A company has decided to start using AWS Athena for querying data in S3
The amount of data is huge and they need to create queries based on a subset of data.
How can they accomplish this in the easiest manner?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer B.
The AWS Documentation mentions the following.
You may want to create views to:
Query a subset of data.
For example, you can create a table with a subset of columns from the original table to simplify querying data.
Combine multiple tables in one query.
When you have multiple tables and want to combine them with UNION ALL, you can create a view with that expression to simplify queries against the combined tables.
Options A and C are incorrect since this would cause a maintenance overhead.
Option D is incorrect since here we need to optimize the query and not the visualization.
For more information on when to use views, please refer to the below URL.
https://docs.aws.amazon.com/athena/latest/ug/when-to-use-views.htmlTo query a subset of data in AWS Athena, there are several options available, but the easiest and most efficient way is to split the data into smaller, manageable files in S3. This allows Athena to perform the queries faster by only scanning the relevant files.
Option A: Split the files in S3 This option is the correct answer to the question. It involves splitting the data files stored in S3 into smaller, manageable files. This can be done manually or using automated tools like AWS Glue or EMR. Once the data is split, Athena can be used to query the specific files that are relevant to the analysis required. This approach can be combined with partitioning, which is another way to optimize query performance in Athena.
Option B: Use views in Athena Views in Athena are virtual tables that allow users to access a specific subset of data from one or more tables. While this can be a useful way to create custom views of data for specific purposes, it does not necessarily improve performance or reduce the amount of data scanned by Athena.
Option C: Create different buckets in S3 Creating different buckets in S3 is not an optimal solution for querying a subset of data in Athena. It requires additional management overhead and can lead to data fragmentation, which can negatively impact performance.
Option D: Create different types of charts in Athena Creating different types of charts in Athena is not relevant to the question. Charts are a way to visualize data and communicate insights, but they are not directly related to querying a subset of data.
In conclusion, the easiest and most efficient way to query a subset of data in AWS Athena is to split the data into smaller, manageable files in S3. This allows Athena to perform queries faster by only scanning the relevant files.