A company is currently using AWS Athena with a large number of data sets in S3
They want to improve the performance of the underlying queries.
Which of the following can help achieve this?[SELECT TWO]
Click on the arrows to vote for the correct answer
A. B. C. D.Answer : A and C.
The AWS Documentation mentions the following.
By partitioning your data, you can restrict the amount of data scanned by each query, thus improving performance and reducing cost.
Athena leverages Hive for partitioning data.
You can partition your data by any key.
A common practice is to partition the data based on time, often leading to a multi-level partitioning scheme.
For example, a customer who has data coming in every hour might decide to partition by year, month, date, and hour.
Another customer, who has data coming from many different sources but loaded one time per day, may partition by a data source identifier and date.
None of the other options will help in achieving better query performance.
Amazon Athena supports a wide variety of data formats like CSV, TSV, JSON, or Textfiles and also supports open source columnar formats such as Apache ORC and Apache Parquet.
Athena also supports compressed data in Snappy, Zlib, LZO, and GZIP formats.
By compressing, partitioning, and using columnar formats you can improve performance and reduce your costs.
For more information on using partitions, please visit the url.
https://docs.aws.amazon.com/athena/latest/ug/partitions.htmlThe performance of queries in AWS Athena can be improved by partitioning and compressing the data.
A. Partition the data: Partitioning the data allows Athena to scan only the relevant data and ignore the rest, which reduces the amount of data scanned, and improves query performance. For example, if a table contains data for multiple years, partitioning by year will enable Athena to scan only the relevant year(s) during query execution. Partitioning the data also makes it easier to manage and organize the data.
C. Compress the data: Compressing the data can reduce the amount of data that Athena needs to scan, which can improve query performance. Athena supports several compression formats, such as GZIP and Snappy. Compressing the data can also reduce the storage cost of the data in S3.
B. Encrypt the data: Encrypting the data can provide security to the data, but it does not directly improve query performance. However, it may add a slight overhead to query execution, depending on the encryption algorithm used.
D. Version the data: Versioning the data allows multiple versions of an object to coexist in S3. It is useful for keeping track of changes made to the data over time, but it does not directly improve query performance.
Therefore, options A and C are correct as they can help improve the performance of queries in AWS Athena.