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 an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
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.