Tag Archives: news

AWS Week in Review – July 4, 2022

Post Syndicated from Marcia Villalba original https://aws.amazon.com/blogs/aws/aws-week-in-review-july-04-2022/

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!

Summer has arrived in Finland, and these last few days have been hotter than in the Canary Islands! Today in the US it is Independence Day. I hope that if you are celebrating, you’re having a great time. This week I’m very excited about some developer experience and artificial intelligence launches.

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

AWS SAM Accelerate is now generally available – SAM Accelerate is a new capability of the AWS Serverless Application Model CLI, which makes it easier for serverless developers to test code changes against the cloud. You can do a hot swap of code directly in the cloud when making a change in your local development environment. This allows you to develop applications faster. Learn more about this launch in the What’s New post.

Amplify UI for React is generally available – Amplify UI is an open-source UI library that helps developers build cloud-native applications. Amplify UI for React comes with over 35 components that you can use, an authentication component that allows you to connect to your backend with no extra configuration, theming for your components. You can also build your UI using Figma. Check the Amplify UI for React site to learn more about all the capabilities offered.

Amazon Connect has new announcements – First, Amazon Connect added support to personalize the flows of the customer experience using Amazon Lex sentiment analysis. It also added support to branch out the flows depending on Amazon Lex confidence scores. Lastly, it added confidence scores to Amazon Connect Customer Profiles to help companies merge duplicate customer records.

Amazon QuickSight – QuickSight authors can now learn and experience Q before signing up. Authors can choose from six different sample topics and explore different visualizations. In addition, QuickSight now supports Level Aware Calculations (LAC) and rolling date functionality. These two new features bring flexibility and simplification to customers to build advanced calculation and dashboards.

Amazon SageMaker – RStudio on SageMaker now allows you to bring your own development environment in a custom image. RStudio on SageMaker is a fully managed RStudio Workbench in the cloud. In addition, SageMaker added four new tabular data modeling algorithms: LightGBM, CatBoost, AutoGluon-Tabular, and TabTransformer to the existing set of built-in algorithms, pre-trained models and pre-built solution templates it provides.

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

Other AWS News
Some other updates and news that you may have missed:

AWS Support announced an improved experience when creating a case – There is a new interface for creating support cases in the AWS Support Center console. Now you can create a case with a simplified three-step process that guides you through the flow. Learn more about this new process in the What’s new post.

New AWS Step Functions workflows collection on Serverless Land – The Step Functions workflows collection is a new experience that makes it easier to discover, deploy, and share AWS Step Functions workflows. In this collection, you can find opinionated templates that implement the best practices to build using Step Functions. Learn more about this new collection in Ben’s blog post.

Podcast Charlas Técnicas de AWS – If you understand Spanish, this podcast is for you. Podcast Charlas Técnicas is one of the official AWS Podcasts in Spanish, which shares a new episode ever other week. The podcast is meant for builders, and it shares stories about how customers implement and learn AWS, how to architect applications, and how to use new services. You can listen to all the episodes directly from your favorite podcast app or from the AWS Podcasts en español website.

AWS open-source news and updates – A newsletter curated by my colleague Ricardo brings you the latest open-source projects, posts, events, and more.

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

AWS Summit New York – Join us on July 12 for the in-person AWS Summit. You can register on the AWS Summit page for free.

AWS re:Inforce – This is an in-person learning conference with a focus on security, compliance, identity, and privacy. You can register now to access hundreds of technical sessions, and other content. It will take place July 26 and 27 in Boston, MA.

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

— Marcia

AWS Week in Review – June 27, 2022

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-week-in-review-june-27-2022/

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!

It’s the beginning of a new week, and I’d like to start with a recap of the most significant AWS news from the previous 7 days. Last week was special because I had the privilege to be at the very first EMEA AWS Heroes Summit in Milan, Italy. It was a great opportunity of mutual learning as this community of experts shared their thoughts with AWS developer advocates, product managers, and technologists on topics such as containers, serverless, and machine learning.

Participants at the EMEA AWS Heroes Summit 2022

Last Week’s Launches
Here are the launches that got my attention last week:

Amazon Connect Cases (available in preview) – This new capability of Amazon Connect provides built-in case management for your contact center agents to create, collaborate on, and resolve customer issues. Learn more in this blog post that shows how to simplify case management in your contact center.

Many updates for Amazon RDS and Amazon AuroraAmazon RDS Custom for Oracle now supports Oracle database 12.2 and 18c, and Amazon RDS Multi-AZ deployments with one primary and two readable standby database instances now supports M5d and R5d instances and is available in more Regions. There is also a Regional expansion for RDS Custom. Finally, PostgreSQL 14, a new major version, is now supported by Amazon Aurora PostgreSQL-Compatible Edition.

AWS WAF Captcha is now generally available – You can use AWS WAF Captcha to block unwanted bot traffic by requiring users to successfully complete challenges before their web requests are allowed to reach resources.

Private IP VPNs with AWS Site-to-Site VPN – You can now deploy AWS Site-to-Site VPN connections over AWS Direct Connect using private IP addresses. This way, you can encrypt traffic between on-premises networks and AWS via Direct Connect connections without the need for public IP addresses.

AWS Center for Quantum Networking – Research and development of quantum computers have the potential to revolutionize science and technology. To address fundamental scientific and engineering challenges and develop new hardware, software, and applications for quantum networks, we announced the AWS Center for Quantum Networking.

Simpler access to sustainability data, plus a global hackathon – The Amazon Sustainability Data Initiative catalog of datasets is now searchable and discoverable through AWS Data Exchange. As part of a new collaboration with the International Research Centre in Artificial Intelligence, under the auspices of UNESCO, you can use the power of the cloud to help the world become sustainable by participating to the Amazon Sustainability Data Initiative Global Hackathon.

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

Other AWS News
A couple of takeaways from the Amazon re:MARS conference:

Amazon CodeWhisperer (preview) – Amazon CodeWhisperer is a coding companion powered by machine learning with support for multiple IDEs and languages.

Synthetic data generation with Amazon SageMaker Ground TruthGenerate labeled synthetic image data that you can combine with real-world data to create more complete training datasets for your ML models.

Some other updates you might have missed:

AstraZeneca’s drug design program built using AWS wins innovation award – AstraZeneca received the BioIT World Innovative Practice Award at the 20th anniversary of the Bio-IT World Conference for its novel augmented drug design platform built on AWS. More in this blog post.

Large object storage strategies for Amazon DynamoDB – A blog post showing different options for handling large objects within DynamoDB and the benefits and disadvantages of each approach.

Amazon DevOps Guru for RDS under the hoodSome details of how DevOps Guru for RDS works, with a specific focus on its scalability, security, and availability.

AWS open-source news and updates – A newsletter curated by my colleague Ricardo to bring you the latest open-source projects, posts, events, and more.

Upcoming AWS Events
It’s AWS Summits season and here are some virtual and in-person events that might be close to you:

On June 30, the AWS User Group Ukraine is running an AWS Tech Conference to discuss digital transformation with AWS. Join to learn from many sessions including a fireside chat with Dr. Werner Vogels, CTO at Amazon.com.

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

Danilo

New – Amazon SageMaker Ground Truth Now Supports Synthetic Data Generation

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/new-amazon-sagemaker-ground-truth-now-supports-synthetic-data-generation/

Today, I am happy to announce that you can now use Amazon SageMaker Ground Truth to generate labeled synthetic image data.

Building machine learning (ML) models is an iterative process that, at a high level, starts with data collection and preparation, followed by model training and model deployment. And especially the first step, collecting large, diverse, and accurately labeled datasets for your model training, is often challenging and time-consuming.

Let’s take computer vision (CV) applications as an example. CV applications have come to play a key role in the industrial landscape. They help improve manufacturing quality or automate warehouses. Yet, collecting the data to train these CV models often takes a long time or can be impossible.

As a data scientist, you might spend months collecting hundreds of thousands of images from the production environments to make sure you capture all variations in data the model will come across. In some cases, finding all data variations might even be impossible, for example, sourcing images of rare product defects, or expensive, if you have to intentionally damage your products to get those images.

And once all data is collected, you need to accurately label the images, which is often a struggle in itself. Manually labeling images is slow and open to human error, and building custom labeling tools and setting up scaled labeling operations can be time-consuming and expensive. One way to mitigate this data challenge is by adding synthetic data to the mix.

Advantages of Combining Real-World Data with Synthetic Data
Combining your real-world data with synthetic data helps to create more complete training datasets for training your ML models.

Synthetic data itself is created by simple rules, statistical models, computer simulations, or other techniques. This allows synthetic data to be created in enormous quantities and with highly accurate labels for annotations across thousands of images. The label accuracy can be done at a very fine granularity, such as on a sub-object or pixel level, and across modalities. Modalities include bounding boxes, polygons, depth, and segments. Synthetic data can also be generated for a fraction of the cost, especially when compared to remote sensing imagery that otherwise relies on satellite, aerial, or drone image collection.

If you combine your real-world data with synthetic data, you can create more complete and balanced data sets, adding data variety that real-world data might lack. With synthetic data, you have the freedom to create any imagery environment, including edge cases that might be difficult to find and replicate in real-world data. You can customize objects and environments with variations, for example, to reflect different lighting, colors, texture, pose, or background. In other words, you can “order” the exact use case you are training your ML model for.

Now, let me show you how you can start sourcing labeled synthetic images using SageMaker Ground Truth.

Get Started on Your Synthetic Data Project with Amazon SageMaker Ground Truth
To request a new synthetic data project, navigate to the Amazon SageMaker Ground Truth console and select Synthetic data.

Amazon SageMaker Ground Truth Synthetic Data

Then, select Open project portal. In the project portal, you can request new projects, monitor projects that are in progress, and view batches of generated images once they become available for review. To initiate a new project, select Request project.

Amazon SageMaker Ground Truth Synthetic Data Project Portal

Describe your synthetic data needs and provide contact information.

Request a synthetic data project

After you submit the request form, you can check your project status in the project dashboard.

Amazon SageMaker Ground Truth Synthetic Data Project Created

In the next step, an AWS expert will reach out to discuss your project requirements in more detail. Upon review, the team will share a custom quote and project timeline.

If you want to continue, AWS digital artists will start by creating a small test batch of labeled synthetic images as a pilot production for you to review.

They collect your project inputs, such as reference photos and available 2D and 3D assets. The team then customizes those assets, adds the specified inclusions, such as scratches, dents, and textures, and creates the configuration that describes all the variations that need to be generated.

They can also create and add new objects based on your requirements, configure distributions and locations of objects in a scene, as well as modify object size, shape, color, and surface texture.

Once the objects are prepared, they are rendered using a photorealistic physics engine, capturing an image of the scene from a sensor that is placed in the virtual world. Images are also automatically labeled. Labels include 2D bounding boxes, instance segmentation, and contours.

You can monitor the progress of the data generation jobs on the project detail page. Once the pilot production test batch becomes available for review, you can spot-check the images and provide feedback for any rework that might be required.

Review available batches of synthetic data

Select the batch you want to review and View details
Sample batch of synthetic data in Amazon SageMaker Ground Truth

In addition to the images, you will also receive output image labels, metadata such as object positions, and image quality metrics as Amazon SageMaker compatible JSON files.

Synthetic Image Fidelity and Diversity Report
With each available batch of images, you also receive a synthetic image fidelity and diversity report. This report provides image and object level statistics and plots that help you make sense of the generated synthetic images.

The statistics are used to describe the diversity and the fidelity of the synthetic images and compare them with real images. Examples of the statistics and plots provided are the distributions of object classes, object sizes, image brightness, and image contrast, as well as the plots evaluating the indistinguishability between synthetic and real images.

Synthetic Image Fidelity and Diversity Report

Once you approve the pilot production test batch, the team will move to the production phase and start generating larger batches of labeled synthetic images with your desired label types, such as 2D bounding boxes, instance segmentation, and contours. Similar to the test batch, each production batch of images will be made available for you together with the image fidelity and diversity report to spot-check, accept, or reject.

All images and artifacts will be available for you to download from your S3 bucket once final production is complete.

