With the latest release of JDBC and ODBC drivers for Amazon Athena, you can use Microsoft’s Azure Active Directory (AD) or Ping Identity’s PingFederate for authentication with compatible business intelligence, SQL, or embedded analytics applications.

Building a business case for the cloud can be a time-consuming process on your own. With Migration Evaluator, organizations can accelerate their evaluation and decision-making for migration to AWS. Quick Insights – a new capability of Migration Evaluator, provides customers with a one-page summary of their projected AWS costs based on measured on-premises provisioning and utilization. This complimentary pre-migration assessment enables customers to rapidly understand the projected cost of running their on-premises estate in the AWS Cloud.

Today, AWS announces Incident Manager, a new capability of AWS Systems Manager that enables faster resolution of critical application availability and performance issues. Incident Manager helps you prepare for incidents with automated response plans that bring the right people and information together. The Incident Manager console provides a unified user interface to view operational data from multiple AWS services and track incident updates, such as alarm status changes and response plan progress. Incident Manager helps you improve service reliability by suggesting post-incident action items, such as automating a runbook step or adding a new alarm.

CloudWatch Synthetics now supports cron expressions, allowing for flexibility when you schedule canaries to monitor your endpoints. For example, you can run a canary every minute on weekdays between 8 AM and 5 PM. Alternately, you can schedule a canary run for the third Tuesday of the month at 12 PM.

You can now launch Apache ActiveMQ 5.15.15 brokers on Amazon MQ. This patch update to ActiveMQ contains several fixes and new features compared to the previously supported version, ActiveMQ 5.15.14.

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface. Starting today, you can use new capabilities of Amazon SageMaker Data Wrangler that make it easier and faster to prepare data for machine learning including: cross-account access for Amazon S3, support for up to 1000 columns of data, distributed jobs, and a new SageMaker Data Wrangler notebook experience.

The Amazon Elastic Block Store (EBS) Container Storage Interface (CSI) driver is now generally available. The EBS CSI driver makes it simple to configure and use block storage for applications running in both Amazon Elastic Kubernetes Service (EKS) and self-managed Kubernetes clusters running on AWS using standard Kubernetes interfaces.

AWS Organizations now offers a new console experience that provides you with a more intuitive way to manage your multi-account environment on AWS. You can now more easily navigate through your Organizational Unit hierarchy and perform AWS account management tasks in a streamlined way. The updated interface also gives you easy access to documentation, product information, and resources to assist you in managing your multi-account environment.

Amazon SageMaker Automatic Model Tuning enables you to find the best version of a model by finding the optimal set of hyperparameter configuration for your dataset. Starting today, SageMaker Automatic Model Tuning now supports running up to 100 parallel training jobs for hyperparameter tuning, which gives you a 10X increase of parallel training jobs so you can complete your tuning faster. Additionally, for “Random” search strategy, SageMaker Automatic Model Tuning now supports exploring up to 10,000 hyperparameter configurations, a 20x increase over previous limit of 500, enabling you to improve coverage of search space leading to potentially better predictive performance of your model.

Today, we are launching Amazon EMR on Amazon EKS support for Pod Templates to make it simple to run Spark jobs on shared EKS clusters. A Pod is a group of one or more containers, with shared storage and network resources, and a specification for how to run the containers. Pod Templates are specifications which determine how each Pod runs. Customers often consolidate multiple applications on a shared EKS cluster to improve utilization and save costs. However, each application may have different requirements. For example, you may want to run performance intensive workloads such as ML model training jobs on SSD-backed instances for better performance, or ad-hoc workloads on Spot instances for lower cost. You can also schedule a separate logging container to forward logs to your existing monitoring application. With this release, you can use Pod Templates with EMR on EKS to configure how to run Spark jobs on shared EKS clusters.