AWS Certified Data Analytics Specialty 2021 – Hands-On - Athena Performance

The video tutorial focuses on optimizing Athena's performance by using columnar data formats like ORC or Parquet, organizing data into a small number of large files, and leveraging partitions. It explains how to preprocess data using tools like Glue and ETL transforms, and how to add partitions to existing datasets using the MSCK REPAIR TABLE command. These strategies help improve query performance and efficiency when working with Athena.

What data format is recommended for optimizing Athena's performance?

• JSON

• XML

• CSV

• ORC or Parquet

Why is it beneficial to use a small number of large files in Athena?

• It enhances data security

• It reduces storage costs

• It improves query performance

• It simplifies data management

How can you organize your data in S3 to improve query performance in Athena?

• By partitioning the data

• By compressing the data

• By duplicating the data

• By encrypting the data

What command can be used to add partitions to an existing Athena dataset?

• ALTER TABLE ADD PARTITION

• MSCK REPAIR TABLE

• ADD PARTITION

• CREATE PARTITION

If you frequently query data by date, how should you partition your data in Athena?

• By file size

• By data type

• By date

• By user ID