Amazon Elasticsearch Service now supports open source Elasticsearch 7.9 and its corresponding version of Kibana. This minor release includes bug fixes and enhancements. 

AWS Secrets Manager now supports higher request rates for the GetSecretValue API operation of up to 5000 requests per second. This increased API limit will be applied to your accounts automatically. No further action required on your end.  

Amazon Elasticsearch Service now offers support for Remote Reindex, enabling you to migrate data from a remote cluster into Amazon Elasticsearch Service. With this feature, you can simply copy data from one cluster to another, making it easier to migrate from legacy versions of Elasticsearch. Remote Reindex also supports migrating indexes from self-managed Elasticsearch onto Amazon Elasticsearch Service, providing a simple mechanism to onboard onto the service.

Amazon Elasticsearch Service now offers anomaly detection for high cardinality datasets. This new feature enables you to sift through thousands of metrics from millions of events to accurately pinpoint individual entities with abnormal patterns. By leveraging machine learning, Amazon Elasticsearch Service now provides reliable and actionable insights to drastically reduce the time to isolate and remediate issues. High cardinality anomaly detection can be invaluable for a number of operational, security and business use cases like identifying hosts with high CPU and memory consumption, finding services with most error rates, isolating suspicious users or IP addresses accessing sensitive information, or detecting outliers in sales by region. 

Amazon Elasticsearch Service now supports Piped Processing Language (PPL), a new feature that enables users to explore, discover and find data stored in Amazon ES, using a set of commands delimited by pipes (|). PPL extends Elasticsearch to support a standard set of commands that is easy for system developers, DevOps engineers, support engineers, site reliability engineers (SREs), and IT managers who are proficient with Linux or Unix to learn. PPL enables these users to begin extracting insights from their log, monitoring and observability data on day one.

Customers can now explicitly specify which qubits are going to be used when they use Amazon Braket to run a quantum circuit on quantum computers from Rigetti. This allows researchers and advanced users to optimize their circuit design based on the latest device calibration data to get more accurate results.

AWS now offers Amazon Comprehend Events, which extracts real world events and the associated arguments from text documents. For example, consider a news article that announces that Amazon acquired WholeFoods Market. Comprehend Events will identify the event as an ‘acquisition’ and detect the acquirer (Amazon), acquiree (WholeFoods Market), the deal amount and the date and time of the deal. Customers can use Comprehend Events to understand relationships from natural language text documents between entities such as organizations, people, and dates to build applications such as analytics on financial data and knowledge graphs. 

Amazon Elastic Container Service (Amazon ECS) Cluster Auto Scaling (CAS) now offers more responsive scaling when using EC2 Auto Scaling groups (ASGs) that span across Availability Zones (AZs) and instance types.

Location-Based Notifications Using Amazon Pinpoint is a new AWS Solutions Implementation that helps you easily add a new geographic dimension to personalized and targeted messaging for end users. Personalized customer engagements can be time-consuming, cumbersome, and expensive. This solution reduces that heavy-lifting by configuring and provisioning a reference architecture. The solution leverages Amazon Pinpoint and AWS AppSync APIs to enable near real-time notifications based on an end-users’ location. It quickly registers locations and adds a geofence markup for geographic boundaries around any point of interest. These geofences can be used for more personalized engagements via the solution APIs pushing timely notifications to end-users when they enter or exits a geofence (for example, your business, a restaurant, or any other venue).

CloudWatch Synthetics now supports canary scripts in Python programming language with the Selenium open source web automation testing framework . This gives you more choice in the programming language and framework to use when creating canaries in CloudWatch Synthetics.