Amazon CloudWatch Contributor Insights for Amazon DynamoDB (Preview) is a new diagnostic tool that provides an at-a-glance view of the traffic trends of your DynamoDB table and helps you identify the most frequently accessed keys. Now, you can monitor a table’s item access patterns continuously and also use CloudWatch Contributor Insights to provide graphs and visualizations of the table’s activity. You can use this information to better understand the top drivers of your application’s traffic and respond appropriately to unsuccessful requests.  

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 customers to perform custom processing such as compressing and decompressing data, redacting sensitive data, or applying customized decryption.

Amazon Athena now enables users to run SQL queries across data stored in relational, non-relational, object, and custom data sources. With federated querying, customers can submit a single SQL query that scans data from multiple sources running on-premises or hosted in the cloud.

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 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.  

Today, Amazon Athena has released a new feature that allows you to connect Athena to your Apache Hive Metastore. 

Today, we publish additional query metrics that can help customers understand Amazon Athena performance. Athena publishes query-related metrics to Amazon CloudWatch. With this release, Athena will publish four additional query metrics. They are:

• Query Planning Time, the time taken to plan the query. This includes the time spent retrieving table partitions from the data source,
• Query Queuing Time, the time that the query was in a queue waiting for resources,
• Service Processing Time, the time taken to write results after the query engine finished its execution,
• Total Execution Time, time Athena took to run the query.

You can now use Amazon Aurora to add machine learning (ML) based predictions to your applications, using a simple, optimized, and secure integration with Amazon SageMaker and Amazon Comprehend. Aurora machine learning is based on the familiar SQL programming language, so you don’t need to build custom integrations, move data around, learn separate tools, or have prior machine learning experience.  

Amazon EMR release 6.0.0 (Beta 2) is now available with Hive 3.1.2, Hadoop 3.2.1, Spark 2.4.4, and Scala 2.12. In this release, Hive LLAP is enabled by default, allowing you to benefit from improved query performance and new features such as materialized views, and workload management. Additionally, Scala has been upgraded, allowing you to start testing your Spark applications with Scala 2.12. 

You can use the Amazon Relational Database Service (Amazon RDS) Data API Client Library with support for Java, now available in preview, to quickly and easily build applications for Amazon Aurora Serverless.  

You can now access Amazon EC2 Auto Scaling, Application Auto Scaling, and AWS Auto Scaling (scaling plans) within your Amazon Virtual Private Cloud (VPC) as these Auto Scaling services now support AWS PrivateLink. With AWS PrivateLink support, you can now privately access auto scaling services from your VPC, without using public IPs, and without requiring the traffic to traverse across the internet.