AWS WAF now supports log filtering, enabling you to specify which web requests are logged and which requests are discarded from log after the inspection. You can use log filtering to keep only the information you want to analyze. Log filtering can also help you save on log delivery and storage costs by reducing the amount of log data stored.

Amazon EMR integration with Apache Ranger is now available on EMR 6.3, allowing you to define, enforce, and audit fine-grained data access control. With this feature, you can define and enforce 1/ database, table, and column level authorization policies for Apache Spark and Apache Hive users to access data through Hive Metastore, and 2/ prefix and object level authorization policies when accessing data in Amazon S3 via the Amazon EMR File System (EMRFS), leveraging Amazon CloudWatch to capture auditing logs.

At re:Invent 2020, AWS announced the preview of AWS SaaS Boost, an open source tool that helps software developers migrate their existing solutions to a Software-as-a-Service (SaaS) delivery model. SaaS Boost significantly offloads development effort by accelerating application transformation to SaaS, freeing up software developers to focus on features that differentiate their products. After receiving interest from hundreds of developers in the project, today we’re pleased to announce its public availability on GitHub . All SaaS products need the foundational capabilities to onboard users, provision infrastructure for tenants, monitor consumption trends, configure tenant profiles, integrate with a billing systems, and surface key metrics. These functions are critical for helping SaaS providers to scale. If every SaaS company spent their resources building these capabilities before building their actual applications, it would result in burning finite capital and slowing down their time to market.

Distributed Load Testing on AWS helps you easily simulate thousands of users connecting to your application so that you can better understand your application performance under load. The solution launches and configures containers on AWS Fargate to generate a specified number of transactions per second without having to provision servers.

Starting today, the new EC2 High Memory instances with 6TB, 9TB, and 12TB of memory (u-6tb1.56xlarge, u-6tb1.112xlarge, u-9tb1.112xlarge, and u-12tb1.112xlarge) are available for usage with On-Demand (OD) and Savings Plan purchase options. This launch gives customers greater flexibility for instance usage and procurement. Customers can choose from two instances with 6TB of memory – u-6tb1.56xlarge and u-6tb1.112xlarge – both offering 100Gbps network and 38Gbps EBS bandwidth, and with 224 vCPUs and 448 vCPUs, respectively. These new instances provide additional flexibility for customers with memory-bound workloads and helps further optimize workload price/performance. The 9TB and 12TB instances offer 448 vCPUs, 100Gbps network and 38Gbps EBS bandwidth. 

Machine Downtime Monitor on AWS is a new solution that provides a dashboard to industrial customers to help them monitor the health of factory floor equipment, which enables easier identification of machine breakdowns and quicker response, improving asset availability. This solution offers an intuitive interface that allows non-technical users to easily configure machines and get up and running quickly. It also empowers production personnel to respond proactively to production interruptions and maximize asset availability.

Amazon Aurora PostgreSQL patch releases are now available for customers using Amazon Aurora PostgreSQL-Compatible Edition. To review the changes, visit the documentation history  and for detailed release notes, visit our version documentation . You can apply the new patch version in the AWS Management Console, via the AWS CLI, or via the RDS API. For detailed instructions, please see our technical documentation .

We are happy to announce that all AWS Federal Information Processing Standard (FIPS) endpoints have been updated to only accept a minimum of Transport Layer Security (TLS) 1.2 connections. This update ensures that our customers who run regulated workloads can meet FedRAMP compliance requirements that mandate a minimum of TLS 1.2 encryption for data in transit. Attempts to connect to AWS FIPS endpoints using TLS 1.0 or TLS 1.1 will result in an HTTP response 503 Service Unavailable error.

The AWS Solutions team updated Improving Forecast Accuracy with Machine Learning, an AWS Solutions Implementation that helps customers easily generate, test, compare, and iterate on forecasts. The solution allows customers to automatically trigger a workflow to automate the creation of forecasts in Amazon Forecast based on user-provided configuration. The solution then provides automatic visualization using Amazon QuickSight (and optionally Amazon SageMaker Jupyter Notebooks) providing a quick, easy, drag-and-drop interface that displays time series input and forecasted output for any input time series data.