AWS Certified Data Engineer Associate DEA-C01 Practice Question
Your company ingests 5 TB of application logs each day into Amazon S3. Analysts run Amazon Athena queries that typically select 5-10 columns out of 200 and filter on date and region. Scan charges are rising and queries are slow. You will convert the data to an optimized format that minimizes data scanned, supports compression, and handles evolving schemas. Which format best meets these requirements?
Apache Parquet is a columnar storage format. Because Athena reads only the columns referenced in a query, converting the dataset to Parquet dramatically reduces the amount of data that must be scanned, lowering query cost and improving performance. Parquet also applies efficient compression and encoding to each column and supports schema evolution, making it well-suited for frequently changing log data. JSON Lines and CSV are row-based formats, so Athena would still scan every column in each file. Avro supports schema evolution but is also row-based, so it does not reduce the scanned data volume for column-subset queries.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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