AWS Week in Review – October 31, 2022

This post was originally published on this site

No tricks, just treats in this weekly roundup of news and announcements. Let’s switch our AWS Management Console into dark mode and dive right into it.

Last Week’s Launches
Here are some launches that got my attention during the previous week:

AWS Local Zones in Hamburg and Warsaw now generally available – AWS Local Zones help you run latency-sensitive applications closer to end users. The AWS Local Zones in Hamburg, Germany, and Warsaw, Poland, are the first Local Zones in Europe. AWS Local Zones are now generally available in 20 metro areas globally, with announced plans to launch 33 additional Local Zones in metro areas around the world. See the full list of available and announced AWS Local Zones, and learn how to get started.

Amazon SageMaker multi-model endpoint (MME) now supports GPU instances – MME is a managed capability of SageMaker Inference that lets you deploy thousands of models on a single endpoint. MMEs can now run multiple models on a GPU core, share GPU instances behind an endpoint across multiple models, and dynamically load and unload models based on the incoming traffic. This can help you reduce costs and achieve better price performance. Learn how to run multiple deep learning models on GPU with Amazon SageMaker multi-model endpoints.

Amazon EC2 now lets you replace the root Amazon EBS volume for a running instance – You can now use the Replace Root Volume for patching features in Amazon EC2 to replace your instance root volume using an updated AMI without needing to stop the instance. This makes patching of the guest operating system and applications easier, while retraining the instance store data, networking, and IAM configuration. Check out the documentation to learn more.

AWS Fault Injection Simulator now supports network connectivity disruption – AWS Fault Injection Simulator (FIS) is a managed service for running controlled fault injection experiments on AWS. AWS FIS now has a new action type to disrupt network connectivity and validate that your applications are resilient to a total or partial loss of connectivity. To learn more, visit Network Actions in the AWS FIS user guide.

Amazon SageMaker Automatic Model Tuning now supports Grid Search – SageMaker Automatic Model Tuning helps you find the hyperparameter values that result in the best-performing model for a chosen metric. Until now, you could choose between random, Bayesian, and hyperband search strategies. Grid search now lets you cover every combination of the specified hyperparameter values for use cases in which you need reproducible tuning results. Learn how Amazon SageMaker Automatic Model Tuning now supports grid search.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Here are some additional news items that you may find interesting:

Celebrating over 20 years of AI/ML innovation – On October 25, we hosted the AWS AI/ML Innovation Day. Bratin Saha and other leaders in the field shared the great strides we have made in the past and discussed what’s next in the world of ML. You can watch the recording here.

AWS open-source news and updates – My colleague Ricardo Sueiras writes this weekly open-source newsletter in which he highlights new open-source projects, tools, and demos from the AWS Community. Read edition #133 here.

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

AWS re:Invent is only 4 weeks away! Join us live in Las Vegas from November 28–December 2 for keynote announcements, training and certification opportunities, access to 1,500+ technical sessions, and much more. Seats are still available to reserve, and walk-ups are available onsite. You can also join us online to watch live keynotes and leadership sessions.

If you are into machine learning like me, check out the ML attendee guide. AWS Machine Learning Hero Vinicius Caridá put together recommended sessions and tips and tricks for building your agenda. We also have attendee guides on additional topics and industries.

On November 2, there is a virtual event for building modern .NET applications on AWS. You can register for free.

On November 11–12, AWS User Groups in India are hosting the AWS Community Day India 2022, with success stories, use cases, and much more from industry leaders. Sign up for free to join this virtual event.

That’s all for this week. Check back next Monday for another Week in Review!

— Antje

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

AWS Named as a Leader in the 2022 Gartner Cloud Infrastructure & Platform Services (CIPS) Magic Quadrant for the 12th Consecutive Year

This post was originally published on this site

This year, and for the twelfth consecutive year, AWS has been named as a Leader in the 2022 Magic Quadrant for Cloud Infrastructure and Platform Services (CIPS). Per Gartner, AWS is the longest-running CIPS Magic Quadrant Leader.

AWS was among the first cloud providers when we launched Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3) 16 years ago. Our APIs have been adopted by the whole industry and often copied by others.

