My main use case for Amazon Athena is serving a healthcare organization in the US that needed an interactive SQL editor to write complex queries and retrieve details instead of checking manually in the S3 bucket. I proposed and implemented the solution end-to-end from scratch using Amazon Athena and other AWS services such as AWS Glue services, including crawlers and modifiers, and CloudWatch for logging purposes. I implemented and provided a production-ready solution to the client, and they are very satisfied with it.Ideally, there are no external or third-party applications installed, so the end user only needs access to AWS accounts with the Amazon Athena query editor. Through the query editor, they can write their query and receive results, and they can store the results if desired. Regarding my main use case from the end user perspective, if they encounter issues with the data, it is not practical to check manually through each data file in the S3 bucket. Instead, I implemented Amazon Athena on top of the data using the AWS Glue service stack, and based on that implementation, whatever tables and databases are created allow users to write complex queries and retrieve the details they need. If they obtain the results they are looking for, their work speed increases significantly, and they are satisfied with writing SQL queries. I have also provided prerequisite queries based on known problem statements.
Senior Software Engineer at a tech services company with 10,001+ employees
Real User
Top 5
Jul 8, 2026
My main use case for Amazon Athena is querying data that sits in S3 buckets. Currently, I am working for an aviation client for which we receive data on a daily basis. We take this data from a source and perform transformations. After the transformation is complete, we have dimension and fact tables. That data is stored into S3 tables in the S3 buckets. Once the data sits in the S3 bucket, we have integrated S3 buckets with Amazon Athena. The data sits in Parquet format with Zstandard compression at level three. We have Lake Formation permissions with Amazon Athena to query the data. We have a catalog for S3 bucket data, and we create our workgroups in Amazon Athena. That is how we use data from S3 buckets for querying via Amazon Athena.
The typical use case for Amazon Athena is that we have data in a data lake, and if we need to query the data from the data lake, we use Amazon Athena before it gets to the data warehouse where we were using Snowflake, so the proper warehouse. That's the main use case. It's just to verify some data on the data lake, and it also allows us to query data on S3 directly, which is what we use it for.
I have experience using Amazon Athena, and this service I have more experience with, actually. I use Amazon Athena for my daily activity; I was using it just now for getting data, and we use Amazon Athena in Lake Formation, mainly using Data Mesh resources. Every table storage is in an S3 and Amazon Glue Catalog that stores the schemas, and we use Amazon Athena to read that data, and it runs with clustered performance. It is very fast to retrieve data, and I used to query SQL and get information and do all the data analyst jobs that are necessary. I use Amazon Athena a lot. I also use Amazon Athena from step functions, calling Athena saved queries or specific queries. I use Amazon Athena also with Python and Boto3, calling Amazon Athena as a client. I use Amazon Athena also inside a Glue job Python script when automating jobs and using Boto3 and AWS Wrangler libraries for Python to query data in Amazon Athena and use the service at AWS.
In the case of both Athena and Glue, if you have some data and want to query upon that, then you can basically use Glue to get the schema and Athena to query the data. You need both of them to work.
Amazon Athena is a serverless, interactive query service for analyzing data in Amazon S3 using SQL. It efficiently supports data lake architectures and offers features for diverse data formats without needing extensive infrastructure. Athena's integration with AWS Glue enhances schema management. Amazon Athena leverages a serverless architecture to provide scalable, cost-effective query capabilities for large datasets stored in Amazon S3. With native support for Parquet and Avro, it...
My main use case for Amazon Athena is serving a healthcare organization in the US that needed an interactive SQL editor to write complex queries and retrieve details instead of checking manually in the S3 bucket. I proposed and implemented the solution end-to-end from scratch using Amazon Athena and other AWS services such as AWS Glue services, including crawlers and modifiers, and CloudWatch for logging purposes. I implemented and provided a production-ready solution to the client, and they are very satisfied with it.Ideally, there are no external or third-party applications installed, so the end user only needs access to AWS accounts with the Amazon Athena query editor. Through the query editor, they can write their query and receive results, and they can store the results if desired. Regarding my main use case from the end user perspective, if they encounter issues with the data, it is not practical to check manually through each data file in the S3 bucket. Instead, I implemented Amazon Athena on top of the data using the AWS Glue service stack, and based on that implementation, whatever tables and databases are created allow users to write complex queries and retrieve the details they need. If they obtain the results they are looking for, their work speed increases significantly, and they are satisfied with writing SQL queries. I have also provided prerequisite queries based on known problem statements.
My main use case for Amazon Athena is querying data that sits in S3 buckets. Currently, I am working for an aviation client for which we receive data on a daily basis. We take this data from a source and perform transformations. After the transformation is complete, we have dimension and fact tables. That data is stored into S3 tables in the S3 buckets. Once the data sits in the S3 bucket, we have integrated S3 buckets with Amazon Athena. The data sits in Parquet format with Zstandard compression at level three. We have Lake Formation permissions with Amazon Athena to query the data. We have a catalog for S3 bucket data, and we create our workgroups in Amazon Athena. That is how we use data from S3 buckets for querying via Amazon Athena.
The typical use case for Amazon Athena is that we have data in a data lake, and if we need to query the data from the data lake, we use Amazon Athena before it gets to the data warehouse where we were using Snowflake, so the proper warehouse. That's the main use case. It's just to verify some data on the data lake, and it also allows us to query data on S3 directly, which is what we use it for.
I have experience using Amazon Athena, and this service I have more experience with, actually. I use Amazon Athena for my daily activity; I was using it just now for getting data, and we use Amazon Athena in Lake Formation, mainly using Data Mesh resources. Every table storage is in an S3 and Amazon Glue Catalog that stores the schemas, and we use Amazon Athena to read that data, and it runs with clustered performance. It is very fast to retrieve data, and I used to query SQL and get information and do all the data analyst jobs that are necessary. I use Amazon Athena a lot. I also use Amazon Athena from step functions, calling Athena saved queries or specific queries. I use Amazon Athena also with Python and Boto3, calling Amazon Athena as a client. I use Amazon Athena also inside a Glue job Python script when automating jobs and using Boto3 and AWS Wrangler libraries for Python to query data in Amazon Athena and use the service at AWS.
I used the solution to load relational databases.
We use Amazon Athena as a dashboarding and reporting tool.
I have been using Amazon Athena to query across the AWS platform, from my Redshift warehouse and S3 storage.
Our company uses the solution for a client-specific requirement to conduct data integration and analysis.
In the case of both Athena and Glue, if you have some data and want to query upon that, then you can basically use Glue to get the schema and Athena to query the data. You need both of them to work.