Amazon Athena

  • An interactive query service that makes it easy to analyze data directly in S3 using standard SQL.


  • Athena is serverless.
  • Has a built-in query editor.
  • Uses Presto, an open source, distributed SQL query engine optimized for low latency, ad hoc analysis of data.
  • Athena supports a wide variety of data formats such as CSV, JSON, ORC, Avro, or Parquet.
  • Athena automatically executes queries in parallel, so that you get query results in seconds, even on large datasets.
  • Athena uses Amazon S3 as its underlying data store, making your data highly available and durable.
  • Athena integrates with Amazon QuickSight for easy data visualization.
  • Athena integrates out-of-the-box with AWS Glue.
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Athena uses a managed Data Catalog to store information and schemas about the databases and tables that you create for your data stored in S3.


  • By partitioning your data, you can restrict the amount of data scanned by each query, thus improving performance and reducing cost.
  • Athena leverages Hive for partitioning data.
  • You can partition your data by any key.


  • You can query geospatial data.
  • You can query different kinds of logs as your datasets.
  • Athena stores query results in S3.
  • Athena retains query history for 45 days.
  • Athena does not support user-defined functions, INSERT INTO statements, and stored procedures.
  • Athena supports both simple data types such as INTEGER, DOUBLE, VARCHAR and complex data types such as MAPS, ARRAY and STRUCT.
  • Athena supports querying data in Amazon S3 Requester Pays buckets.


  • Control access to your data by using IAM policies, access control lists, and S3 bucket policies.
  • If the files in the target S3 bucket is encrypted, you can perform queries on the encrypted data itself.


  • You pay only for the queries that you run. You are charged based on the amount of data scanned by each query.
  • You are not charged for failed queries.
  • You can get significant cost savings and performance gains by compressing, partitioning, or converting your data to a columnar format, because each of those operations reduces the amount of data that Athena needs to scan to execute a query.


AWS Knowledge Center Videos: How do I analyze my S3 logs using Athena?



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