Have you worked with Athena? Do you have experience creating tables in Athena?

Amazon Athena is a serverless, interactive query service that lets you analyze data stored in Amazon S3 using standard SQL. Because it is serverless, you don’t need to provision or manage any infrastructure; you simply point Athena at the data location and run queries. To create a table in Athena, you define a schema that maps to the underlying file format (e.g., Parquet, CSV, JSON) and specify the S3 location. A typical statement looks like:

CREATE EXTERNAL TABLE mydb.mytable (

id INT,

name STRING,

created_at TIMESTAMP

) WITH (

format = 'PARQUET',

external_location = 's3://my-bucket/data/',

partitioned_by = ARRAY['year', 'month']

);

Athena automatically registers the table in the AWS Glue Data Catalog, which can be reused by other services. Best practices include partitioning the data to reduce scan size, compressing files (e.g., using Snappy or GZIP), and using columnar formats like Parquet for cost‑effective queries. Query performance is proportional to the amount of data scanned, so careful schema design and partitioning can significantly lower costs and improve speed.