Availability
Amazon SageMaker Ground Truth synthetic data is available in US East (N. Virginia). Synthetic data is priced on a per-label basis. You can request a custom quote that is tailored to your specific use case and requirements by filling out the project requirement form.

Learn more about SageMaker Ground Truth synthetic data on our Amazon SageMaker Data Labeling page.

Request your synthetic data project through the Amazon SageMaker Ground Truth console today!

— Antje

Now in Preview – Amazon CodeWhisperer- ML-Powered Coding Companion

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-in-preview-amazon-codewhisperer-ml-powered-coding-companion/

As I was getting ready to write this post I spent some time thinking about some of the coding tools that I have used over the course of my career. This includes the line-oriented editor that was an intrinsic part of the BASIC interpreter that I used in junior high school, the IBM keypunch that I used when I started college, various flavors of Emacs, and Visual Studio. The earliest editors were quite utilitarian, and grew in sophistication as CPU power become more plentiful. At first this increasing sophistication took the form of lexical assistance, such as dynamic completion of partially-entered variable and function names. Later editors were able to parse source code, and to offer assistance based on syntax and data types — Visual Studio‘s IntelliSense, for example. Each of these features broke new ground at the time, and each one had the same basic goal: to help developers to write better code while reducing routine and repetitive work.

Announcing CodeWhisperer
Today I would like to tell you about Amazon CodeWhisperer. Trained on billions of lines of code and powered by machine learning, CodeWhisperer has the same goal. Whether you are a student, a new developer, or an experienced professional, CodeWhisperer will help you to be more productive.

We are launching in preview form with support for multiple IDEs and languages. To get started, you simply install the proper AWS IDE Toolkit, enable the CodeWhisperer feature, enter your preview access code, and start typing:

CodeWhisperer will continually examine your code and your comments, and present you with syntactically correct recommendations. The recommendations are synthesized based on your coding style and variable names, and are not simply snippets.

CodeWhisperer uses multiple contextual clues to drive recommendations including the cursor location in the source code, code that precedes the cursor, comments, and code in other files in the same projects. You can use the recommendations as-is, or you can enhance and customize them as needed. As I mentioned earlier, we trained (and continue to train) CodeWhisperer on billions of lines of code drawn from open source repositories, internal Amazon repositories, API documentation, and forums.

CodeWhisperer in Action
I installed the CodeWhisperer preview in PyCharm and put it through its paces. Here are a few examples to show you what it can do. I want to build a list of prime numbers. I type # See if a number is pr. CodeWhisperer offers to complete this, and I press TAB (the actual key is specific to each IDE) to accept the recommendation:

On the next line, I press Alt-C (again, IDE-specific), and I can choose between a pair of function definitions. I accept the first one, and CodeWhisperer recommends the function body, and here’s what I have:

I write a for statement, and CodeWhisperer recommends the entire body of the loop:

CodeWhisperer can also help me to write code that accesses various AWS services. I start with # create S3 bucket and TAB-complete the rest:

I could show you many more cool examples, but you will learn more by simply joining the preview and taking CodeWhisperer for a spin.

Join the Preview
The preview supports code written in Python, Java, and JavaScript, using VS Code, IntelliJ IDEA, PyCharm, WebStorm, and AWS Cloud9. Support for the AWS Lambda Console is in the works and should be ready very soon.

Join the CodeWhisperer preview and let me know what you think!

Jeff;

AWS IoT ExpressLink Now Generally Available – Quickly Develop Devices That Connect Securely to AWS Cloud

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/aws-iot-expresslink-now-generally-available-quickly-develop-devices-that-connect-securely-to-aws-cloud/

At AWS re:Invent 2021, we introduced AWS IoT ExpressLink, software for partner-manufactured connectivity modules that makes it easier and faster for original equipment manufacturers to connect any type of product to the cloud, such as industrial sensors, small and large home appliances, irrigation systems, and medical devices.

Today we announce the general availability of AWS IoT ExpressLink and the related connectivity modules offered by AWS Partners, such as EspressifInfineon, and u-blox. The modules contain built-in cloud-connectivity software implementing AWS-mandated security requirements. Integrating these wireless modules into the hardware design of your device makes it faster and easier to securely connect Internet of Things (IoT) devices to the AWS Cloud and integrate with a range of AWS services.

Connecting devices to the AWS cloud requires developers to add tens of thousands of lines of new code to their processor of devices, which demands specialized skills. Merging this new code with their application code also requires a deep understanding of networking and cryptography to ensure the device is both functional and implementing AWS managed security requirements.

Some devices are too resource-constrained to support cloud connectivity, meaning their processors are too small or slow to handle the additional code. For example, a small piece of equipment, like a pool pump, may contain a tiny processor that is optimized to drive a particular type of motor but does not have the memory space or the performance necessary to handle both the motor and a cloud connection.

Modules with AWS IoT ExpressLink include simple codes required to connect the device to the cloud, thereby reducing the development cycle and accelerating time to market. To take the pool pump from the previous example, you can keep the tiny processor in the equipment, and delegate the heavy lifting of connecting to the cloud to AWS IoT ExpressLink, allowing the manufacturer to make the simple application software, and avoid costly redesign.

Modules with AWS IoT ExpressLink feature best practices for device-to-cloud connectivity and security as manufacturing partners incorporate AWS-mandated security requirements designed to help protect devices from remote attacks and to help achieve a secure connection to the AWS Cloud. These include the following provisioning and security procedures:

  • Cryptographically signed certificate with unique device ID.
  • Cryptographically secured boot based in a hardware root of trust.
  • Transport Layer Security (TLS v1.2 or higher) encryption of wireless network connections.
  • Encryption of all sensitive data stored on the module, both in transit and at rest.
  • Hardware root of trust for secrets storage and application code segregation.
  • Compliance with security regression test suite.
  • Verification of communication interfaces (Command Line Interface, Wi-Fi, BLE, or Cellular) against memory corruption attacks.
  • Support for cryptographically secured AWS IoT over-the-air (OTA) firmware updates to keep the devices up to date with new features and security patches.

AWS IoT ExpressLink natively integrates with AWS IoT services, such as AWS IoT Device Management, to help customers easily monitor and update their device fleets at scale.

How AWS IoT ExpressLink Works
I’ll explain how AWS IoT ExpressLink communicates with AWS partner modules and allows you to simply connect to the cloud.

For example, Infineon’s IFW56810 is a single-band Wi-Fi 4 connectivity module that provides a simple, secure solution for connecting products to AWS IoT cloud services. The IFW56810 module is preprogrammed with a tested secured firmware of AWS IoT ExpressLink implementation and supports an easy-to-use AWS IoT ExpressLink AT command interface for configuration.

To get started, connect the IFW956810 evaluation kit to the PC using either the Type-C connector or Type-A male to Type-C female cable. Run a serial terminal to communicate with the kit over USB by choosing the higher of the two enumerated COM ports on Windows with the following configuration. Once you open the serial terminal after configuring your setting, such as baudrate, type AT in the serial terminal. You should see a response OK.

You can also send AWS IoT ExpressLink commands as simple as CONNECT, SEND, and SUBSCRIBE to start communicating with the cloud. The device will translate these commands, make an MQTT connection, and send messages to AWS IoT Core.

Whether you are using a Wi-Fi or a cellular LTE-M module, you can make the most basic telemetry application that can be expressed in 10 lines of pseudo-code as follows.

int main()
{
    print("AT+CONNECT\n");
    while(1){
        print("AT+SEND data {\"A\"=%d}", getSensorA());
        delays(1);
    }
}

To learn more, visit the AWS IoT ExpressLink programmer’s guide.

Customer Stories
Many of our customers use AWS IoT ExpressLink to offload the complex but undifferentiated work required to securely connect devices to the AWS Cloud, which improves the developer experience by reducing the design effort, and helping them deliver product faster.

Cardinal Peak is a Colorado-based product engineering services company that reduces the risk of outsourcing an engineering project. Cardinal Peak specializes in developing connected products in multiple markets, including audio, video, security, health care and others. With design skills in hardware, electronics, embedded, cloud and end-user software, Cardinal Peak provides end-to-end design services for its clients.

Keegan Landreth, Embedded Software Engineer at Cardinal Peak said:

“AWS IoT ExpressLink allowed me to put together a WiFi-connected product demo sending sensor data to the cloud in a single afternoon! Secure networking for embedded systems has never been this easy. It’s an almost completely transparent interface between my application and AWS, as simple as printing data to a serial port. Being able to do OTA firmware updates through it is a huge value add-on. The best part is that I can reuse the same code to make a cellular version, which is unheard of!”

ēdn makes SmallGarden, cloud-powered indoor smart gardening products to let you easily grow plants providing light, water, nutrients, and heat as necessary at home.

Ryan Woltz, CEO of ēdn, said:

“We were looking for a quick and easy way to enable robust cloud capabilities for our indoor gardening product lines. However, from past experience, we knew that doing so adds significant risk in terms of time, money, and overall go-to-market execution. IoT device connectivity is complex, forcing our team to either outsource the development to a costly third party or allocate internal engineering resources, significantly delaying innovative features that differentiate our offerings in the market. Even a small misstep in the implementation of provisioning, security, or over-the-air functionality can set a product back months.

Now, thanks to u-blox’s hardware module with AWS IoT ExpressLink, we can enable secure and reliable cloud connectivity for our devices within days. This not only allows us to accelerate product development, but it ensures our engineering team remains focused on shipping leading-edge technologies that make nature accessible indoors.”

u-blox is an AWS Partner with a broad portfolio of chips, modules, and services. Harald Kroell, Product Manager at u-blox, said:

“At u-blox, with AWS IoT ExpressLink, we strengthen our Wi-Fi and LTE-M portfolio and bring silicon-to-cloud connectivity to the next level. By bridging our hardware and services with the AWS cloud, we progress on our mission to make businesses wirelessly connected and build solutions to last an IoT lifetime.

With the SARA-R5 and NORA-W2 modules with AWS IoT ExpressLink, customers can connect products with two different wireless technologies to AWS with a single homogeneous interface, which significantly reduces development effort. It also enables new business opportunities by lowering the barrier of connecting devices, which previously would have been too expensive to connect.”

To get started, order SARA-R5 Starter Kit and USB-NORA-W256AWS with its development kit user guide, including modules powered by AWS IoT ExpressLink.

AWS IoT ExpressLink Partners
As in the case of u-blox, two other AWS Partners, Infineon Technologies AG and Espressif Systems, have developed wireless modules that support a range of connectivity options, including Wi-Fi and cellular, and are powered by AWS IoT ExpressLink. All qualified devices in the AWS Partner Device Catalog are available for purchase from AWS Partners.

Infineon Technologies AG specializes in semiconductor solutions the goal of which is to make life easier, safer, and greener. Sivaram Trikutam, Vice President, Wi-Fi Product Line at Infineon Technologies, said:

“We’re excited to be working with AWS on the AIROC™ IFW56810 Cloud Connectivity Manager (CCM) solution supporting AWS IoT ExpressLink. With this plug-and-play solution, developers and engineers no longer need to create complex code or possess a wide range of technical competencies in Wi-Fi, embedded systems, antenna design, and cloud configuration.

Now, they can easily, quickly, and securely connect devices at scale to AWS, so they can focus on creating new revenue streams and getting to market faster. We are excited to work with our partner AWS on new business opportunities that help our customers meet their needs.”

Espressif Systems is a multinational, fabless semiconductor company with a strong focus on providing connectivity solutions to internet-connected devices. Amey Inamdar, Director of Technical Marketing, Espressif Systems, said:

“At Espressif, we continuously strive to provide secure, green, versatile, and cost-effective AIoT solutions with a focus on ease of use for our customers. The AWS IoT ExpressLink program fits well into that philosophy, providing a convenient AWS IoT connectivity.

It enables customers to seamlessly transform their offline product into a cloud-connected product by offloading the complexity to the module with AWS IoT ExpressLink, with reduced development costs and a faster time to market and hence lowering the barrier to entry to build secure connected devices. Espressif is proud to participate in this program with Espressif’s module with AWS IoT ExpressLink to provide secure and affordable AWS IoT connectivity.”

Order and Get Started Now
You can discover a range of Partner-provided modules with AWS IoT ExpressLink in the AWS Partner Device Catalog. Order your evaluation kits with AWS IoT ExpressLink today. The kit will include an application processor or will connect to compatible development platforms such as Arduino.