We believe this report validates AWS’s ability to innovate and deliver the broadest and deepest set of services for cloud computing. I encourage you to read the full report to appreciate the details.

As Jeff Bezos wrote in his first letter to shareholders in 1997 (reprinted at the end of each annual letter since then), Amazon makes decisions and weighs trade-offs differently than some companies. We focus on the long-term value rather than short-term profits, we make bold rather than timid investment decisions, and most importantly, we relentlessly focus on you: our customers. As a matter of fact, 90 percent of AWS’s roadmap for new services and capabilities is directly driven by your feedback and requests.

I work with AWS service teams every day. These teams work hard to innovate on your behalf. They make bold investments to invent, build, and operate services that help you innovate and build amazing experiences for your customers. The entire team is proud to see these efforts recognized by Gartner.

Our teams closely work with the vibrant AWS Partner Network. AWS has the largest and most dynamic community, with millions of active customers every month and more than 100,000 partners from over 150 countries—with almost 70% headquartered outside the United States. There is a real network effect when you use AWS.

The Magic Quadrant for CIPS, showing Amazon Web Services as a leader.

The full Gartner report has details about the features and factors they reviewed. It explains the methodology used and the results. This report can serve as a guide when choosing a cloud provider that helps you innovate on behalf of your customers.

— seb

Gartner, Magic Quadrant for Cloud Infrastructure and Platform Services, 19 October 2022, Raj Bala, et. al.

The Magic Quadrant graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from AWS.

Gartner does not endorse any vendor, product or service depicted in our research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.


Automated Endpoint Discovery

This post was originally published on this site

Tweet VMware Skyline Advisor Pro’s Automated Endpoint Detection feature, which currently supports Aria Operations (formerly vRealize Operations), Aria Automation (Formerly vRealize Automation), and Aria Suite Lifecycle (formerly vRealize Lifecycle Manager) will automatically detect unconnected endpoints and recommend that you connect them. And, if you’re currently a Skyline customer, here’s why this feature is such a … Continued

The post Automated Endpoint Discovery appeared first on VMware Support Insider.

Upcoming Critical OpenSSL Vulnerability: What will be Affected?, (Thu, Oct 27th)

This post was originally published on this site

Some here may still remember Heartbleed. Heartbleed was a critical OpenSSL vulnerability that surprised many organizations, and patching the issue was a major undertaking. Heartbleed caused OpenSSL and other open source projects to rethink how they address security issues and how they communicate with their users. OpenSSL started to pre-announce any security updates about a week ahead of time.

Introducing Amazon Neptune Serverless – A Fully Managed Graph Database that Adjusts Capacity for Your Workloads

This post was originally published on this site

Amazon Neptune is a fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. With Neptune, you can use open and popular graph query languages to execute powerful queries that are easy to write and perform well on connected data. You can use Neptune for graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security.

Neptune has always been fully managed and handles time-consuming tasks such as provisioning, patching, backup, recovery, failure detection and repair. However, managing database capacity for optimal cost and performance requires you to monitor and reconfigure capacity as workload characteristics change. Also, many applications have variable or unpredictable workloads where the volume and complexity of database queries can change significantly. For example, a knowledge graph application for social media may see a sudden spike in queries due to sudden popularity.

Introducing Amazon Neptune Serverless
Today, we’re making that easier with the launch of Amazon Neptune Serverless. Neptune Serverless scales automatically as your queries and your workloads change, adjusting capacity in fine-grained increments to provide just the right amount of database resources that your application needs. In this way, you pay only for the capacity you use. You can use Neptune Serverless for development, test, and production workloads and optimize your database costs compared to provisioning for peak capacity.

With Neptune Serverless you can quickly and cost-effectively deploy graphs for your modern applications. You can start with a small graph, and as your workload grows, Neptune Serverless will automatically and seamlessly scale your graph databases to provide the performance you need. You no longer need to manage database capacity and you can now run graph applications without the risk of higher costs from over-provisioning or insufficient capacity from under-provisioning.

