DEA-C01 Question 454
Select 3You are working as a data engineer for a retail company that processes large volumes of transactional data daily. The data is ingested into an Amazon S3 data lake. To enable efficient querying and reduce costs, your team decides to implement a solution to optimize the data storage format and partitioning. Which combination of actions should you take to achieve this?
- A
Convert the data to Parquet format before storing it in Amazon S3
- B
Use AWS Glue to create metadata tables in the AWS Glue Data Catalog
- C
Use Amazon S3 Transfer Acceleration to speed up data retrieval
- D
Partition the data based on frequently queried fields like 'region' or 'date'
- E
Enable versioning on the S3 bucket to maintain historical data changes
Show answer and explanation
Correct answers: A, B, D
Explanation
To optimize data storage and querying in Amazon S3, you should convert data into a columnar format like Parquet, which is highly efficient for analytic workloads. Additionally, creating metadata tables in AWS Glue Data Catalog allows for seamless querying and schema management. Partitioning data based on frequently queried fields further improves query performance by reducing the amount of data scanned. These steps collectively ensure cost-efficient and performant data analytics workflows.
- A. Correct.
Converting the data to a columnar format like Parquet reduces storage costs and increases query performance by storing data more efficiently.
- B. Correct.
Using AWS Glue to create metadata tables in the Glue Data Catalog allows you to query the data using Amazon Athena or other tools. It also provides schema management capabilities.
- C. Incorrect.
Amazon S3 Transfer Acceleration is meant to speed up transfers over long distances but does not optimize data storage or querying.
- D. Correct.
Partitioning data based on frequently queried fields, such as 'region' or 'date', improves query performance by limiting the amount of data scanned in each query.
- E. Incorrect.
Enabling versioning on the S3 bucket helps maintain historical versions of objects but does not directly improve data storage optimization or querying efficiency.