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.
Athena is easy to use – simply point to your data in Amazon S3, define the schema, and start querying using standard SQL.
Amazon Athena uses Presto with full standard SQL support and works with a variety of standard data formats, including CSV, JSON, ORC, Apache Parquet and Avro.
While Amazon Athena is ideal for quick, ad-hoc querying and integrates with Amazon QuickSight for easy visualization, it can also handle complex analysis, including large joins, window functions, and arrays.
Amazon Athena uses a managed Data Catalog to store information and schemas about the databases and tables that you create for your data stored in Amazon S3.
With Amazon Athena, you don’t have to worry about managing or tuning clusters to get fast performance.
Athena is optimized for fast performance with Amazon S3.
Athena automatically executes queries in parallel, so that you get query results in seconds, even on large datasets.
Most results are delivered within seconds.
With Athena, there’s no need for complex ETL jobs to prepare data for analysis.
This makes it easy for anyone with SQL skills to quickly analyze large-scale datasets.
Athena is out-of-the-box integrated with AWS Glue Data Catalog, allowing you to create a unified metadata repository across various services, crawl data sources to discover schemas and populate your Catalog with new and modified table and partition definitions, and maintain schema versioning.
You can also use Glue’s fully-managed ETL capabilities to transform data or convert it into columnar formats to optimize cost and improve performance.
Query services like Amazon Athena, data warehouses like Amazon Redshift, and sophisticated data processing frameworks like Amazon EMR, all address different needs and use cases.
Amazon Redshift provides the fastest query performance for enterprise reporting and business intelligence workloads, particularly those involving extremely complex SQL with multiple joins and sub-queries.
Amazon EMR makes it simple and cost effective to run highly distributed processing frameworks such as Hadoop, Spark, and Presto when compared to on-premises deployments. Amazon EMR is flexible – you can run custom applications and code, and define specific compute, memory, storage, and application parameters to optimize your analytic requirements.
Amazon Athena provides the easiest way to run ad-hoc queries for data in S3 without the need to setup or manage any servers.
The table below shows the primary use case and situations for using a few AWS query and analytics services:
With Amazon Athena, you pay only for the queries that you run.
You are charged based on the amount of data scanned by each query.
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.
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