With Neptune Serverless, you can continue to use the same query languages (Apache TinkerPop Gremlin, openCypher, and RDF/SPARQL) and features (such as snapshots, streams, high availability, and database cloning) already available in Neptune.

Let’s see how this works in practice.

Creating an Amazon Neptune Serverless Database
In the Neptune console, I choose Databases in the navigation pane and then Create database. For Engine type, I select Serverless and enter my-database as the DB cluster identifier.

Console screenshot.

I can now configure the range of capacity, expressed in Neptune capacity units (NCUs), that Neptune Serverless can use based on my workload. I can now choose a template that will configure some of the next options for me. I choose the Production template that by default creates a read replica in a different Availability Zone. The Development and Testing template would optimize my costs by not having a read replica and giving access to DB instances that provide burstable capacity.

Console screenshot.

For Connectivity, I use my default VPC and its default security group.

Console screenshot.

Finally, I choose Create database. After a few minutes, the database is ready to use. In the list of databases, I choose the DB identifier to get the Writer and Reader endpoints that I am going to use later to access the database.

Using Amazon Neptune Serverless
There is no difference in the way you use Neptune Serverless compared to a provisioned Neptune database. I can use any of the query languages supported by Neptune. For this walkthrough, I choose to use openCypher, a declarative query language for property graphs originally developed by Neo4j that was open-sourced in 2015 and contributed to the openCypher project.

To connect to the database, I start an Amazon Linux Amazon Elastic Compute Cloud (Amazon EC2) instance in the same AWS Region and associate the default security group and a second security group that gives me SSH access.

With a property graph I can represent connected data. In this case, I want to create a simple graph that shows how some AWS services are part of a service category and implement common enterprise integration patterns.

I use curl to access the Writer openCypher HTTPS endpoint and create a few nodes that represent patterns, services, and service categories. The following commands are split into multiple lines in order to improve readability.

curl https://<my-writer-endpoint>:8182/openCypher 
-d "query=CREATE (mq:Pattern {name: 'Message Queue'}),
(pubSub:Pattern {name: 'Pub/Sub'}),
(eventBus:Pattern {name: 'Event Bus'}),
(workflow:Pattern {name: 'WorkFlow'}),
(applicationIntegration:ServiceCategory {name: 'Application Integration'}),
(sqs:Service {name: 'Amazon SQS'}), (sns:Service {name: 'Amazon SNS'}),
(eventBridge:Service {name: 'Amazon EventBridge'}), (stepFunctions:Service {name: 'AWS StepFunctions'}),
(sqs)-[:IMPLEMENT]->(mq), (sns)-[:IMPLEMENT]->(pubSub),

This is a visual representation of the nodes and their relationships for the graph created by the previous command. The type (such as Service or Pattern) and properties (such as name) are shown inside each node. The arrows represent the relationships (such as CONTAIN or IMPLEMENT) between the nodes.

Visualization of graph data.

Now, I query the database to get some insights. To query the database, I can use either a Writer or a Reader endpoint. First, I want to know the name of the service implementing the “Message Queue” pattern. Note how the syntax of openCypher resembles that of SQL with MATCH instead of SELECT.

curl https://<my-endpoint>:8182/openCypher 
-d "query=MATCH (s:Service)-[:IMPLEMENT]->(p:Pattern {name: 'Message Queue'}) RETURN;"
  "results" : [ {
    "" : "Amazon SQS"
  } ]

I use the following query to see how many services are in the “Application Integration” category. This time, I use the WHERE clause to filter results.

curl https://<my-endpoint>:8182/openCypher 
-d "query=MATCH (c:ServiceCategory)-[:CONTAIN]->(s:Service) WHERE'Application Integration' RETURN count(s);"
  "results" : [ {
    "count(s)" : 4
  } ]

There are many options now that I have this graph database up and running. I can add more data (services, categories, patterns) and more relationships between the nodes. I can focus on my application and let Neptune Serverless manage capacity and infrastructure for me.

Availability and Pricing
Amazon Neptune Serverless is available today in the following AWS Regions: US East (Ohio, N. Virginia), US West (N. California, Oregon), Asia Pacific (Tokyo), and Europe (Ireland, London).

