Amazon Athena features

Amazon Athena is serverless, so there is no infrastructure to manage. You don’t need to worry about configuration, software updates, failures or scaling your infrastructure as your datasets and number of users grow. Athena automatically takes care of all of this for you, so you can focus on the data, not the infrastructure.

To get started, log into the Athena console, define your schema using the console wizard or by entering DDL statements, and immediately start querying using the built-in query editor. You can also use AWS Glue to automatically crawl data sources to discover data and populate your Data Catalog with new and modified table and partition definitions. Results are displayed in the console within seconds, and automatically written to a location of your choice in S3. You can also download them to your desktop. With Athena, there’s no need for complex ETL jobs to prepare your data for analysis. This makes it simple for anyone with SQL skills to quickly analyze large-scale datasets.

Amazon Athena is based on Trino and Presto, open source, distributed SQL engines optimized for low latency, interactive data analysis. This means you can run queries against large datasets in Amazon S3 using ANSI SQL, with full support for large joins, window functions, and arrays. Athena supports a wide variety of data formats such as CSV, JSON, ORC, Avro, or Parquet. With Athena’s federated data source connectors, you can query additional data stores and join the data with data stored in Amazon S3. You can access Athena and run queries from the Athena console, API, CLI, AWS SDK, and supported business intelligence and SQL development applications through Athena's JDBC and ODBC drivers.

Amazon Athena offers two flexible pricing models. By default, queries are billed based on the data scanned per query in terabytes (TB). This allows you to submit queries without planning ahead for compute. If you prefer to pay based on the compute your queries consume or want to control concurrency and prioritize workloads, use capacity-based pricing available with Provisioned Capacity. For added flexibility, you can use per query billing and capacity-based pricing at the same time in the same account.

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.  

Amazon Athena is highly available and executes queries using compute resources across multiple facilities, automatically routing queries appropriately if a particular facility is unreachable. Athena uses Amazon S3 as its underlying data store, making your data highly available and durable. Amazon S3 provides durable infrastructure to store important data and is designed for durability of 99.999999999% of objects. Your data is redundantly stored across multiple facilities and multiple devices in each facility.

Amazon Athena allows you to control access to your data by using AWS Identity and Access Management (IAM) policies, access control lists (ACLs), and Amazon S3 bucket policies. With IAM policies, you can grant IAM users fine-grained control to your S3 buckets. By controlling access to data in S3, you can restrict users from querying it using Athena. Athena also allows you to query encrypted data stored in Amazon S3 and write encrypted results back to your S3 bucket. Both, server-side encryption and client-side encryption are supported.

Amazon Athena integrates out-of-the-box with AWS Glue. With Glue Data Catalog, you will be able to create a unified metadata repository across various services, crawl data sources to discover data and populate your Data 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 query performance and reduce costs. Learn more about AWS Glue.

Athena provides built-in connectors to 30 popular AWS, on premises, and other cloud data stores, including Amazon Redshift, Amazon DynamoDB, Google BigQuery, Google Cloud Storage, Azure Synapse, Azure Data Lake Storage, Redis, Snowflake, and SAP Hana. By using Athena data source connectors, you can generate insights from multiple data sources using the Athena SQL syntax and without the need to move or transform your data. Data connectors run as AWS Lambda functions and can be enabled for cross-account access to scale SQL queries to hundreds of end users. For a list of supported sources, see Available data source connectors. To learn how to build a custom data source connector, see the Athena connector SDK.

You can invoke your SageMaker Machine Learning models in an Athena SQL query to run inference. The ability to use ML models in SQL queries makes complex tasks such anomaly detection, customer cohort analysis and sales predictions as simple as writing a SQL query. Athena makes it simple for anyone with SQL experience to run ML models deployed on Amazon SageMaker.