You can then immediately start sending telemetry data to the cloud through the simple AWS IoT ExpressLink serial interface. You can use sample codes for integrating an AWS IoT ExpressLink module into an application. These examples are intended to demonstrate how to perform the common operations for an IoT device.

To learn more, visit the product page. Please send feedback to AWS re:Post for AWS IoT ExpressLink or through your usual AWS support contacts.

Channy

New – High Volume Outbound Communication with Amazon Connect Outbound Campaigns

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/new-high-volume-outbound-communication-with-amazon-connect-outbound-campaigns/

The new high volume outbound communication capability in Amazon Connect which was announced at Enterprise Connect last year, is now generally available to all. It is named Amazon Connect outbound campaigns.

If you haven’t heard about Amazon Connect, it is an easy-to-use cloud contact center service that helps companies of any size deliver superior customer service at lower cost. You can read the original blog post Jeff wrote at launch in 2017, with amazing Lego art 🙂

Contact centers not only receive calls and communications, but they also send outbound communications to customers. There are a variety of reasons to send outbound communication: appointment reminders, telemarketing, subscription renewals, and billing reminders. The vast majority of these communications are phone calls, and in many contact centers, agents make the calls manually using customer contact lists in external systems. Since customers only answer about ten percent of calls, these agents can spend nearly half of their time dialing and waiting. This can result in millions of dollars in lost productivity each year for a contact center with as few as 200 agents.

To help you to address this challenge, today we are adding to Amazon Connect outbound campaigns a set of high-volume outbound communication capabilities that allows you to proactively reach more of your customers across voice, SMS, and email. When using this capability, you will have a scalable way for proactive outreach for hundreds to millions of your customers, and you will increase your agents’ productivity and lower your operational costs.

Amazon Connect outbound campaigns delivers a predictive phone dialer. The dialer includes an answering machine detection system powered by machine learning. It allows the automatic detection of answering machines for voice calls and passes calls to agents only when the call is answered by a human. The dialer also adjusts the call rate depending on factors such as percentage of human-answered the calls, call duration, and agent availability. There is no integration required to get the benefit of existing Amazon Connect features, such as automated workflows, routing, and machine learning capabilities like Contact Lens. You now have a single system for inbound and outbound communications.

To further refine the customer experience or use multiple channels in your campaigns, for example, to send an SMS or email message to your customers when they do not answer calls, you have the option to use Amazon Pinpoint. Amazon Pinpoint is a flexible and scalable outbound and inbound marketing communications service. It allows you to define customer segments, define the customer journey, define the contact strategy, and more. Amazon Pinpoint is the system handling high-volume SMS and email campaigns.

To better understand how Amazon Connect, Amazon Pinpoint, and other AWS services work together, you can refer to this very detailed blog post.

Let’s show you how it works
Imagine I am a contact center manager, and I want to create an outbound call campaign to target a selected list of customers.

I first import my customer contact list from a spreadsheet on Amazon S3. I may also import it from popular customer relationship management (CRM) and marketing automation applications, such as Marketo, Salesforce, Twilio’s Segment, ServiceNow, Shopify, Zendesk, and Amazon Pinpoint itself.

Amazon Connect outbound campaigns - import contact 2

Then I create a campaign and define some journey parameters: the communication channel, the start time, and the corresponding content, such as a call script, email template, or SMS message. At the scheduled start time, the journey is executed using Amazon Connect for calls or Amazon Pinpoint for SMS or emails, as specified.

Amazon Connect outbound campaigns - create campaign

When I configure the campaign to run in Predictive dial mode, as I mentioned before, the dialer automatically adjusts the dial rate based on the duration of calls and the real-time availability of agents. Once a call is answered, Amazon Connect distinguishes whether it is a live voice or a recorded message and routes the live customer to an available agent in the Amazon Connect agent application, where the agent can see the call script that I specified during setup, along with relevant customer information.

As explained earlier, I may use Amazon Pinpoint to define the customer journey. By doing so, I can combine voice, email, and SMS channels in the same outbound communication campaign to improve the efficiency of my agents and my customer’s experience. For example, a financial institution can use Amazon Connect to send an SMS notification to remind a customer of a missed payment and include a link to request a call back from an agent. When a call is requested, Amazon Connect automatically queues the call, dials the customer’s number, detects their voice, and connects an available agent to the customer.

Amazon Connect outbound campaigns - journey workflow

Amazon Pinpoint allows you to define the details of the customer journey.

Amazon Connect outbound campaigns - setup quiet times

As usual with AWS services, I can analyze contact events sent via Amazon EventBridge. EventBridge is a serverless event bus that makes it easier to build event-driven applications at scale using events generated from your applications, integrated software-as-a-service (SaaS) applications, and AWS service. When filtering or analyzing events posted to EventBridge, I can create metrics such as time to connect to an agent, duration of the contact, and call abandonment rate

These metrics help me understand the status of my campaign and ensure compliance with applicable regulations, such as maximum call abandonment rates. I also can use historical reports of these metrics to understand the effectiveness of all my communications campaigns over time.

Amazon Connect outbound campaigns - jounrey metrics

Speaking of compliance, we do not want anyone to abuse the system, intentionally or not, or to break any local compliance rules.

Access and Compliance
Using automated services to drive outbound communication campaigns is strictly regulated in several countries and territories. For example, the US adopted the Telephone Consumer Protection Act (TCPA) in 1991, and the United Kingdom’s Office of Communications has similar rules.

Amazon Connect outbound campaigns gives you the tools to stay compliant with these regulations and many others. However, just like with traditional IT security, it is a shared responsibility. It is your responsibility to use the service in a compliant manner. We are happy to assist you in addressing specific use cases.

Let’s share two examples to illustrate how Amazon Connect outbound campaigns can help you meet your compliance status: respect quiet time and monitor call abandonment rate.

The use of quiet times allows contact center managers to configure a schedule for channel communications based on the day of the week and the hours of the day. More precise delivery times means your customers are most likely to engage with the communication and increase metrics such as open rates for SMS and email, as well as pick-up rates for voice calls. It also allows contact center managers to follow country and state-level voice dialing legislation. The following screenshot shows how you can configure quiet times using Amazon Pinpoint.

Amazon Connect outbound campaigns - quiet times

According to TCPA, call abandonment rate is the percentage of calls picked up by a live customer but not connected to a live agent within two seconds after the customer greeting. I found it interesting that in the UK, the time is measured from the start of your customer greetings, while in the US, it is measured from the end of the greeting. Amazon Connect outbound campaigns provides you with metrics, such as customerGreetingStart, customerGreetingStop, andconnectedToAgent for each outbound communication. Contact center managers can use these to compute the abandonment rate and dial up or down the outgoing communication channel accordingly.

Other metrics, configuration parameters, and AWS Lambda API integration allow contact center managers to consult a Do-Not-Call (DNC) registry or list scrubbing and verify your customer’s local time zone or bank holiday calendars, just to name a few.

Pricing and Availability
Amazon Connect outbound campaigns is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (London) AWS Regions. This allows you to start your outbound campaigns for customers in the USA, UK, Australia, and New Zealand.

As usual, pricing is based on your usage; you only pay for what you use with no upfront or minimum engagement. The key metrics we are using for pricing are the minutes of outbound calls. The pricing page has all the details.

And now, go build your contact centers.

— seb

AWS Week in Review – June 20, 2022

Post Syndicated from Steve Roberts original https://aws.amazon.com/blogs/aws/aws-week-in-review-june-20-2022/

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!

Last Week’s Launches
It’s been a quiet week on the AWS News Blog, however a glance at What’s New page shows the various service teams have been busy as usual. Here’s a round-up of announcements that caught my attention this past week.

Support for 15 new resource types in AWS Config – AWS Config is a service for assessment, audit, and evaluation of the configuration of resources in your account. You can monitor and review changes in resource configuration using automation against a desired configuration. The newly expanded set of types includes resources from Amazon SageMaker, Elastic Load Balancing, AWS Batch, AWS Step Functions, AWS Identity and Access Management (IAM), and more.

New console experience for AWS Budgets – A new split-view panel allows for viewing details of a budget without needing to leave the overview page. The new panel will save you time (and clicks!) when you’re analyzing performance across a set of budgets. By the way, you can also now select multiple budgets at the same time.

VPC endpoint support is now available in Amazon SageMaker Canvas SageMaker Canvas is a visual point-and-click service enabling business analysts to generate accurate machine-learning (ML) models without requiring ML experience or needing to write code. The new VPC endpoint support, available in all Regions where SageMaker Canvas is suppported, eliminates the need for an internet gateway, NAT instance, or a VPN connection when connecting from your SageMaker Canvas environment to services such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, and more.

Additional data sources for Amazon AppFlow – Facebook Ads, Google Ads, and Mixpanel are now supported as data sources, providing the ability to ingest marketing and product analytics for downstream analysis in AppFlow-connected software-as-a-service (SaaS) applications such as Marketo and Salesforce Marketing Cloud.

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

Other AWS News
Some other updates you may have missed from the past week:

Amazon Elastic Compute Cloud (Amazon EC2) expanded the Regional availability of AWS Nitro System-based C6 instance types. C6gn instance types, powered by Arm-based AWS Graviton2 processors, are now available in the Asia Pacific (Seoul), Europe (Milan), Europe (Paris), and Middle East (Bahrain) Regions, while C6i instance types, powered by 3rd generation Intel Xeon Scalable processors, are now available in the Europe (Frankfurt) Region.

As a .NET and PowerShell Developer Advocate here at AWS, there are some news and updates related to .NET I want to highlight:

Upcoming AWS Events
The AWS New York Summit is approaching quickly, on July 12. Registration is also now open for the AWS Summit Canberra, an in-person event scheduled for August 31.

Microsoft SQL Server users may be interested in registering for the SQL Server Database Modernization webinar on June 21. The webinar will show you how to go about modernizing and how to cost-optimize SQL Server on AWS.

Amazon re:MARS is taking place this week in Las Vegas. I’ll be there as a host of the AWS on Air show, along with special guests highlighting their latest news from the conference. I also have some On Air sessions on using our AI services from .NET lined up! As usual, we’ll be streaming live from the expo hall, so if you’re at the conference, give us a wave. You can watch the show live on Twitch.tv/aws, Twitter.com/AWSOnAir, and LinkedIn Live.

A reminder that if you’re a podcast listener, check out the official AWS Podcast Update Show. There is also the latest installment of the AWS Open Source News and Updates newsletter to help keep you up to date.

No doubt there’ll be a whole new batch of releases and announcements from re:MARS, so be sure to check back next Monday for a summary of the announcements that caught our attention!

— Steve

AWS Week in Review – June 13, 2022

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-week-in-review-june-13-2022/

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!

Last Week’s Launches
I made a short trip to Austin, Texas last week in order to visit and learn from some customers. As is always the case, the days when I was traveling were filled with AWS launches; here’s my recap of a few that caught my eye:

R6id Instances – In her first post for the AWS News Blog, Senior Developer Advocate Veliswa Boya wrote about the new R6id instances. These are a variant of our sixth generation of x86-based R6i instances, and feature up to 7.6 TB of NVMe Local Instance Storage. Powered by 3rd Generation Intel Xeon Scalable (Ice Lake) processors, the instances offer higher compute performance, a new larger size (.32xlarge), always-on memory encryption, and double the EBS and network performance as the previous generation. The instances are available now in four AWS Regions.

AWS MGN Post-Launch Actions AWS Application Migration Service (AWS MGN) helps you to migrate your existing servers to AWS, with automation that can handle a wide variety of applications. We launched a set of optional post-migration actions to provide additional support for your migration and modernization efforts. The initial set of actions install the AWS Systems Manager agent, install the AWS Elastic Disaster Recovery Service Agent, migrate from CentOS to Rocky Linux, and convert SUSE Linux subscriptions to AWS-provided subscriptions. You can read my blog post to learn more.