With Neptune Serverless, you only pay for what you use. The database capacity is adjusted to provide the right amount of resources you need in terms of Neptune capacity units (NCUs). Each NCU is a combination of approximately 2 gibibytes (GiB) of memory with corresponding CPU and networking. The use of NCUs is billed per second. For more information, see the Neptune pricing page.

Having a serverless graph database opens many new possibilities. To learn more, see the Neptune Serverless documentation. Let us know what you build with this new capability!

Simplify the way you work with highly connected data using Neptune Serverless.


Why is My Cat Using Baidu? And Other IoT DNS Oddities, (Wed, Oct 26th)

This post was originally published on this site

My cat, Gluon, is having a problem. Last year, a new cat, Einstein, invaded her property, and since then, she has no longer ventured outside after some unfortunate encounters with Einstein. Gluon now spends most of her time inside doing cat stuff like grooming and sleeping; unfortunately, she has gained an unhealthy amount of weight. To help, we got her an automated cat feeder to better control her food intake. The cat feeder is sporting not just the obligatory WiFi and Cloud/App connectivity but also a camera, so it was immediately moved to our "IoT" network.

AWS Batch for Amazon Elastic Kubernetes Service

This post was originally published on this site

Today I’m pleased to announce AWS Batch for Amazon Elastic Kubernetes Service (Amazon EKS). AWS Batch for Amazon EKS is ideal for customers who no longer want to shoulder the burden of configuring, fine-tuning, and managing Kubernetes clusters and pods to use with their batch processing workflows. Furthermore, there is no charge for this service. You only pay for the resources that your batch jobs launch.

When I’ve previously considered Kubernetes, it appeared to be focused on the management and hosting of microservice workloads. I was therefore surprised to discover that Kubernetes is also used by some customers to run large-scale, compute-intensive batch workloads. The differences between batch and microservice workloads mean that using Kubernetes for batch processing can be difficult and requires you to invest significant time in custom configuration and management to fine-tune a suitable solution.

Microservice and batch workloads on Kubernetes
Before we look further at AWS Batch for Amazon EKS, let’s consider some of the important differences between batch and microservice workloads to help set some context on why running batch workloads on Kubernetes can be difficult:

  • Microservice workloads are assumed to start and not stop—we expect them to be continuously available. In contrast, batch workloads run to completion and then exit—regardless of success or failure.
  • The results from a batch workload might not be available for several minutes—and sometimes hours or even days. Microservice workloads are expected to respond to requests within milliseconds.
  • We usually deploy microservice workloads across several Availability Zones to ensure high availability. This isn’t a requirement for batch workloads. Although we might distribute a batch job to allow it to process different input data in a distributed analysis, we more typically want to prioritize fast and optimal access to resources the job needs within the Availability Zone in which it is running.
  • Microservice and batch workloads scale differently. For microservices, scaling is generally predictable and usually linear as load increases (or decreases). With batch workloads, you might first perform an initial, or infrequently repeated, proof-of-concept run to analyze performance and discover the correct tuning needed for a full production run. The difference in size between the two can be exponential. Furthermore, with batch workloads, we might scale to an extreme level for a run, then scale back to zero instances for long periods of time, sometimes months.

Although third-party frameworks can help with running batch workloads on Kubernetes, you can also roll your own. Whichever approach you take, significant gaps and challenges can remain in handling the undifferentiated heavy lifting of building, configuring, and maintaining custom batch solutions. Then you also need to consider the scheduling, placing, and scaling of batch workloads on Kubernetes in a cost-effective manner. So how does AWS Batch on Amazon EKS help?

AWS Batch for Amazon EKS
AWS Batch for Amazon EKS offers a fully managed service to run batch workloads using clusters hosted on Amazon Elastic Compute Cloud (Amazon EC2) with no need to install and manage complex, custom batch solutions to address the differences highlighted earlier. AWS Batch provides a scheduler that controls and runs high-volume batch jobs, together with an orchestration component that evaluates when, where, and how to place jobs submitted to a queue. There’s no need for you, as the user, to coordinate any of this work—you just submit a job request into the queue.

