You are working for a global financial company.
Company locations spread across various countries upload transaction details data to the S3 bucket in the US-West region.
A large amount of data is uploaded daily from each of these locations simultaneously.
You are using Amazon Athena to query this data & create reports using Amazon QuickSight to create a daily dashboard for the management team.
In some cases, while running queries, you are observing Amazon S3 exception errors. Also, in the monthly bills, a high percentage of cost is associated with Amazon Athena.
Which of the following could help eliminate S3 errors while querying data and reducing the cost associated with queries? (SELECT TWO)
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answers - B and C.
AWS Athena pricing is based upon per query and the amount of data scanned in each query.
In the above case, each regional office is uploading a large amount of data simultaneously.
This data needs to be partitioned based upon location & date.
A separate Workgroup can be created based upon users, teams, applications or workloads.
This will minimize the amount of data scanned for each query, improve performance & reducing cost.
Option A is incorrect as partitioning the data on user credentials is irrelevant here.
Option D is incorrect as a single Workgroup will not decrease the amount of data scanned per query.
For more information on Partitioning data & using Workgroups, refer to the following URLs-
https://docs.aws.amazon.com/athena/latest/ug/partitions.html https://docs.aws.amazon.com/athena/latest/ug/manage-queries-control-costs-with-workgroups.htmlBased on the scenario described, there are two main challenges that need to be addressed: reducing the occurrence of Amazon S3 exceptions while querying data and reducing the cost associated with Amazon Athena queries. The following options can help to address these challenges:
A. Partition data based upon user credentials: This option involves partitioning the data based on user credentials. In other words, each user or group of users has their own partition, and they can only access the data within their partition. This approach can help to reduce the amount of data that each user or group needs to query, which can reduce the likelihood of Amazon S3 exceptions. Additionally, since each user or group is only querying their own partition, the overall cost of queries may be reduced. However, this approach may require more upfront planning and management to ensure that partitions are set up correctly and users are only accessing the data they need.
B. Partition data based upon date and location: This option involves partitioning the data based on date and location. This approach can help to reduce the amount of data that needs to be queried, which can reduce the likelihood of Amazon S3 exceptions. Additionally, since data is partitioned based on location, users may be able to query data that is geographically closer to them, which can help to reduce query times and costs. However, this approach may require more upfront planning and management to ensure that partitions are set up correctly and that queries are optimized to take advantage of partitioning.
C. Create a separate Workgroups based upon user groups: This option involves creating separate Workgroups for different user groups. Each Workgroup can have its own query queue and query execution settings, which can help to prevent queries from one group from interfering with queries from another group. Additionally, each Workgroup can have its own cost allocation tags, which can help to track costs associated with each group. This approach can help to reduce the likelihood of Amazon S3 exceptions by ensuring that queries are executed in an orderly manner. However, this approach may require more management overhead to set up and maintain the separate Workgroups.
D. Create a single Workgroup for all users: This option involves creating a single Workgroup for all users. This approach may be simpler to set up and manage compared to option C, but it may not be as effective in reducing the likelihood of Amazon S3 exceptions. Additionally, it may be more difficult to track costs associated with each user or group.
In summary, options A and B can help to address the challenges of reducing Amazon S3 exceptions and reducing query costs. Option C may be effective in preventing queries from interfering with each other, while option D may be simpler to set up but may not be as effective in reducing Amazon S3 exceptions or tracking costs.