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. Most results are delivered within seconds. With Athena, there’s no need for complex ETL jobs to prepare your 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.
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Query data anywhere
Run federated queries against relational databases, data warehouses, object stores, and non-relational data stores. Federated SQL queries allow you to query the data in-place from wherever it resides. You can use familiar SQL to JOIN data across multiple data sources for quick analysis, and store results in Amazon S3 for subsequent use. Athena federated query also introduces a new Query Federation SDK that allows you to write your own data source connectors to query custom data stores.
Create your own User-Defined Functions (UDFs)
Write custom scalar functions and invoke them in your SQL queries. You can write your UDFs using the Athena Query Federation SDK. UDFs can be used in both SELECT and FILTER clauses of a SQL query. You can invoke multiple UDFs in the same query. While Athena provides built-in functions, UDFs enables you to perform custom processing such as compressing and decompressing data, redacting sensitive data, or applying customized decryption.
Machine learning in your SQL queries
Invoke machine learning models for inference directly from your SQL queries. Customers can use more than a dozen built-in machine learning algorithms provided by Amazon SageMaker, train their own models, or find and subscribe to model packages from the AWS Marketplace and deploy on Amazon SageMaker Hosting Services. There is no additional setup required. The ability to use machine learning models in SQL queries makes complex tasks such anomaly detection, customer cohort analysis, and sales predictions as simple as invoking a function in a SQL query.
Movable Ink uses Amazon Athena to query seven years’ worth of historical data and get results in moments, with the flexibility to explore data for deeper insights.
Atlassian built a self-service data lake using Amazon Athena and other AWS Analytics services.
Upsolver is a data lake ETL service. It provides a visual, SQL-based interface for creating real-time tables in Athena with little engineering overhead and according to performance best practices. Upsolver's ETL also enables updates/deletes to tables in Athena for common CDC and compliance use cases.