Job queueing, dependency tracking, retries, prioritization, compute resource provisioning for Amazon Elastic Compute Cloud (EC2) and Amazon Elastic Compute Cloud (EC2) Spot, and pod submission are all handled using a serverless queue. As a managed service, AWS Batch for Amazon EKS enables you to reduce your operational and management overhead and focus instead on your business requirements. It provides integration with other services such as AWS Identity and Access Management (IAM), Amazon EventBridge, and AWS Step Functions and allows you to take advantage of other partners and tools in the Kubernetes ecosystem.

When running batch jobs on Amazon EKS clusters, AWS Batch is the main entry point to submit workload requests. Based on the queued jobs, AWS Batch then launches worker nodes in your cluster to process the jobs. These nodes are kept separate in a distinct namespace from your other node groups in Amazon EKS. Similarly, nodes in other pods are isolated from those used with AWS Batch.

How it works
AWS Batch uses managed Amazon EKS clusters, which need to be registered with AWS Batch, and permissions set so that AWS Batch can launch and manage compute environments in those clusters to process jobs submitted to the queue. You can find instructions on how to launch a managed cluster that AWS Batch can use in this topic in the Amazon EKS User Guide. Instructions for configuring permissions can be found in the AWS Batch User Guide.

Once one or more clusters have been registered, and permissions set, users can submit jobs to the queue. When a job is submitted, the following actions take place to process the request:

  • On receiving a job request, the queue dispatches a request to the configured compute environment for resources. If an AWS Batch managed scaling group does not yet exist, one is created, and AWS Batch then starts launching Amazon Elastic Compute Cloud (EC2) instances in the group. These new instances are added to the AWS Batch Kubernetes namespace of the cluster.
  • The Kubernetes scheduler places any configured DaemonSet on the node.
  • Once the node is ready, AWS Batch starts sending pod placement requests to your cluster, using labels and taints to make the placement choices for the pods, bypassing much of the logic of the k8s scheduler.
  • This process is repeated, scaling as needed across more EC2 instances in the scaling group until the maximum configured capacity is reached.
  • If the job queue has another compute environment defined, such as one configured to use Spot instances, it will launch additional nodes in that compute environment.
  • Once all work is complete, AWS Batch removes the nodes from the cluster, and terminates the instances.

These steps are illustrated in the animation below.

Animation showing the steps AWS Batch takes when processing a request using an Amazon EKS cluster

Start using your clusters with AWS Batch today
AWS Batch for Amazon Elastic Kubernetes Service (Amazon EKS) is available today. As I noted earlier, there is no charge for this service, and you pay only for the resources your jobs consume. To learn more, visit the Getting Started with Amazon EKS topic in the AWS Batch User Guide. There is also a self-guided workshop to help introduce you to AWS Batch on Amazon EKS.

— Steve

AWS Week in Review – October 24, 2022

This post was originally published on this site

Last week, we announced plans to launch the AWS Asia Pacific (Bangkok) Region, which will become our third AWS Region in Southeast Asia. This Region will have three Availability Zones and will give AWS customers in Thailand the ability to run workloads and store data that must remain in-country.

In the Works – AWS Region in Thailand
With this big news, AWS announced a 190 billion baht (US 5 billion dollars) investment to drive Thailand’s digital future over the next 15 years. It includes capital expenditures on the construction of data centers, operational expenses related to ongoing utilities and facility costs, and the purchase of goods and services from Regional businesses.

Since we first opened an office in Bangkok in 2015, AWS has launched 10 Amazon CloudFront edge locations, a highly secure and programmable content delivery network (CDN) in Bangkok. In 2020, we launched AWS Outposts, a family of fully managed solutions delivering AWS infrastructure and services to virtually any on-premises or edge location for a truly consistent hybrid experience in Thailand. This year, we also plan the upcoming launch of an AWS Local Zone in Bangkok, which will enable customers to deliver applications that require single-digit millisecond latency to end users in Thailand.

Photo courtesy of Conor McNamara, Managing Director, ASEAN at AWS

The new AWS Region in Thailand is also part of our broader, multifaceted investment in the country, covering our local team, partners, skills, and the localization of services, including Amazon Transcribe, Amazon Translate, and Amazon Connect.

