We recently updated AWS DevOps Monitoring Dashboard, a reference implementation that automates the setup of DevOps metrics dashboards so that customers developing on AWS can measure development activity and identify areas for continued improvement.

You now can further enhance the security of your applications by encrypting data in transit between your applications and your Amazon DynamoDB Accelerator (DAX) clusters, and between the nodes within a DAX cluster. To use this new feature, enable encryption in transit when creating a DAX cluster and use the latest version of any of the DAX clients. If you enable encryption in transit for a DAX cluster, all requests and responses between your applications and clusters are encrypted by Transport Layer Security (TLS), and connections to the cluster can be authenticated by verification of a cluster X.509 certificate. In addition, the data in transit between the nodes within a cluster also is encrypted. You can enable encryption in transit in the DynamoDB console, AWS CLI, AWS SDKs, and AWS CloudFormation. 

Starting today, Amazon Quantum Ledger Database (QLDB) is available in the Europe (London) region. Amazon QLDB is a fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log. You can use QLDB to track all application data changes, and maintain a complete and verifiable history of changes to your data over time.

Amazon Connect now provides an API to programmatically create and manage quick connects. Quick connects are a way for you to create preconfigured destinations for common transfers and make them available for agents to use. Using this API, you can programmatically configure thousands of quick connects to agents, queues, and phone numbers. Additionally, you can now delete quick connects that are no longer required using the delete API. To learn more, see the API documentation . The quick connects API also comes with support for AWS CloudFormation. For more information, see Amazon Connect Resource Type Reference in the AWS CloudFormation User Guide.

AWS Serverless Application Model (AWS SAM) framework launches four new templates for building machine learning inference-based applications on AWS Lambda. Now, customers can leverage these templates as a starting point to build, test, and deploy their container-based Serverless ML applications.

Amazon CloudWatch Metric Math now supports 14 new functions including RUNNING_SUM, TIME_SERIES, and DATAPOINT_COUNT. Two new FILL variants, two logarithmic functions, two functions for calculating the difference between each datapoint, and five time functions are also now supported. With CloudWatch Metric Math, you can aggregate and transform metrics to create custom visualizations of your health and performance metrics. The new functions announced today allow you to view the logarithmic values of your metric, better visualize the change in latency over time, and calculate the cumulative sales of a product.

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.