You can now develop AWS Lambda functions using Java 11. You can use Java 11 features such as its improved HTTP Client API and new methods for reading and writing strings when authoring your functions. Lambda functions written in Java 11 run on Amazon Linux 2, the latest generation of Amazon Linux, and Amazon Corretto 11, a no-cost, production-ready distribution of OpenJDK 11 that comes with long-term support. 

You can now easily provision managed worker nodes for Amazon Elastic Kubernetes Service (EKS) clusters and keep them up to date using the Amazon EKS management console, APIs, or CLI.

You can now author your AWS Lambda functions in Node.js 12, and use its new features such as the performance improvements in the V8 engine, private class fields, and enhanced stack tracing. Lambda functions written in Node.js 12 run on the latest generation of Amazon Linux, Amazon Linux 2. You can read the Node.js programming model in the AWS Lambda documentation to learn more about writing functions in Node.js 12. 

You can now develop your AWS Lambda functions using Python 3.8. This is the newest major release of the Python language, and contains many new features such as assignment expressions, positional-only arguments, and typing improvements. Lambda functions written in Python 3.8 run on the latest generation of Amazon Linux, Amazon Linux 2. You can read the Python programming model in the AWS Lambda documentation to learn more about writing functions in Python 3.8. 

We are happy to announce the Amazon EMR runtime for Apache Spark – A performance optimized runtime environment for Apache Spark, available and turned on by default on Amazon EMR clusters. EMR runtime for Spark is up to 32x faster with 100% API compatibility with open source Spark. The runtime is on by default starting in EMR release 5.28. 

Today, AWS CloudFormation is introducing a set of capabilities that make it easy to model and automate the management of third party resources such as SaaS monitoring or incident management tools with the benefits of infrastructure-as-code. With this launch, you can use AWS CloudFormation as a single tool to automate provisioning of your infrastructure and application resources, whether AWS or third party, without the need for custom scripts or manual processes. You can now create your own private AWS CloudFormation resource providers, share them with the open source community, and leverage third party providers developed by others.  

We are thrilled to announce that we have also released another requested feature – improved CloudFormation support. With this update, you can leverage improved CloudFormation support to quickly take resources from development into production, or replicate existing configurations into new regions. Check out the documentation for more details.

Amazon Personalize is a machine learning service which enables you to personalize your website, app, ads, emails, and more, with custom machine learning models which can be created in Amazon Personalize, with prior no machine learning experience.  

AWS AppSync is a managed GraphQL service that simplifies application development by letting you create a flexible API to securely access, manipulate, and combine data from one or more data sources. AppSync allows you to easily make any of its supported data sources real time, with connection management handled automatically between the client and the service. With today’s launch we’re releasing enhancements to AppSync that will further optimize access to applications requiring real-time updates, such as gaming leaderboards, social media apps, sports scores, live streaming, interactive chatrooms, IoT dashboards, and many others, by enabling a new protocol option with support to metrics and larger payloads.

Amazon GuardDuty customers can now export findings to Amazon S3 using the GuardDuty management console and API. With findings export, aggregating findings from across regions is simplified. When configured from the GuardDuty master account, customers can export findings from all associated member accounts and all AWS regions to a single customer owned S3 bucket. The S3 bucket used can be in the same account in which GuardDuty is enabled, or in a different AWS account. Once Findings export is configured in each Region, Amazon GuardDuty findings are automatically exported from GuardDuty to the configured Amazon S3 bucket. This feature enhancement gives customers a simplified way to aggregate all findings to a single customer owned Amazon S3 bucket across all accounts and regions for integration with other AWS services, third-party applications, or for long-term retention.