Many Thailand customers have chosen AWS to run their workloads to accelerate innovation, increase agility, and drive cost savings, such as 2C2P, CP All Plc., Digital Economy Promotion Agency, Energy Response Co. Ltd. (ENRES), PTT Global Public Company Limited (PTT), Siam Cement Group (SCG), Sukhothai Thammathirat Open University, The Stock Exchange of Thailand, Papyrus Studio, and more.

For example, Dr. Werner Vogels, CTO of, introduced the story of Papyrus Studio, a large film studio and one of the first customers in Thailand.

“Customer stories like Papyrus Studio inspire us at AWS. The cloud can allow a small company to rapidly scale and compete globally. It also provides new opportunities to create, innovate, and identify business opportunities that just aren’t possible with conventional infrastructure.”

For more information on how to enable AWS and get support in Thailand, contact our AWS Thailand team.

Last Week’s Launches
My favorite news of last week was to launch dark mode as a beta feature in the AWS Management Console. In Unified Settings, you can choose between three settings for visual mode: Browser default, Light, and Dark. Browser default applies the default dark or light setting of the browser, dark applies the new built-in dark mode, and light maintains the current look and feel of the AWS console. Choose your favorite!

Here are some launches that caught my eye for web, mobile, and IoT application developers:

New AWS Amplify Library for Swift – We announce the general availability of Amplify Library for Swift (previously Amplify iOS). Developers can use Amplify Library for Swift via the Swift Package Manager to build apps for iOS and macOS (currently in beta) platforms with Auth, Storage, Geo, and more features.

The Amplify Library for Swift is open source on GitHub, and we deeply appreciate the feedback we have gotten from the community. To learn more, see Introducing the AWS Amplify Library for Swift in the AWS Front-End Web & Mobile Blog or Amplify Library for Swift documentation.

New Amazon IVS Chat SDKs – Amazon Interactive Video Service (Amazon IVS) now provides SDKs for stream chat with support for web, Android, and iOS. The Amazon IVS stream chat SDKs support common functions for chat room resource management, sending and receiving messages, and managing chat room participants.

Amazon IVS is a managed, live-video streaming service using the broadcast SDKs or standard streaming software such as Open Broadcaster Software (OBS). The service provides cross-platform player SDKs for playback of Amazon IVS streams you need to make low-latency live video available to any viewer around the world. Also, it offers Chat Client Messaging SDK. For more information, see Getting Started with Amazon IVS Chat in the AWS documentation.

New AWS Parameters and Secrets Lambda Extension – This is new extension for AWS Lambda developers to retrieve parameters from AWS Systems Manager Parameter Store and secrets from AWS Secrets Manager. Lambda function developers can leverage this extension to improve their application performance as it decreases the latency and the cost of retrieving parameters and secrets.

Previously, you had to initialize either the core library of a service or the entire service SDK inside a Lambda function for retrieving secrets and parameters. Now you can simply use the extension. To learn more, see AWS Systems Manager Parameter Store documentation and AWS Secrets Manager documentation.

New FreeRTOS Long Term Support Version – We announce the second release of FreeRTOS Long Term Support (LTS) – FreeRTOS 202210.00 LTS. FreeRTOS LTS offers a more stable foundation than standard releases as manufacturers deploy and later update devices in the field. This release includes new and upgraded libraries such as AWS IoT Fleet Provisioning, Cellular LTE-M Interface, coreMQTT, and FreeRTOS-Plus-TCP.

All libraries included in this FreeRTOS LTS version will receive security and critical bug fixes until October 2024. With an LTS release, you can continue to maintain your existing FreeRTOS code base and avoid any potential disruptions resulting from FreeRTOS version upgrades. To learn more, see the FreeRTOS announcement.

Here is some news on performance improvement and increasing capacity:

Up to 10X Improving Amazon Aurora Snapshot Exporting Speed – Amazon Aurora MySQL-Compatible Edition for MySQL 5.7 and 8.0 now speed up to 10x faster snapshot exports to Amazon S3. The performance improvement is automatically applied to all types of database snapshot exports, including manual snapshots, automated system snapshots, and snapshots created by the AWS Backup service. For more information, see Exporting DB cluster snapshot data to Amazon S3 in the Amazon Aurora documentation.