Mainframe Modernization Service – This new service helps you to modernize your mainframe applications and to deploy them to AWS fully-managed runtime environments. As Seb notes in his post, Modernize Your Mainframe Applications & Deploy Them In The Cloud, the application modernization journey is composed of four phases: assessing the situation, mobilizing the project, migrating & modernizing, and operating & optimizing. The Mainframe Modernization Service provides assistance during each phase, and you can review each one in the blog post.

Amazon Aurora – We made multiple Amazon Aurora announcements (all for the PostgreSQL-compatible edition) including support for the Large Objects (LO) module, zero-downtime patching (ZDP), support for bug-fix versions 13.7, 12.11, 11.16, and 10.21, and updates to the pglogical and wal2json extensions.

Amazon SageMaker – There were also multiple announcements related to Amazon SageMaker and Amazon SageMaker Data Wrangler including the ability to split data into train and test sets with a few clicks, Data Wrangler support for model training with Amazon SageMaker Autopilot & the power to export features to Amazon SageMaker Feature Store, new interactive product tours & sample data sets for Amazon SageMaker Canvas, provisioning and management of ML models with CloudFormation templates, and Amazon SageMaker Experiments support for common chart types.

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 updates that caught my eye last week:

Open Source News – My colleague Ricardo Sueiras published installment #116 of his AWS Open Source News and Updates. He’s got the latest and greatest on tools for Lambda, Lambda@Edge, CDK, schema management, EMR, and a whole lot more!

aws-prod-infrastructure is an open source tool that generates Terraform code from an AWS production account. Terragen does the same thing, with pricing plans for hobbyists (free), professionals, and enterprises.

resoto creates an inventory of your cloud, provides deep visibility, and reacts to changes in your infrastructure.

green-boost from AWS Labs helps you to quickly build full stack serverless web apps on AWS.

Upcoming AWS Events
Here are some events that may be of interest to you:

re:Mars (June 21-24) – I’ll be heading to Las Vegas next week to attend re:Mars; there’s still some time to register and attend!

AWS Summits (June & July)AWS Summits will take place in-person in June (Toronto and Milan) and July (New York), with more cities on the list for later in the year.

And that’s all for this week!

Jeff;

New – Amazon EC2 R6id Instances with NVMe Local Instance Storage of up to 7.6 TB

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/new-amazon-ec2-r6id-instances/

In November 2021, we launched the memory-optimized Amazon EC2 R6i instances, our sixth-generation x86-based offering powered by 3rd Generation Intel Xeon Scalable processors (code named Ice Lake).

Today I am excited to announce a disk variant of the R6i instance: the Amazon EC2 R6id instances with non-volatile memory express (NVMe) SSD local instance storage. The R6id instances are designed to power applications that require low storage latency or require temporary swap space.

Customers with workloads that require access to high-speed, low-latency storage, including those that need temporary storage for scratch space, temporary files, and caches, have the option to choose the R6id instances with NVMe local instance storage of up to 7.6 TB. The new instances are also available as bare-metal instances to support workloads that benefit from direct access to physical resources.

Here’s some background on what led to the development of the sixth-generation instances. Our customers who are currently using fifth-generation instances are looking for the following:

  • Higher Compute Performance – Higher CPU performance to improve latency and processing time for their workloads
  • Improved Price Performance – Customers are very sensitive to price performance to optimize costs
  • Larger Sizes – Customers require larger sizes to scale their enterprise databases
  • Higher Amazon EBS Performance – Customers have requested higher Amazon EBS throughput (“at least double”) to improve response times for their analytics applications
  • Local Storage – Large customers have expressed a need for more local storage per vCPU

Sixth-generation instances address these requirements by offering generational improvement across the board, including 15 percent increase in price performance, 33 percent more vCPUs, up to 1 TB memory, 2x networking performance, 2x EBS performance, and global availability.

Compared to R5d instances, the R6id instances offer:

  • Larger instance size (.32xlarge) with 128 vCPUs and 1024 GiB of memory, enabling customers to consolidate their workloads and scale up applications.
  • Up to 15 percent improvement in compute price performance and 20 percent higher memory bandwidth.
  • Up to 58 percent higher storage per vCPU and 34 percent lower cost per TB.
  • Up to 50 Gbps network bandwidth and up to 40 Gbps EBS bandwidth; EBS burst bandwidth support for sizes up to .4xlarge.
  • Always-on memory encryption.
  • Support for new Intel Advanced Vector Extensions (AVX 512) instructions such as VAES, VCLMUL, VPCLMULQDQ, and GFNI for faster execution of cryptographic algorithms such as those used in IPSec and TLS implementations.

The detailed specifications of the R6id instances are as follows:

Instance Name

vCPUs RAM (GiB)

Local NVMe SSD Storage (GB)

EBS Throughput (Gbps)

Network Bandwidth (Gbps)

r6id.large 2 16 1 x 118 Up to 10 Up to 12.5
r6id.xlarge 4 32 1 x 237 Up to 10 Up to 12.5
r6id.2xlarge 8 64 1 x 474 Up to 10 Up to 12.5
r6id.4xlarge 16 128 1 x 950 Up to 10 Up to 12.5
r6id.8xlarge 32 256 1 x 1900 10 12.5
r6id.12xlarge 48 384 2 x 1425 15 18.75
r6id.16xlarge 64 512 2 x 1900 20 25
r6id.24xlarge 96 768 4 x 1425 30 37.5
r6id.32xlarge 128 1024 4 x 1900 40 50
r6id.metal 128 1024 4 x 1900 40 50

Now available

The R6id instances are available today in the AWS US East (Ohio), US East (N.Virginia), US West (Oregon), and Europe (Ireland) Regions as On-Demand, Spot, and Reserved Instances or as part of a Savings Plan. As usual, with EC2, you pay for what you use. For more information, see the Amazon EC2 pricing page.

To learn more, visit our Amazon EC2 R6i instances page, and please send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

Veliswa x

Modernize Your Mainframe Applications & Deploy Them In The Cloud

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/modernize-your-mainframe-applications-deploy-them-in-the-cloud/

Today, we are launching AWS Mainframe Modernization service to help you modernize your mainframe applications and deploy them to AWS fully-managed runtime environments. This new service also provides tools and resources to help you plan and implement migration and modernization.

Since the introduction of System/360 on April 7 1964, mainframe computers have enabled many industries to transform themselves. The mainframe has revolutionized the way people buy things, how people book and purchase travel, and how governments manage taxes or deliver social services. Two thirds of the Fortune 100 companies have their core businesses located on a mainframe. And according to a 2018 estimate, $3 trillion ($3 x 10^12) in daily commerce flows through mainframes.

Mainframes are using their very own set of technologies: programming languages such as COBOL, PL/1, and Natural, to name a few, or databases and data files such as VSAM, DB2, IMS DB, or Adabas. They also run “application servers” (or transaction managers as we call them) such as CICS or IMS TM. Recent IBM mainframes also run applications developed in the Java programming language deployed on WebSphere Application Server.

Many of our customers running mainframes told us they want to modernize their mainframe-based applications to take advantage of the AWS cloud. They want to increase their agility and their capacity to innovate, gain access to a growing pool of talents with experience running workloads on AWS, and benefit from the continual AWS trend of improving cost/performance ratio.

Application modernization is a journey composed of four phases:

  • First, you assess the situation. Are you ready to migrate? You define the business case and educate the migration team.
  • Second, you mobilize. You kick off the project, identify applications for a proof of concept, and refine your migration plan and business cases.
  • Third, you migrate and modernize. For each application, you run in-depth discovery, decide on the right application architecture and migration journey, replatform or refactor the code base, and test and deploy to production.
  • Last, you operate and optimize. You monitor deployed applications, manage resources, and ensure that security and compliance are up to date.

AWS Mainframe Modernization helps you during each phase of your journey.

Assess and Mobilize
During the assessment and mobilization phase, you have access to analysis and development tools to discover the scope of your application portfolio and to transform source code as needed. Typically, the service helps you discover the assets of your mainframe applications and identify all the data and other dependencies. We provide you with integrated development environments where you can adapt or refactor your source code, depending on whether you are replatforming or refactoring your applications.

Application Automated Refactoring
You may choose to use the automated refactoring pattern, where mainframe application assets are automatically converted into a modern language and ecosystem. With automated refactoring, AWS Mainframe Modernization uses Blu Age tools to convert your COBOL, PL/1, or JCL code to Java services and scripts. It generates modern code, data access, and data format by implementing patterns and rules to transform screens, indexed files, and batch applications to a modern application stack.

AWS Mainfraime Modernization Refactoring

Application Replatforming
You may also choose to replatform your applications, meaning move them to AWS with minimal changes to the source code. When replatforming, the fully-managed runtime comes preinstalled with the Micro Focus mainframe-compatible components, such as transaction managers, data mapping tools, screen and maps readers, and batch execution environments, allowing you to run your application with minimum changes.

AWS Mainfraime Modernization Replatforming

This blog post can help you learn more about nuances between replatforming and refactoring.

DevOps For Your Mainframe Applications
AWS Mainframe Modernization service provides you with AWS CloudFormation templates to easily create continuous integration and continuous deployment pipelines. It also deploys and configures monitoring services to monitor the managed runtime. This allows you to maintain or continue to evolve your applications once migrated, using best practices from Agile and DevOps methodologies.

Managed Services
AWS Mainframe Modernization takes care of the undifferentiated heavy lifting and provides you with fully managed runtime environments based on 15 years of cloud architecture best practices in terms of security, high availability, scalability, system management, and using infrastructure as code. These are all important for the business-critical applications running on mainframes.

The analysis tools, development tools, and the replatforming or refactoring runtimes come preinstalled and ready to use. But there is much more than preinstalled environments. The service deploys and manages the whole infrastructure for you. It deploys the required network, load balancer, and configure log collection with Amazon CloudWatch, among others. It manages application versioning, deployments, and high availability dependencies. This saves you days of designing, testing, automating, and deploying your own infrastructure.

The fully managed runtime includes extensive automation and managed infrastructure resources that you can operate via the AWS console, the AWS Command Line Interface (CLI), and application programming interfaces (APIs). This removes the burden and undifferentiated heavy lifting of managing a complex infrastructure. It allows you to spend time and focus on innovating and building new capabilities.

Let’s Deploy an App
As usual, I like to show you how it works. I am using a demo banking application. The application has been replatformed and is available as two .zip files. The first one contains the application binaries, and the second one the data files. I uploaded the content of these zipped files to an Amazon Simple Storage Service (Amazon S3) bucket. As part of the prerequisites, I also created a PostgreSQL Aurora database, stored its username and password in AWS Secrets Manager, and I created an encryption key in AWS Key Management Service (KMS).

Sample Banking Application files

Create an Environment
Let’s deploy and run the BankDemo sample application in an AWS Mainframe Modernization managed runtime environment with the Micro Focus runtime engine. For brevity, I highlight only the main steps. The full tutorial is available as part of the service documentation.

I open the AWS Management Console and navigate to AWS Mainframe Modernization. I navigate to Environments and select Create environment.

AWS Mainframe Migration - Create EnvironmentI give the environment a name and select Micro Focus runtime since we are deploying a replatformed application. Then I select Next.

AWS Mainframe Modernization - Create Environment 2In the Specify Configurations section, I leave all the default values: a Standalone runtime environment, the M2.m5.large EC2 instance type, and the default VPC and subnets. Then I select Next.

AWS Mainframe Modernization - Create Environment 3

On the Attach Storage section, I mount an EFS endpoint as /m2/mount/demo. Then I select Next.

AWS Mainframe Modernization - Create Environment 4In the Review and create section, I review my configuration and select Create environment. After a while, the environment status switches to Available.

AWS Mainframe Modernization - environment available

Create an Application
Now that I have an environment, let’s deploy the sample banking application on it. I select the Applications section and select Create application.

AWS Mainframe Modernization - Create ApplicatioI give my application a name, and under Engine type, I select Micro Focus.

AWS Mainframe Modernization - Create Application 2In the Specify resources and configurations section, I enter a JSON definition of my application. The JSON tells the runtime environment where my application’s various files are located and how to access Secrets Manager. You can find a sample JSON file in the tutorial section of the documentation.

AWS Mainframe Modernization - Create Application 3In the last section, I Review and create the application. I select Create application. After a moment, the application becomes available.

