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A company wants to optimize the cost of its data and analytics platform. The company is ingesting a number of .csv and JSON files in Amazon S3 from various data sources. Incoming data is expected to be 50 GB each day. The company is using Amazon Athena to query the raw data in Amazon S3 directly. Most queries aggregate data from the past 12 months, and data that is older than 5 years is infrequently queried. The typical query scans about 500 MB of data and is expected to return results in less than 1 minute. The raw data must be retained indefinitely for compliance requirements. Which solution meets the company’s requirements?
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Question 114 - DAS-C01 discussion

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A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as .csv files that are stored in Amazon S3. The company’s analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall data. The amount of data that is ingested into Amazon S3 has increased substantially over time, and the query latency also has increased.

Which solutions could the company implement to improve query performance? (Choose two.)

A.
Use MySQL Workbench on an Amazon EC2 instance, and connect to Athena by using a JDBC or ODBC connector. Run the query from MySQL Workbench instead of Athena directly.
Answers
A.
Use MySQL Workbench on an Amazon EC2 instance, and connect to Athena by using a JDBC or ODBC connector. Run the query from MySQL Workbench instead of Athena directly.
B.
Use Athena to extract the data and store it in Apache Parquet format on a daily basis. Query the extracted data.
Answers
B.
Use Athena to extract the data and store it in Apache Parquet format on a daily basis. Query the extracted data.
C.
Run a daily AWS Glue ETL job to convert the data files to Apache Parquet and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data on a daily basis.
Answers
C.
Run a daily AWS Glue ETL job to convert the data files to Apache Parquet and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data on a daily basis.
D.
Run a daily AWS Glue ETL job to compress the data files by using the .gzip format. Query the compressed data.
Answers
D.
Run a daily AWS Glue ETL job to compress the data files by using the .gzip format. Query the compressed data.
E.
Run a daily AWS Glue ETL job to compress the data files by using the .lzo format. Query the compressed data.
Answers
E.
Run a daily AWS Glue ETL job to compress the data files by using the .lzo format. Query the compressed data.
Suggested answer: B, C

Explanation:


Reference: https://www.upsolver.com/blog/apache-parquet-why-use https://aws.amazon.com/blogs/big-data/work-withpartitioned-data-in-aws-glue/

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asked 16/09/2024
Tyrika Warren
39 questions
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