which: Supports virtually unlimited storage Is cost-effective for storing petabytes of data Once in S3, you can use: Amazon Athena to run on-demand SQL queries Amazon Redshift Spectrum or Amazon EMR for advanced analytics Fully managed, minimal maintenance, and scales automatically Why the other options are less ideal: B. Kinesis Data Firehose to Amazon Redshift ❌ Amazon Redshift is more expensive and suited for structured data with known schema ❌ Petabyte-scale data could require frequent manual scaling and maintenance ❌ Less cost-effective than S3 + Athena for large-scale, intermittent querying
Why this is the best solution: The requirements are: Highly available data ingestion, Supports petabyte-scale data, Enables on-demand analytics with SQL and Minimal operational overhead