AWS Mainframe Modernization - application is availableOnce available, I deploy the application to the environment. I select the AWSNewsBlog-SampleBanking app, then I select the Actions dropdown menu, and I select Deploy application.

AWS Mainframe Modernization - deploy the appAfter a while, the application status changes to Ready.

Import Data sets
The last step before starting the application is to import its data sets. In the navigation pane, I select Applications, then choose AWSNewsBlog-SampleBank. I then select the Data sets tab and select Import. I may either specify the data set configuration values individually using the console or provide the location of an S3 bucket that contains a data set configuration JSON file.

AWS Mainframe Modernization - import data setsI use the JSON file provided by the tutorial in the documentation. Before uploading the JSON file to S3, I replace the $S3_DATASET_PREFIX variable with the actual value of my S3 bucket and prefix. For this example, I use awsnewsblog-samplebank/catalog.

AWS Mainframe Modernization - import data sets 2After a while, the data set status changes to Completed.

My application and its data set are now deployed into the cloud.

Start the Application
The last step is to start the application. I navigate to the Applications section. I then select AWSNewsBlog-SampleBank. In the Actions dropdown menu, I select Start application. After a moment, the application status changes to Running.

AWS Mainframe Modernization - application running

Access the Application
To access the application, I need a 3270 terminal emulator. Depending on your platform, a couple of options are available. I choose to use a web-based TN3270 web-based client provided by Micro Focus and available on the AWS Marketplace. I configure the terminal emulator to point it to the AWS Mainframe Modernization environment endpoint, and I use port 6000.

TN3270 Configuration

Once the session starts, I receive the CICS welcome prompt. I type BANK and press ENTER to start the app. I authenticate with user BA0001 and password A. The main application menu is displayed. I select the first option of the menu and press ENTER.

TN3270 SampleBank demo

Congrats, your replatformed application has been deployed in the cloud and is available through a standard IBM 3270 terminal emulator.

Pricing and Availability
AWS Mainframe Modernization service is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), Canada (Central), Europe (Frankfurt), Europe (Ireland), and South America (São Paulo).

You only pay for what you use. There are no upfront costs. Third-party license costs are included in the hourly price. Runtime environments for refactored applications, based on Blu Age, start at $2.50/hour. Runtime environments for replatformed applications, based on Micro Focus, start at $5.55/hour. This includes the software licenses (Blu Age or Micro Focus). As usual, AWS Support plans are available. They also cover Blu Age and Micro Focus software.

Committed plans are available for pricing discounts. The pricing details are available on the service pricing page.

And now, go build 😉

— seb

AWS MGN Update – Configure DR, Convert CentOS Linux to Rocky Linux, and Convert SUSE Linux Subscription

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-mgn-update-configure-dr-convert-centos-linux-to-rocky-linux-and-convert-suse-linux-subscription/

Just about a year ago, Channy showed you How to Use the New AWS Application Migration Server for Lift-and-Shift Migrations. In his post, he introduced AWS Application Migration Service (AWS MGN) and said:

With AWS MGN, you can minimize time-intensive, error-prone manual processes by automatically replicating entire servers and converting your source servers from physical, virtual, or cloud infrastructure to run natively on AWS. The service simplifies your migration by enabling you to use the same automated process for a wide range of applications.

Since launch, we have added agentless replication along with support for Windows 10 and multiple versions of Windows Server (2003, 2008, and 2022). We also expanded into additional regions throughout 2021.

New Post-Launch Actions
As the title of Channy’s post stated, AWS MGN initially supported direct, lift-and-shift migrations. In other words, the selected disk volumes on the source servers were directly copied, bit-for-bit to EBS volumes attached to freshly launched Amazon Elastic Compute Cloud (Amazon EC2) instances.

Today we are adding a set of optional post-launch actions that provide additional support for your migration and modernization efforts. The actions are initiated and managed by the AWS Systems Manager agent, which can be automatically installed as the first post-launch action. We are launching with an initial set of four actions, and plan to add more over time:

Install Agent – This action installs the AWS Systems Manager agent, and is a prerequisite to the other actions.

Disaster Recovery – Installs the AWS Elastic Disaster Recovery Service agent on each server and configures replication to a specified target region.

CentOS Conversion – If the source server is running CentOS, the instances can be migrated to Rocky Linux.

SUSE Subscription Conversion – If the source service is running SUSE Linux via a subscription provided by SUSE, the instance is changed to use an AWS-provided SUSE subscription.

Using Post-Launch Actions
My AWS account has a post-launch settings template that serves as a starting point, and provides the default settings each time I add a new source server. I can use the values from the template as-is, or I can customize them as needed. I open the Application Migration Service Console and click Settings to view and edit my template:

I click Post-launch settings template, and review the default values. Then I click Edit to make changes:

As I noted earlier, the Systems Manager agent executes the other post-launch actions, and is a prerequisite, so I enable it:

Next, I choose to run the post-launch actions on both my test and cutover instances, since I want to test against the final migrated configuration:

I can now configure any or all of the post-launch options, starting with disaster recovery. I check Configure disaster recovery on migrated servers and choose a target region:

Next, I check Convert CentOS to Rocky Linux distribution. This action converts a CentOS 8 distribution to a Rocky Linux 8 distribution:

Moving right along, I check Change SUSE Linux Subscription to AWS provided SUSE Linux subscription, and then click Save template:

To learn more about pricing for the SUSE subscriptions, visit the Amazon EC2 On-Demand Pricing page.

After I have set up my template, I can view and edit the settings for each of my source servers. I simply select the server and choose Edit post-launch settings from the Replication menu:

The post-launch actions will be run at the appropriate time on the test or the cutover instances, per my selections. Any errors that arise during the execution of an action are written to the SSM execution log. I can also examine the Migration dashboard for each source server and review the Post-launch actions:

Available Now
The post-launch actions are available now and you can start using them today in all regions where AWS Application Migration Service (AWS MGN) is supported.

Jeff;

AWS Week In Review – June 6, 2022

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/aws-week-in-review-june-6-2022/

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!

I’ve just come back from a long (extended) holiday weekend here in the US and I’m still catching up on all the AWS launches that happened this past week. I’m particularly excited about some of the data, machine learning, and quantum computing news. Let’s have a look!

Last Week’s Launches
The launches that caught my attention last week are the following:

Amazon EMR Serverless is now generally available Amazon EMR Serverless allows you to run big data applications using open-source frameworks such as Apache Spark and Apache Hive without configuring, managing, and scaling clusters. The new serverless deployment option for Amazon EMR automatically scales resources up and down to provide just the right amount of capacity for your application, and you only pay for what you use. To learn more, check out Channy’s blog post and listen to The Official AWS Podcast episode on EMR Serverless.

AWS PrivateLink is now supported by additional AWS services AWS PrivateLink provides private connectivity between your virtual private cloud (VPC), AWS services, and your on-premises networks without exposing your traffic to the public internet. The following AWS services just added support for PrivateLink:

  • Amazon S3 on Outposts has added support for PrivateLink to perform management operations on your S3 storage by using private IP addresses in your VPC. This eliminates the need to use public IPs or proxy servers. Read the June 1 What’s New post for more information.
  • AWS Panorama now supports PrivateLink, allowing you to access AWS Panorama from your VPC without using public endpoints. AWS Panorama is a machine learning appliance and software development kit (SDK) that allows you to add computer vision (CV) to your on-premises cameras. Read the June 2 What’s New post for more information.
  • AWS Backup has added PrivateLink support for VMware workloads, providing direct access to AWS Backup from your VMware environment via a private endpoint within your VPC. Read the June 3 What’s New post for more information.

Amazon SageMaker JumpStart now supports incremental model training and automatic tuning – Besides ready-to-deploy solution templates for common machine learning (ML) use cases, SageMaker JumpStart also provides access to more than 300 pre-trained, open-source ML models. You can now incrementally train all the JumpStart models with new data without training from scratch. Through this fine-tuning process, you can shorten the training time to reach a better model. SageMaker JumpStart now also supports model tuning with SageMaker Automatic Model Tuning from its pre-trained model, solution templates, and example notebooks. Automatic tuning allows you to automatically search for the best hyperparameter configuration for your model.

Amazon Transcribe now supports automatic language identification for multi-lingual audioAmazon Transcribe converts audio input into text using automatic speech recognition (ASR) technology. If your audio recording contains more than one language, you can now enable multi-language identification, which identifies all languages spoken in the audio file and creates a transcript using each identified language. Automatic language identification for multilingual audio is supported for all 37 languages that are currently supported for batch transcriptions. Read the What’s New post from Amazon Transcribe to learn more.

Amazon Braket adds support for Borealis, the first publicly accessible quantum computer that is claimed to offer quantum advantage – If you are interested in quantum computing, you’ve likely heard the term “quantum advantage.” It refers to the technical milestone when a quantum computer outperforms the world’s fastest supercomputers on a well-defined task. Until now, none of the devices claimed to demonstrate quantum advantage have been accessible to the public. The Borealis device, a new photonic quantum processing unit (QPU) from Xanadu, is the first publicly available quantum computer that is claimed to have achieved quantum advantage. Amazon Braket, the quantum computing service from AWS, has just added support for Borealis. To learn more about how you can test a quantum advantage claim for yourself now on Amazon Braket, check out the What’s New post covering the addition of Borealis support.

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

Other AWS News
Some other updates and news that you may have missed:

New AWS Heroes – A warm welcome to our newest AWS Heroes! The AWS Heroes program is a worldwide initiative that acknowledges individuals who have truly gone above and beyond to share knowledge in technical communities. Get to know them in the June 2022 introduction blog post!

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 #115 here.

Upcoming AWS Events
Join me in Las Vegas for Amazon re:MARS 2022. The conference takes place June 21–24 and is all about the latest innovations in machine learning, automation, robotics, and space. I will deliver a talk on how machine learning can help to improve disaster response. Say “Hi!” if you happen to be around and see me.

We also have more AWS Summits coming up over the next couple of months, both in-person and virtual.

In Europe:

In North America:

In South America:

Find an AWS Summit near you, and get notified when registration opens in your area.

Imagine Conference 2022You can now register for IMAGINE 2022 (August 3, Seattle). The IMAGINE 2022 conference is a no-cost event that brings together education, state, and local leaders to learn about the latest innovations and best practices in the cloud.

Sign up for the SQL Server Database Modernization webinar on June 21 to learn how to modernize and cost-optimize Microsoft SQL Server on AWS.

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

— Antje

Introducing the newest AWS Heroes – June 2022

Post Syndicated from Ross Barich original https://aws.amazon.com/blogs/aws/introducing-the-newest-aws-heroes-june-2022/

AWS Heroes are some of the worlds most active and vocal leaders in AWS communities, recognized for their unwavering focus on sharing insights and technical knowledge with others. Heroes have a variety of contributions to community learning: they host events, meetups, and workshops, author blogs, contribute to open source projects, speak at conferences, and more. You can view some of their prominent content in the AWS Heroes Content Library.

Today we are thrilled to introduce to the world the latest cohort of AWS Heroes:

Adam Bien – Munich, Germany

DevTools Hero Adam Bien is an independent Architect, Consultant, Developer, Trainer, conference speaker, and podcaster. Adam started with Java since JDK 1.0 and still enjoys writing serverless Java, often in Amazon Corretto. He also codes live on YouTube. Adam uses CDK in greenfield serverless Java applications, as well as to help his clients migrate their on-premise Java applications to the AWS cloud. He likes to apply Java’s pragmatic patterns and best practices to serverless runtimes, especially AWS Lambda and AWS Fargate. High productivity, reduction of complexity, and cost effectiveness are his main focuses.

Adam Elmore – Nixa, USA

DevTools Hero Adam Elmore is an independent cloud consultant who helps startups build products on AWS. He’s also the host of AWS FM, a podcast with guests from around the AWS community, and the creator of the AWS Community on Twitter. Adam is passionate about open source and has made a handful of contributions to the AWS CDK over the years. In 2020 he created Ness, an open source CLI tool for deploying web sites and apps to AWS. Previously, Adam co-founded StatMuse—a Disney backed startup building technology that answers sports questions—and served as CTO for five years.

