A Company Analyzes Historical Data and Needs to Query Data

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.
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.
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.