AWS Certified Data Engineer Associate DEA-C01 Practice Question
An e-commerce company ingests 20 GB of click-stream logs every 15 minutes into AWS. An AWS Glue Spark job will convert the logs from JSON to partitioned Parquet before loading them into Amazon Redshift. The solution must minimize cost, decouple ingestion from transformation jobs, and scale automatically with data volume without manual capacity management. Where should the logs be placed as the intermediate staging location?
Store the raw logs in an Amazon S3 bucket that serves as the landing zone.
Load the logs directly into Amazon Redshift staging tables in a separate schema.
Write the logs into an Amazon DynamoDB table using binary (BLOB) attributes.
Persist the logs on an Amazon EFS file system mounted on an ingestion EC2 instance.
Amazon S3 is designed for virtually unlimited capacity, 99.999999999% durability, and high throughput without provisioning storage or IOPS. Landing raw data in S3 creates a low-cost, durable buffer between ingestion and downstream processing so that AWS Glue jobs can read the data in parallel whenever scheduled. Redshift tables are not intended for storing large raw files and would tightly couple ingestion to analytics. DynamoDB is optimized for key-value items up to 400 KB, making it unsuitable for multi-gigabyte log files. Amazon EFS would require mounting and managing throughput classes, resulting in higher cost and administrative overhead compared with S3.
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Why is Amazon S3 recommended as a landing zone for raw logs?
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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