Considerations for Implementing S3 Data Sets and Amazon Athena

Answer - A and C.

The AWS Documentation mentions the following.

Use these tips and examples when you specify the location in Amazon S3.

Athena reads all files in an Amazon S3 location you specify in the CREATE TABLE statement and cannot ignore any files included in the prefix.

When you create tables, include in the Amazon S3 path only the files you want Athena to read.

Use AWS Lambda functions to scan files in the source location, remove any empty files, and move unneeded files to another location.

In the LOCATION clause, use a trailing slash for your folder or bucket.

Option B is incorrect since the file can have other delimiters and it is not necessary that it is in csv format.

Option D is incorrect since this is not a prime requirement for using Athena with S3

For more information on Table locations for Amazon S3, please refer to the below URL.

https://docs.aws.amazon.com/athena/latest/ug/tables-location-format.html

Sure, here is a detailed explanation of the two options that must be taken into consideration when hosting data sets via files uploaded to S3 and using Amazon Athena to create tables based on the files in S3.

Option A: Ensure that a Lambda function is in place to remove any unwanted files from the S3 bucket.

When data sets are stored in S3, unwanted files may accumulate over time. These files can consume storage space, slow down data access, and even cause issues with data analysis. To prevent this, it's important to have a mechanism in place to remove unwanted files from the S3 bucket.

One way to do this is by using a Lambda function. Lambda is a serverless compute service offered by AWS that can be used to perform automated tasks. In this case, a Lambda function can be created to periodically scan the S3 bucket for unwanted files and delete them. The Lambda function can be scheduled to run at specific intervals or triggered by certain events, such as when a new file is uploaded to the bucket.

Option C: In the LOCATION clause, use a trailing slash for your folder or bucket.

When creating tables in Amazon Athena based on files stored in S3, it's important to specify the correct location of the files. This is done using the LOCATION clause in the CREATE TABLE statement. The location can be either a folder or a bucket, and it's important to use a trailing slash for the folder or bucket name.

For example, if the files are stored in a folder called "myfolder" in the S3 bucket "mybucket", the LOCATION clause should be specified as:

LOCATION 's3://mybucket/myfolder/'

Note the trailing slash after the folder name. This is important because if it's not included, Athena may not be able to find the files and the table creation will fail.

Option B and D are not directly related to the implementation of hosting data sets via files uploaded to S3 and using Amazon Athena to create tables based on the files in S3. Therefore, they are not the correct options to consider in this case.

Option B: Ensure that all files are in CSV file.

While Athena supports various file formats, such as Parquet, ORC, and Avro, it's not necessary to ensure that all files are in CSV format. Athena is designed to work with structured data, and as long as the files are in a structured format that Athena can parse, such as JSON or XML, they can be used to create tables in Athena.

Option D: Ensure that versioning is enabled for the S3 bucket.

While versioning is a useful feature in S3 that can help protect against accidental file deletions and overwrites, it's not necessary to ensure that versioning is enabled for the S3 bucket when hosting data sets via files uploaded to S3 and using Amazon Athena to create tables based on the files in S3. Versioning does not have any direct impact on Athena's ability to create tables based on the files in the bucket.