Brooke Jamieson – Brisbane, Australia

Machine Learning Hero Brooke Jamieson is the Head of Enablement – AI/ML and Data at Blackbook.ai, and is an international conference speaker. Brooke specializes in researching & developing technically robust solutions that help “non-data people” harness the power of Artificial Intelligence and Machine Learning for their industry. Outside of their ‘day job’, Brooke is a dedicated member of the AWS Community and is a regular speaker at local user groups, global events, and guest lectures at multiple Australian Universities. They also make entry-level cloud career and technical content on TikTok, to reach broad audiences and diverse groups wanting to transition to careers in AI/ML and Cloud. Brooke is an Advisory Board member of Women in Digital, and strives to promote STEM pathways to young people in regional Australia & members of the LGBTIQA+ community.

Chao Cai – Beijing, China

Community Hero Chao Cai has 15 years of world-class experience in software development, including more than 10 years as a software architect. He is currently the VP and Chief Architect at Mobvista Inc. Chao is passionate about sharing his knowledge and experience to the community. His WeChat public account has more than 4,000 followers and over 34,000 engineers have taken his online courses. Chao is a respected leader in the China tech community. He is invited as the speaker to the global tech conferences, such as, QCon and ArchSummit each year. As an active advocate for AWS, Chao is also a regular speaker at AWS tech events.

Cyril Bandolo – Douala, Cameroon

Machine Learning Hero Cyril Bandolo is a data scientist working as a Senior Manager Data Analytics at Yoomee Mobile. Cyril has a natural talent and passion for teaching and transferring knowledge in his machine learning blog, where he focuses on building and deploying end-to-end machine learning projects on AWS. On his YouTube channel, he recently launched a weekly live hands-on series called “Sagemaker Saturdays” during which, every weekend he walks the viewers through end-to-end machine learning projects with Sagemaker Studio Lab and Sagemaker Studio. Cyril is always trying to encounter and apply new machine learning solutions to make lives better and help move the bottom line.

Kristi Perreault – Denver, USA

Serverless Hero Kristi Perreault is a Principal Software Engineer at Liberty Mutual Insurance, where her focus is serverless first development, and enterprise enablement. She holds an M.S. in Electrical & Computer Engineering specializing in cloud computing & IoT, and is very passionate about promoting women in technology. She organizes the Serverless Denver user group as part of ServerlessDays, co-organizes CDK Day, and writes extensively about serverless and diversity on her dev.to and Medium blog sites. You’ll find her speaking about embracing and scaling serverless first initiatives on dozens of podcasts, webinars, conferences, and meetups both virtually and on stage.

Sanchit Jain – Mumbai, India

Community Hero Sanchit Jain is the AWS Analytics Practice Lead and a certified expert specializing in AWS Cloud at Quantiphi Inc. He is also the AWS User Group Mumbai Lead and actively contributes to the AWS community by delivering sessions at AWS User Groups, AWS Community Days, and various educational institutes. He also shares his knowledge by publishing blogs about AWS services, architectures, and best practices. Recently, Sanchit hosted an AWS Solution Architect Certification Bootcamp, which spanned over two months, with 7500+ viewers. He also delivered a session recently at AWS Summit India 2022 on Building a data lake with AWS Lake Formation.

Shigeru Oda – Saitama, Japan

Community Hero Shigeru Oda is an expert system engineer at NSD CO., LTD. Since 2020 he has run 25 events with the JAWS-UG Beginners Chapter (about 4200 registered members). In September 2020 he promoted the 24-hour online event JAWS SONIC 2020 & MIDNIGHT JAWS 2020, which was attended by about 1500 people. In March 2021, he promoted JAWS DAYS 2021 as a steering member, which was attended by about 4,000 people. And in November 2021, he promoted JAWS PANKRATION 2021, a second 24-hour online event, providing 900 AWS users in Japan, as well as around the world with an opportunity to learn beyond the language barrier of English and Japanese through simultaneous interpretation. He received the AWS Samurai 2021 Award for these activities.

Yasunori Kirimoto – Sapporo, Japan

DevTools Hero Yasunori Kirimoto is currently the Co-Founder and CTO of MIERUNE Inc. and owner of dayjournal. He specializes in the field of GIS (Geographic Information System) and FOSS4G (Free Open Source Software for GeoSpatial). Yasunori’s work includes contributions to the Amazon Location Service samples on GitHub and open source projects such as AWS Amplify and AWS CDK. He has also published numerous blog posts on AWS CloudFormation and other AWS architectures. When he’s not acting as a bridge between AWS and the location-based information field, he engages with the open-source community and enjoys participating in venture projects to gain a broader understanding of technology.

 

 

 

If you’d like to learn more about the new Heroes, or connect with a Hero near you, please visit the AWS Heroes website or browse the AWS Heroes Content Library.

Ross

Amazon EMR Serverless Now Generally Available – Run Big Data Applications without Managing Servers

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/amazon-emr-serverless-now-generally-available-run-big-data-applications-without-managing-servers/

At AWS re:Invent 2021, we introduced three new serverless options for our data analytics services – Amazon EMR Serverless, Amazon Redshift Serverless, and Amazon MSK Serverless – that make it easier to analyze data at any scale without having to configure, scale, or manage the underlying infrastructure.

Today we announce the general availability of Amazon EMR Serverless, a serverless deployment option for customers to run big data analytics applications using open-source frameworks like Apache Spark and Hive without configuring, managing, and scaling clusters or servers.

With EMR Serverless, you can run analytics workloads at any scale with automatic scaling that resizes resources in seconds to meet changing data volumes and processing requirements. EMR Serverless automatically scales resources up and down to provide just the right amount of capacity for your application, and you only pay for what you use.

During the preview, we heard from customers that EMR Serverless is cost-effective because they do not incur cost from having to overprovision resources to deal with demand spikes. They do not have to worry about right-sizing instances or applying OS updates, and can focus on getting products to market faster.

Amazon EMR provides various deployment options to run applications to fit varied needs such as EMR clusters on Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Kubernetes Service (Amazon EKS) clusters, AWS Outposts, or EMR Serverless.

  • EMR on Amazon EC2 clusters is suitable for customers that need maximum control and flexibility over how to run their application. With EMR clusters, customers can choose the EC2 instance type to enhance the performance of certain applications, customize the Amazon Machine Image (AMI), choose EC2 instance configuration, customize, and extend open-source frameworks and install additional custom software on cluster instances.
  • EMR on Amazon EKS is suitable for customers that want to standardize on EKS to manage clusters across applications or use different versions of an open-source framework on the same cluster.
  • EMR on AWS Outposts is for customers who want to run EMR closer to their data center within an Outpost.
  • EMR Serverless is suitable for customers that want to avoid managing and operating clusters, and simply want to run applications using open-source frameworks.

Also, when you build an application using an EMR release (for example, a Spark job using EMR release 6.4), you can choose to run it on an EMR cluster, EMR on EKS, or EMR Serverless without having to rewrite the application. This allows you to build applications for a given framework version and retain the flexibility to change the deployment model based on future operational needs.

Getting Started with Amazon EMR Serverless
To get started with EMR Serverless, you can use Amazon EMR Studio, a free EMR feature which provides an end to end development and debugging experience. With EMR Studio, you can create EMR Serverless applications (Spark or Hive), choose the version of open-source software for your application, submit jobs, check the status of running jobs, and invoke Spark UI or Tez UI for job diagnostics.

When you select the Get started button in the EMR Serverless Console, you can create and set up EMR Studio with preconfigured EMR Serverless applications.

In EMR Studio, when you choose Applications in the Serverless menu, you can create one or more EMR Serverless applications and choose the open source framework and version for your use case. If you want separate logical environments for test and production or for different line-of-business use cases, you can create separate applications for each logical environment.

An EMR Serverless application is a combination of (a) the EMR release version for the open-source framework version you want to use and (b) the specific runtime that you want your application to use, such as Apache Spark or Apache Hive.

When you choose Create application, you can set your application NameType of either Spark or Hive, and supported Release version. You can also select the option of default or custom settings for pre-initialized capacity, application limits, and Amazon Virtual Private Cloud (Amazon VPC) connectivity options. Each EMR Serverless application is isolated from other applications and runs within a secure VPC.

Use the default option if you want jobs to start immediately. But charges apply for each worker when the application is started. To learn more about pre-initialized capacity, see Configuring and managing pre-initialized capacity.

When you select Start application, your application is setup to start with pre-initialized capacity of 1 Spark driver and 1 Spark executor. Your application is by default configured to start when jobs are submitted and stop when the application is idle for more than 15 minutes.

You can customize these settings and setup different application limits by selecting Choose custom settings.

In the Job runs menu, you can see a list of run jobs for your application.

Choose Submit job and set up job details such as the name, AWS Identity and Access Management (IAM) role used by the job, script location, and arguments of the JAR or Python script in the Amazon Simple Storage Service (Amazon S3) bucket that you want to run.

If you want logs for your Spark or Hive jobs to be submitted to your S3 bucket, you will need to setup the S3 bucket in the same Region where you are running EMR Serverless jobs.

Optionally, you can set additional configuration properties that you can specify for each job, such as Spark properties, job configurations to override the default configurations for applications (such as using the AWS Glue Data Catalog as its metastore), storing logs to Amazon S3, and retaining logs for 30 days.

The following is an example of running a Python script using the StartJobRun API.

$ aws emr-serverless start-job-run \
    --application-id <application_id> \
    --execution-role-arn <iam_role_arn> \
    --job-driver '{
        "sparkSubmit": {
            "entryPoint": "s3://spark-scripts/scripts/spark-etl.py",
            "entryPointArguments": "s3://spark-scripts/output",
            "sparkSubmitParameters": "--conf spark.executor.cores=1 --conf spark.executor.memory=4g --conf spark.driver.cores=1 --conf spark.driver.memory=4g --conf spark.executor.instances=1"
        }
    }' \
    --configuration-overrides '{
        "monitoringConfiguration": {
           "s3MonitoringConfiguration": {
             "logUri": "s3://spark-scripts/logs/"
           }
        }
    }'

You can check on job results in your S3 bucket. For details, you can use Spark UI for Spark Application, and Hive/Tez UI in the Job runs menu to understand how the job ran or to debug it if it failed.

For more debugging, EMR Serverless will push event logs to the sparklogs folder in your S3 log destination for Spark applications. In the case of Hive applications, EMR Serverless will continuously upload the Hive driver and Tez tasks logs to the HIVE_DRIVER or TEZ_TASK folders of your S3 log destination. To learn more, see Logging in the AWS documentation.

Things to Know
With EMR Serverless, you can get all the benefits of running Amazon EMR. I want to quote some things to know about EMR Serverless from an AWS Big Data Blog post of preview announcements:

  • Automatic and fine-grained scaling – EMR Serverless automatically scales up workers at each stage of processing your job and scales them down when they’re not required. You’re charged for aggregate vCPU, memory, and storage resources used from the time a worker starts running until it stops, rounded up to the nearest second with a 1-minute minimum. For example, your job may require 10 workers for the first 10 minutes of processing the job and 50 workers for the next 5 minutes. With fine-grained automatic scaling, you only incur cost for 10 workers for 10 minutes and 50 workers for 5 minutes. As a result, you don’t have to pay for underutilized resources.
  • Resilience to Availability Zone failures – EMR Serverless is a Regional service. When you submit jobs to an EMR Serverless application, it can run in any Availability Zone in the Region. In case an Availability Zone is impaired, a job submitted to your EMR Serverless application is automatically run in a different (healthy) Availability Zone. When using resources in a private VPC, EMR Serverless recommends that you specify the private VPC configuration for multiple Availability Zones so that EMR Serverless can automatically select a healthy Availability Zone.
  • Enable shared applications – When you submit jobs to an EMR Serverless application, you can specify the IAM role that must be used by the job to access AWS resources such as S3 objects. As a result, different IAM principals can run jobs on a single EMR Serverless application, and each job can only access the AWS resources that the IAM principal is allowed to access. This enables you to set up scenarios where a single application with a pre-initialized pool of workers is made available to multiple tenants wherein each tenant can submit jobs using a different IAM role but use the common pool of pre-initialized workers to immediately process requests.

