When I tried it it was slower than bigquery. Plus you've got to mess about creating hive schemas.
Then again, both BigQuey and Snowflake require that you move data into their storage engine (Redshift too), and that's an additional step that's proportional to the size and complexity of your data. At the same time, it's stupid to store your logs as OLAP optimized formats and completely lose legibility. In sum, Athena trades off performance for convenience.
No matter what database vendors say, you can't defy the principles of computer science.
Apache Drill is a schema discovery on read approach that can handle some of this. Its not perfect, but it does simplify some of the process where its capabilities fit the task at hand.
https://cloud.google.com/bigquery/federated-data-sources
(I'm Felipe Hoffa and I work for Google https://twitter.com/felipehoffa)
> Amazon Athena supports a wide variety of data formats like CSV, TSV, JSON, or Textfiles and also supports open source columnar formats such as Apache ORC and Apache Parquet. Athena also supports compressed data in Snappy, Zlib, and GZIP formats. By compressing, partitioning, and using columnar formats you can improve performance and reduce your costs.