DEA-C01 · Question #262
A company wants to use Apache Spark jobs that run on an Amazon EMR cluster to process streaming data. The Spark jobs will transform and store the data in an Amazon S3 bucket. The company will use Amaz
The correct answer is B. Use ORC format. Use AWS Glue Data Catalog to track schema changes. D. Use Apache Parquet format. Use AWS Glue Data Catalog to track schema changes.. ORC is a columnar format optimized for Athena and provides high compression and predicate pushdown for faster queries; Glue Data Catalog manages schema evolution. Parquet is a columnar format optimized for Athena with efficient compression and predicate pushdown; Glue Data Catalo
Question
A company wants to use Apache Spark jobs that run on an Amazon EMR cluster to process streaming data. The Spark jobs will transform and store the data in an Amazon S3 bucket. The company will use Amazon Athena to perform analysis. The company needs to optimize the data format for analytical queries. Which solutions will meet these requirements with the SHORTEST query times? (Choose two.)
Options
- AUse Avro format. Use AWS Glue Data Catalog to track schema changes.
- BUse ORC format. Use AWS Glue Data Catalog to track schema changes.
- CUse Apache Parquet format. Use an external Amazon DynamoDB table to track schema
- DUse Apache Parquet format. Use AWS Glue Data Catalog to track schema changes.
- EUse ORC format. Store schema definitions in separate files in Amazon S3.
How the community answered
(57 responses)- A14% (8)
- B72% (41)
- C11% (6)
- E4% (2)
Explanation
ORC is a columnar format optimized for Athena and provides high compression and predicate pushdown for faster queries; Glue Data Catalog manages schema evolution. Parquet is a columnar format optimized for Athena with efficient compression and predicate pushdown; Glue Data Catalog tracks schema changes.
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