Now Available
Amazon EMR Serverless is available in US East (N. Virginia), US West (Oregon), Europe (Ireland), and Asia Pacific (Tokyo) Regions. With EMR Serverless, there are no upfront costs, and you pay only for the resources you use. You pay for the amount of vCPU, memory, and storage resources consumed by your applications. For pricing details, see the EMR Serverless pricing page.

To learn more, visit the Amazon EMR Serverless User Guide. Please send feedback to AWS re:Post for Amazon EMR Serverless or through your usual AWS support contacts.

Learn all the details about Amazon EMR Serverless and get started today.

Channy

AWS Week In Review – May 30, 2022

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/aws-week-in-review-may-30-2022/

Today, the US observes Memorial Day. South Korea also has a national Memorial Day, celebrated next week on June 6. In both countries, the day is set aside to remember those who sacrificed in service to their country. This time provides an opportunity to recognize and show our appreciation for the armed services and the important role they play in protecting and preserving national security.

AWS also has supported our veterans, active-duty military personnel, and military spouses with our training and hiring programs in the US. We’ve developed a number of programs focused on engaging the military community, helping them develop valuable AWS technical skills, and aiding in transitioning them to begin their journey to the cloud. To learn more, see AWS’s military commitment.

Last Week’s Launches
The launches that caught my attention last week are the following:

Three New AWS Wavelength Zones in the US and South Korea  – We announced the availability of three new AWS Wavelength Zones on Verizon’s 5G Ultra Wideband network in Nashville, Tennessee, and Tampa, Florida in the US, and Seoul in South Korea on SK Telecom’s 5G network.

AWS Wavelength Zones embed AWS compute and storage services at the edge of communications service providers’ 5G networks while providing seamless access to cloud services running in an AWS Region. We have a total of 28 Wavelength Zones in Canada, Germany, Japan, South Korea, the UK, and the US globally. Learn more about AWS Wavelength and get started today.

New Amazon EC2 C7g, M6id, C6id, and P4de Instance Types – Last week, we announced four new EC2 instance types. C7g instances are the first instances powered by the latest AWS Graviton3 processors and deliver up to 25 percent better performance over Graviton2-based C6g instances for a broad spectrum of applications, even high-performance computing (HPC) and CPU-based machine learning (ML) inference.

M6id and C6id instances are powered by the Intel Xeon Scalable processors (Ice Lake) with an all-core turbo frequency of 3.5 GHz, equipped with up to 7.6 TB of local NVMe-based SSD block-level storage, and deliver up to 15 percent better price performance compared to the previous generation instances.

P4de instances are a preview of our latest GPU-based instances that provide the highest performance for ML training and HPC applications. It is powered by 8 NVIDIA A100 GPUs with 80 GB high-performance HBM2e GPU memory, 2X higher than the GPUs in our current P4d instances. The new P4de instances provide a total of 640GB of GPU memory, providing up to 60 percent better ML training performance along with 20 percent lower cost to train when compared to P4d instances.

Amazon EC2 Stop Protection Feature to Protect Instances From Unintentional Stop Actions – Now you don’t have to worry about stopping or terminating your instances from accidental actions. With Stop Protection, you can safeguard data in instance store volume(s) from unintentional stop actions. Previously, you could protect your instances from unintentional termination actions by enabling Termination Protection too.

When enabled, the Stop or Termination Protection feature blocks attempts to stop or terminate the instance via the EC2 console, API call, or CLI command. This feature provides an extra measure of protection for stateful workloads since instances can be stopped or terminated only by deactivating the Stop Protection feature.

AWS DataSync Supports Google Cloud Storage and Azure Files Storage Locations – We announced the general availability of two additional storage locations for AWS DataSync, an online data movement service that makes it easy to sync your data both into and out of the AWS Cloud. With this release, DataSync now supports Google Cloud Storage and Azure Files storage locations in addition to Network File System (NFS) shares, Server Message Block (SMB) shares, Hadoop Distributed File Systems (HDFS), self-managed object storage, AWS Snowcone, Amazon Simple Storage Service (Amazon S3), Amazon Elastic File System (Amazon EFS), Amazon FSx for Windows File Server, Amazon FSx for Lustre, and Amazon FSx for OpenZFS.

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

Other AWS News
Last week, there were lots of announcements of public sectors at AWS Summit Washington, DC.

To learn more, watch the keynote of Max Peterson, Vice President of AWS Worldwide Public Sector.

Upcoming AWS Events
If you have a developer background or similar and are looking to develop ML skills you can use to solve real-world problems, Let’s Ship It – with AWS! ML Edition is the perfect place to start. Over eight episodes of Twitch training scheduled from June 2 to July 21, you can learn hands-on how to build ML models, such as predicting demand and personalizing your offerings, and more.

The AWS Summit season is mostly over in Asia Pacific and Europe, but there are some upcoming virtual and in-person Summits that might be close to you in June:

More to come in August and September.

Please join Amazon re:MARS 2022 (June 21 – 24) to hear from recognized thought leaders and technical experts who are building the future of machine learning, automation, robotics, and space. You can preview Robotics at Amazon to discuss the recent real-world challenges of building robotic systems, published by Amazon Science.

You can now register for AWS re:Inforce 2022 (July 26 – 27). Join us in Boston to learn how AWS is innovating in the world of cloud security, and hone your technical skills in expert-led interactive sessions.

You can now register for AWS re:Invent 2022 (November 28 – December 2). Join us in Las Vegas to experience our most vibrant event that brings together the global cloud community. You can virtually attend live keynotes and leadership sessions and access our on-demand breakout sessions even after re:Invent closes.

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!

New – Amazon EC2 M6id and C6id Instances with Up to 7.6 TB Local NVMe Storage

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/new-amazon-ec2-m6id-and-c6id-instances-with-up-to-7-6-tb-local-nvme-storage/

Last year, we launched the Amazon EC2 M6i instances and C6i instances, our sixth-generation offerings that include 3rd generation Intel Xeon Scalable processors.

Today we are expanding Amazon EC2 M6id and C6id instances, backed by NVMe-based SSD block-level instance storage physically connected to the host server. These instances are powered by the Intel Xeon Scalable processors (Ice Lake) with an all-core turbo frequency of 3.5 GHz, equipped with up to 7.6 TB of local NVMe-based SSD block-level storage, and deliver up to 15 percent better price performance compared to previous generation instances.

M6id instances are ideal for workloads that require a balance of compute and memory resources along with high-speed, low-latency local block storage, including data logging and media processing. C6id is ideal for compute-intensive workloads, including those that need access to high-speed, low-latency local storage like video encoding, image manipulation, and other forms of media processing. Both M6id and C6id will also benefit applications that need temporary storage of data, such as batch and log processing and applications that need caches and scratch files.

Compared to previous generation instances, new instance types provide:

  • Up to 58 percent higher storage per vCPU and 34 percent lower cost per TB compared to M5d instances, and up to 138 percent higher storage per vCPU and 56 percent lower cost per TB compared with C5d instances.
  • Larger instance sizes (32xlarge) with up to 128 vCPUs and 512 GiB (M6id) or 256 GiB (C6id) of memory that make it easier and more cost-efficient to consolidate workloads and scale up applications.
  • Up to 15 percent improvement in compute price performance and 20 percent higher memory bandwidth.
  • 2 times increased bandwidth up to 40 Gbps for Amazon EBS and 50 Gbps for networking.

Here are the specs of M6id instances in detail:

Instance Name vCPUs RAM (GiB) Local NVMe SSD Storage (GB) EBS Throughput (Gbps) Network Bandwidth (Gbps)
m6id.large 2 8 1 x 118 Up to 10 Up to 12.5
m6id.xlarge 4 16 1 x 237 Up to 10 Up to 12.5
m6id.2xlarge 8 32 1 x 474 Up to 10 Up to 12.5
m6id.4xlarge 16 64 1 x 950 Up to 10 Up to 12.5
m6id.8xlarge 32 128 1 x 1900 10 12.5
m6id.12xlarge 48 192 2 x 1425 15 18.75
m6id.16xlarge 64 156 2 x 1900 20 25
m6id.24xlarge 96 384 4 x 1425 30 37.5
m6id.32xlarge 128 512 4 x 1900 40 50
m6id.metal 128 512 4 x 1900 40 50

Here are also the specs of C6id instances in detail:

Instance Name vCPUs RAM (GiB) Local NVMe SSD Storage (GB) EBS Throughput (Gbps) Network Bandwidth (Gbps)
c6id.large 2 4 1 x 118 Up to 10 Up to 12.5
c6id.xlarge 4 8 1 x 237 Up to 10 Up to 12.5
c6id.2xlarge 8 16 1 x 474 Up to 10 Up to 12.5
c6id.4xlarge 16 32 1 x 950 Up to 10 Up to 12.5
c6id.8xlarge 32 64 1 x 1900 10 12.5
c6id.12xlarge 48 96 2 x 1425 15 18.75
c6id.16xlarge 64 128 2 x 1900 20 25
c6id.24xlarge 96 192 4 x 1425 30 37.5
c6id.32xlarge 128 256 4 x 1900 40 50
c6id.metal 128 256 4 x 1900 40 50

You can use any Amazon Machine Images (AMIs) that include drivers for the Elastic Network Adapter (ENA) and NVMe. For optimal networking performance on these new instances, ENA driver update may be required. For more information on optimal ENA driver for M6id and C6id instances, see this article on migrating instances.

Here are a couple of things to remind you about the local NVMe storage on these instances:

  • You don’t have to specify a block device mapping in your AMI or during the instance launch; the local storage will show up as one or more devices (/dev/nvme*1 on Linux) after the guest operating system has booted.
  • Each local NVMe device is hardware encrypted using the XTS-AES-256 block cipher and a unique key. Each key is destroyed when the instance is stopped or terminated.
  • Local NVMe devices have the same lifetime as the instance they are attached to and do not stick around after the instance has been stopped or terminated.

Now Available
You can launch M6id and C6id instances today in the AWS US East (Ohio), US East (N. Virginia), US West (Oregon), and Europe (Ireland) Regions as On-Demand, Spot, and Reserved Instances or as part of a Savings Plan. As usual with EC2, you pay for what you use. For more information, see the EC2 pricing page.

To learn more, visit our Amazon EC2 M6i instances or C6i instances page, and please send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

– Channy

New for AWS DataSync – Move Data Between AWS and Google Cloud Storage or AWS and Microsoft Azure Files

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-aws-datasync-move-data-between-aws-and-google-cloud-storage-or-aws-and-microsoft-azure-files/

Moving data to and from AWS Storage services can be automated and accelerated with AWS DataSync. For example, you can use DataSync to migrate data to AWS, replicate data for business continuity, and move data for analysis and processing in the cloud. You can use DataSync to transfer data to and from AWS Storage services, including Amazon Simple Storage Service (Amazon S3), Amazon Elastic File System (Amazon EFS), and Amazon FSx. DataSync also integrates with Amazon CloudWatch and AWS CloudTrail for logging, monitoring, and alerting.

Today, we added to DataSync the capability to migrate data between AWS Storage services and either Google Cloud Storage or Microsoft Azure Files. In this way, you can simplify your data processing or storage consolidation tasks. This also helps if you need to import, share, and exchange data with customers, vendors, or partners who use Google Cloud Storage or Microsoft Azure Files. DataSync provides end-to-end security, including encryption and integrity validation, to ensure your data arrives securely, intact, and ready to use.

Let’s see how this works in practice.

Preparing the DataSync Agent
First, I need a DataSync agent to read from, or write to, storage located in Google Cloud Storage or Azure Files. I deploy the agent on an Amazon Elastic Compute Cloud (Amazon EC2) instance. The latest DataSync Amazon Machine Image (AMI) ID is stored in the Parameter Store, a capability of AWS Systems Manager. I use the AWS Command Line Interface (CLI) to get the value of the /aws/service/datasync/ami parameter:

aws ssm get-parameter --name /aws/service/datasync/ami --region us-east-1
{
    "Parameter": {
        "Name": "/aws/service/datasync/ami",
        "Type": "String",
        "Value": "ami-0e244fe801cf5a510",
        "Version": 54,
        "LastModifiedDate": "2022-05-11T14:08:09.319000+01:00",
        "ARN": "arn:aws:ssm:us-east-1::parameter/aws/service/datasync/ami",
        "DataType": "text"
    }
}

Using the EC2 console, I start an EC2 instance using the AMI ID specified in the Value property of the parameter. For networking, I use a public subnet and the option to auto-assign a public IP address. The EC2 instance needs network access to both the source and the destination of a data moving task. Another requirement for the instance is to be able to receive HTTP traffic from DataSync to activate the agent.