3X Increasing Amazon RDS Read Capacity – Amazon Relational Database Service (RDS) for MySQL, MariaDB, and PostgreSQL now supports 15 read replicas per instance, including up to 5 cross-Region read replicas, delivering up to 3x the previous read capacity. For more information, see Working with read replicas in the Amazon RDS documentation.

2X Increasing AWS Snowball Edge Compute Capacity – The AWS Snowball Edge Compute Optimized device doubled the compute capacity up to 104 vCPUs, doubled the memory capacity up to 416GB RAM, and is now fully SSD with 28TB NVMe storage. The updated device is ideal when you need dense compute resources to run complex workloads such as machine learning inference or video analytics at the rugged, mobile edge such as trucks, aircraft or ships.  You can get started by ordering a Snowball Edge device on the AWS Snow Family console.

2X Increasing Amazon SQS FIFO Default Quota – Amazon Simple Queue Service (SQS) announces the increase of default quota up to 6,000 transactions per second per API action. It is double the previous 3,000 throughput quota for a high throughput mode for FIFO (first in, first out) queues in all AWS Regions where Amazon SQS FIFO queue is available. For a detailed breakdown of default throughput quotas per Region, see Quotas related to messages in the Amazon SQS documentation.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Here are some other news items that you may find interesting:

22 New or Updated Open Datasets on AWS – We released 22 new or updated datasets, including Amazonia-1 imagery, Bitcoin and Ethereum data, and elevation data over the Arctic and Antarctica. The full list of publicly available datasets is on the Registry of Open Data on AWS and is now also discoverable on AWS Data Exchange.

Sustainability with AWS Partners (ft. AWS On Air) – This episode covers a broad discipline of environmental, social, and governance (ESG) issues across all regions, organization types, and industries. AWS Sustainability & Climate Tech provides a comprehensive portfolio of AWS Partner solutions built on AWS that address climate change events and the United Nation’s Sustainable Development Goals (SDG).

AWS Open Source News and Updates #131 – This newsletter covers latest open-source projects such as Amazon EMR Toolkit for VS Code, a VS Code Extension to make it easier to develop Spark jobs on EMR and AWS CDK For Discourse, sample codes that demonstrates how to create a full environment for Discourse, etc. Remember to check out the Open source at AWS keep up to date with all our activity in open source by following us on @AWSOpen.

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

AWS re:Invent 2022 Attendee Guide – Browse re:Invent 2022 attendee guides, curated by AWS Heroes, AWS industry teams, and AWS Partners. Each guide contains recommended sessions, tips and tricks for building your agenda, and other useful resources. Also, seat reservations for all sessions are now open for all re:Invent attendees. You can still register for AWS re:Invent either offline or online.

AWS AI/ML Innovation Day on October 25 – Join us for this year’s AWS AI/ML Innovation Day, where you’ll hear from Bratin Saha and other leaders in the field about the great strides AI/ML has made in the past and the promises awaiting us in the future.

AWS Container Day at Kubecon 2022 on October 25–28 – Come join us at KubeCon + CloudNativeCon North America 2022, where we’ll be hosting AWS Container Day Featuring Kubernetes on October 25 and educational sessions at our booth on October 26–28. Throughout the event, our sessions focus on security, cost optimization, GitOps/multi-cluster management, hybrid and edge compute, and more.

You can browse all upcoming in-person, and virtual events.

That’s all for this week. Check back next Monday for another Week in Review!

— Channy

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

PSScriptAnalyzer (PSSA) 1.21.0 has been released

This post was originally published on this site


We are happy to announce the release of PSScriptAnalyzer 1.21. This minor version update includes:

  • Three new rules
  • Enhances one rule
  • Pipeline support to Invoke-Formatter
  • Four bug fixes
  • Improved message details in PSUseCorrectCasing
  • Lots of documentation updates
  • Updated system requirements

A big portion of changes are in less visible areas such as the maintenance of dependencies and CI.

