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.  

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.  

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.

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.  

Amazon EMR release 5.28.0 now supports Apache Hudi (Incubating). Data engineers using Amazon EMR for data pipeline development and data processing can now use Apache Hudi to simplify incremental data management and data privacy use cases requiring record-level insert, updates, and delete operations. Apache Hudi enables Amazon S3-based data lakes to comply with data privacy laws, consume real time streams and change data capture logs, reinstate late arriving data, and track change history and rollback. Apache Hudi is open-source and supports storing data on Amazon S3 in vendor neutral, open source formats such as Apache Parquet and Apache Avro.

AWS App Mesh has increased the default limits for a set of App Mesh resources – virtual nodes, backends, routers, and routes. These limit increases make it easier for you to manage larger applications with App Mesh. The number of virtual nodes per mesh, which map to your services, tasksets, or deployments that can be connected to the mesh, increased from 20 to 200. The number of backends per node; the number of dependent services that a specific service can connect with, increased from 25 to 50. The number of virtual routers per mesh increased from 20 to 200, to enable routing on all mesh connected services. The number of routes per virtual router increased from 20 to 50, to enable matching on multiple parameters for routing decisions.  

Elastic Fabric Adapter (EFA) is now compatible with Intel® MPI Library 2019 Update 6. Intel® MPI Library is a multi-fabric message passing library that implements the Message Passing Interface (MPI). Customers can use the library to create, maintain, and test applications that perform better on HPC clusters based on Intel® processors.

Today, AWS Systems Manager Parameter Store launched enhanced search capability to enable you to search for parameters easily by parameter name. The new search experience enables easy discovery of parameters when you have large number of parameters in your account or when you do not remember the exact name of a parameter.