When processing queries, Athena retrieves metadata information from your metadata store such as AWS Glue Data Catalog or your Hive Metastore before performing partition pruning. In cases when your tables
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,
Amazon Athena now supports user-defined functions (UDFs), a feature that enables customers to write custom scalar functions and invoke them in SQL queries. While Athena provides built-in functions, UDFs enable
Running analytics on data spread across applications can be complex and time consuming. Data required for analytics is often spread across relational, key-value, document, in-memory, search, graph, object, time-series and
Today, Amazon Athena released a new feature that allows users to easily invoke machine learning models for inference directly from their SQL queries. The ability to use machine learning models
You can create custom dashboards, set alarms and triggers on metrics in CloudWatch, or use pre-populated dashboards directly from the Athena console to consume these new query metrics. Please
Customers use a Hive Metastore as a common metadata catalog for their big data environments. Such customers run Apache Spark, Presto, and Apache Hive on Amazon EC2 and Amazon EMR
With the latest release, Amazon Athena is now available in US East (Northern Virginia, & Ohio), US West (Oregon, & N. California), AWS GovCloud, Europe (Ireland, Frankfurt, London, Stockholm, and
With the latest release, Amazon Athena is now available in US East (Northern Virginia, & Ohio), US West (Oregon), AWS GovCloud, Europe (Ireland, Frankfurt, London, Stockholm and Paris), and Asia