System requirements

Starting with this version the minimum requirements are changing as follows:

  • For PowerShell ‘Core’ users ($PSVersionTable.PSEdition -eq 'Core'):

    The minimum required PowerShell version increases from 7.0.3 to 7.0.11. With PowerShell 7.0
    reached end of life this December, the next version of PSScriptAnalyzer will require 7.2.

    Use $PSVersionTable.PSVersion to see which version of PowerShell you are using. For more
    information about supported versions of PowerShell, see the
    PowerShell support lifecycle.

  • For Windows PowerShell users (version 3 to 5.1 or ($PSVersionTable.PSEdition -ne 'Core'))

    The minimum required version of the .NET Framework runtime increased from 4.5.2 to 4.6.2.
    Versions 4.5.2., 4.6 and 4.6.1 have reached end of support this year. This change should not
    impact the majority of users on supported and patched OS versions.

    For more details about .NET support, see
    .NET Framework blog post.

For more information, see the full changelog.

New Rules


This rule is to call out the usage of multiple type attributes in a function that the parser accepts
as valid syntax but can later cause unexpected behavior or an error at runtime.

For example, the following function

function foo { [switch][string] $Param1 }

will throw the following error when the function is invoked:

Cannot convert the “” value of type “System.String” to type “System.Management.Automation.SwitchParameter”

Thanks to GitHub user hankyi95 for contributing this rule.


The rule detects the usage of a trailing semicolon at the end of a line, which is not required in
most PowerShell scripts and can be omitted. This rule is not enabled by default and can be used by

The latest Preview version of the PowerShell extension for Visual Studio Code already includes
PSScriptAnalyzer 1.21.0 and can control this formatting rule via a new
powershell.codeFormatting.avoidSemicolonsAsLineTerminators setting. This should soon be available
in the non-preview version of the extension as well.

Thanks to Aliaksei (GitHub user tempora-mutantur) for contributing this rule.


This rule inspects the Get-FileHash cmdlet for usage of either MD5 or SHA-1 as the value
to the -Algorithm parameter since those algorithms are no longer considered secure. Users
should consider replacing the value with SHA256, SHA384, SHA512, or other safer algorithms
where possible. It is understood there will be legacy or backwards compatibility cases where this
cannot be done. PSScriptAnalyzer has a suppression feature that can be used to document the
reason for it in your code.

Thanks to Michael Van Leeuwen (GitHub user MJVL) for contributing this rule.


Invoke-Formatter now accepts input via the pipeline, either just as a string or
[pscustomobject] with parameter values.

The AvoidUsingPlainTextForPassword rule is the first built-in rule to now return more than one
suggestion for correction. In addition to suggesting the usage of SecureString, it now offers
an alternative suggestion with PSCredential.

With version 2.40.0 of the az CLI, its entrypoint changed from a batch script to an az.ps1
script. Since this script just passed its received arguments as $args as-is to python, it is still
a CLI and not a script with parameters. But it causes now a AvoidUsingPositionalParameters
warning, which is a false positive. We therefore added a CommandAllowList configuration to it,
which has az in it by default.

Documentation changes

In order to keep documentation all consolidated in one location, we have moved the documentation in
the repo to the repo of Please see the documentation notice in the
repo in order to get more specific details.


Expect more formatting rules and options in the next release and continue giving us feedback. We are
looking to increase our release frequency now that the Microsoft internal build has moved to a new
system, which is what caused some delay to this release. To reduce maintenance, we are looking to
drop support for PowerShell version 3 and 4 in future versions of PSScriptAnalyzer, which would also
align it with the PowerShell extension for Visual Studio Code where this change was successful. The
first step could be to increase the value of the PowerShellVersion property in the module manifest
but still ship the version specific binaries so that it would still work if one changed its value in
the manifest.

On behalf of the Script Analyzer team,

Christoph Bergmeister, Project Maintainer from the community, Avanade

Jim Truher, Senior Software Engineer, PowerShell Team, Microsoft

The post PSScriptAnalyzer (PSSA) 1.21.0 has been released appeared first on PowerShell Team.