When using AWS DataSync in a virtual private cloud (VPC) based on the Amazon VPC service, it is a best practice to use VPC endpoints to connect the agent with the DataSync service. In the VPC console, I choose Endpoints on the navigation pane and then Create endpoint. I enter a name for the endpoint and select the AWS services category.

Console screenshot.

In the Services section, I look for DataSync.

Console screenshot.

Then, I select the same VPC where I started the EC2 instance.

Console screenshot.

To reduce cross-AZ traffic, I choose the same subnet used for the EC2 instance.

Console screenshot.

The DataSync agent running on the EC2 instance needs network access to the VPC endpoint. For simplicity, I use the default security group of the VPC for both. I create the VPC endpoint and, after a few minutes, it’s ready to be used.

Console screenshot.

In the AWS DataSync console, I choose Agents from the navigation pane and then Create agent. I select Amazon EC2 for the Hypervisor.

Console screenshot.

I choose VPC endpoints using AWS PrivateLink for the Endpoint type. I select the VPC endpoint I created before and the same Subnet and Security group I used for the VPC endpoint.

I choose the option to Automatically get the activation key and type the public IP of the EC2 instance. Then, I choose Get key.

Console screenshot.

After the DataSync agent has been activated, I don’t need HTTP access anymore, and I remove that from the security groups of the EC2 instance. Now that the DataSync agent is active, I can configure tasks and locations to move my data.

Moving Data from Google Cloud Storage to Amazon S3
I have a few images in a Google Cloud Storage bucket, and I want to synchronize those files with an S3 bucket. In the Google Cloud console, I open the settings of the bucket. There, I create a service account with Storage Object Viewer permissions and write down the credentials (access key and secret) to access the bucket programmatically.

Back in the AWS DataSync console, I choose Tasks and then Create task.

To configure the source of the task, I create a location. I select Object storage for the Location type and choose the agent I just created. For the Server, I use storage.googleapis.com. Then, I enter the name of the Google Cloud bucket and the folder where my images are stored.

Console screenshot.

For authentication, I enter the access key and the secret I retrieved when I created the service account. I choose Next.

Console screenshot.

To configure the destination of the task, I create another location. This time, I select Amazon S3 for the Location Type. I choose the destination S3 bucket and enter a folder that will be used as a prefix for the files transferred to the bucket. I use the Autogenerate button to create the IAM role that will give DataSync permissions to access the S3 bucket.

Console screenshot.

In the next step, I configure the task settings. I enter a name for the task. Optionally, I can fine-tune how DataSync verifies the integrity of the transferred data or allocate a bandwidth for the task.

Console screenshot.

I can also choose what data to scan and what to transfer. By default, all source data is scanned, and only data that has changed is transferred. In the Additional settings, I disable Copy object tags because tags are currently not supported with Google Cloud Storage.

Console screenshot.

I can select the schedule used to run this task. For now, I leave it Not scheduled, and I will start it manually.

Console screenshot.

For logging, I use the Autogenerate button to create a log group for DataSync. I choose Next.

Console screenshot.

I review the configurations and create the task. Now, I start the data moving task from the console. After a few minutes, the files are synced with my S3 bucket and I can access them from the S3 console.

Console screenshot.

Moving Data from Azure Files to Amazon FSx for Windows File Server
I take a lot of pictures, and I also have a few images in an Azure file share. I want to synchronize those files with an Amazon FSx for Windows file system. In the Azure console, I select the file share and choose the Connect button to generate a PowerShell script that checks if this storage account is accessible over the network.

$connectTestResult = Test-NetConnection -ComputerName <SMB_SERVER> -Port 445
if ($connectTestResult.TcpTestSucceeded) {
    # Save the password so the drive will persist on reboot
    cmd.exe /C "cmdkey /add:`"danilopsync.file.core.windows.net`" /user:`"localhost\<USER>`" /pass:`"<PASSWORD>`""
    # Mount the drive
    New-PSDrive -Name Z -PSProvider FileSystem -Root "\\danilopsync.file.core.windows.net\<SHARE_NAME>" -Persist
} else {
    Write-Error -Message "Unable to reach the Azure storage account via port 445. Check to make sure your organization or ISP is not blocking port 445, or use Azure P2S VPN, Azure S2S VPN, or Express Route to tunnel SMB traffic over a different port."
}

From this script, I grab the information I need to configure the DataSync location:

  • SMB Server
  • Share Name
  • User
  • Password

Back in the AWS DataSync console, I choose Tasks and then Create task.

To configure the source of the task, I create a location. I select Server Message Block (SMB) for the Location Type and the agent I created before. Then, I use the information I found in the script to enter the SMB Server address, the Share name, and the User/Password to use for authentication.

Console screenshot.

To configure the destination of the task, I again create a location. This time, I choose Amazon FSx for the Location type. I select an FSx for Windows file system that I created before and use the default share name. I use the default security group to connect to the file system. Because I am using AWS Directory Service for Microsoft Active Directory with FSx for Windows File Server, I use the credentials of a user member of the AWS Delegated FSx Administrators and Domain Admins groups. For more information, see Creating a location for FSx for Windows File Server in the documentation.

Console screenshot.

In the next step, I enter a name for the task and leave all other options to their default values in the same way I did for the previous task.

Console screenshot.

I review the configurations and create the task. Now, I start the data moving task from the console. After a few minutes, the files are synched with my FSx for Windows file system share. I mount the file system share with a Windows EC2 instance and see that my images are there.

EC2 screenshot.

When creating a task, I can reuse existing locations. For example, if I want to synchronize files from Azure Files to my S3 bucket, I can quickly select the two corresponding locations I created for this post.

Availability and Pricing
You can move your data using the AWS DataSync console, AWS Command Line Interface (CLI), or AWS SDKs to create tasks that move data between AWS storage and Google Cloud Storage buckets or Azure Files file systems. As your tasks run, you can monitor progress from the DataSync console or by using CloudWatch.

There are no changes to DataSync pricing with these new capabilities. Moving data to and from Google Cloud or Microsoft Azure is charged at the same rate as all other data sources supported by DataSync today.

You may be subject to data transfer out fees by Google Cloud or Microsoft Azure. Because DataSync compresses data in flight when copying between the agent and AWS, you may be able to reduce egress fees by deploying the DataSync agent in a Google Cloud or Microsoft Azure environment.

When using DataSync to move data from AWS to Google Cloud or Microsoft Azure, you are charged for data transfer out from EC2 to the internet. See Amazon EC2 pricing for more information.

Automate and accelerate the way you move data with AWS DataSync.

Danilo

New – Amazon EC2 C7g Instances, Powered by AWS Graviton3 Processors

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/new-amazon-ec2-c7g-instances-powered-by-aws-graviton3-processors/

I am excited to announce that Amazon Elastic Compute Cloud (Amazon EC2) C7g instances powered by the latest AWS Graviton3 processors that have been available in preview since re:Invent last year are now available for all.

Let’s decompose the name C7g: the “C” instance family is designed for compute-intensive workloads. This is the 7th generation of this instance family. And the “g” means it is based on AWS Graviton, the silicon designed by AWS. These instances are the first instances to be powered by the latest generation of AWS Graviton, the Graviton3 processors.

As you bring more diverse workloads to the cloud, and as your compute, storage, and networking demands increase at a rapid pace, you are asking us to push the price performance boundary even further so that you can accelerate your migration to the cloud and optimize your costs. Additionally, you are looking for more energy-efficient compute options to help you reduce your carbon footprint and achieve your sustainability goals. We do this by working back from your requests, and innovating at a rapid pace across all levels of the AWS infrastructure. Our Graviton chips offer better performance at lower cost along with enhanced capabilities. For example, AWS Graviton3 processors offer you enhanced security with always-on memory encryption, dedicated caches for every vCPU, and support for pointer authentication.

Let’s illustrate this with numbers. When we launched Graviton2-based instances, they provided up to 40 percent better price/performance for a wide variety of workloads over comparable fifth-generation x86-based instances. We now have 12 instance families (M6g, M6gd, C6g, C6gd, C6gn, R6g, R6gd, T4g, X2gd, Im4gn, Is4gen, and G5g) that are powered by AWS Graviton2 processors that provide significant price performance benefits for a wide range of workloads. In 2021, we saw tens of thousands of AWS customers take advantage of this innovation by using Graviton2-based EC2 instances.

Our next generation, Graviton3 processors, deliver up to 25 percent higher performance, up to 2x higher floating-point performance, and 50 percent faster memory access based on leading-edge DDR5 memory technology compared with Graviton2 processors.

Graviton3 also uses up to 60 percent less energy for the same performance as comparable EC2 instances, which helps you reduce your carbon footprint.

Snap Inc, known for its popular social media services such as Snapchat and Bitmoji, adopted AWS Graviton2-based instances to optimize their price performance on Amazon EC2. Aaron Sheldon, software engineer at Snap, told us: “We trialed the new AWS Graviton3-based Amazon EC2 C7g instances and found that they provide significant performance improvements on real workloads compared to previous generation C6g instances. We are excited to migrate our Graviton2-based workloads to Graviton3, including messaging, storage, and friend graph workloads.”

The C7g instances are available in eight sizes with 1, 2, 4, 8, 16, 32, 48, and 64 vCPUs. C7g instances support configurations up to 128 GiB of memory, 30 Gbps of network performance, and 20 Gbps of Amazon Elastic Block Store (EBS) performance. These instances are powered by the AWS Nitro System, a combination of dedicated hardware and a lightweight hypervisor.

The following table summarizes the key characteristics of each instance type in this family.

Instance Name vCPUs
Memory
Network Bandwidth
EBS Bandwidth
c7g.medium 1 2 GiB up to 12.5 Gbps up to 10 Gbps
c7g.large 2 4 GiB up to 12.5 Gbps up to 10 Gbps
c7g.xlarge 4 8 GiB up to 12.5 Gbps up to 10 Gbps
c7g.2xlarge 8 16 GiB up to 15 Gbps up to 10 Gbps
c7g.4xlarge 16 32 GiB up to 15 Gbps up to 10 Gbps
c7g.8xlarge 32 64 GiB 15 Gbps 10 Gbps
c7g.12xlarge 48 96 GiB 22.5 Gbps 15 Gbps
c7g.16xlarge 64 128 GiB 30 Gbps 20 Gbps

C7g instances are initially available in US East (N. Virginia) and US West (Oregon) AWS Regions; other Regions will be added shortly after launch.

As usual, you can purchase C7g capacity on demand, as Reserved Instances, or as Spot instances, and use your Saving Plans. The pricing details are available on the EC2 pricing page.

I have the chance to talk with AWS customers on a daily basis, and many of my discussions are around price performance and the sustainability of their workloads. With more than 500 instance types to choose from, one question I often receive is: what are the workloads that would benefit from C7g?

You will find that C7g instances provide the best price performance within their instance families for a broad spectrum of compute-intensive workloads, including application servers, micro services, high-performance computing, electronic design automation, gaming, media encoding, or CPU-based ML inference. These instances are ideal for all Linux-based workloads, including containerized and micro service-based applications built using Amazon Elastic Kubernetes Service (EKS), Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Container Registry, Kubernetes, and Docker, and written in popular programming languages such as C/C++, Rust, Go, Java, Python, .NET Core, Node.js, Ruby, and PHP.

The next question I receive is: given that Graviton instances are based on Arm architecture, how difficult is it to migrate from x86?

Graviton3 instances are supported by a broad choice of operating systems, independent software vendors, container services, agents, and developer tools, enabling you to migrate your workloads with minimal effort.

Applications and scripts written in high-level programming languages such as Python, Node.js, Ruby, Java, or PHP will typically just require a redeployment. Applications written in lower-level programming languages such as C/C++, Rust, or Go will require a re-compilation.

But you don’t always need to migrate your applications. Several managed services are based on Graviton already, such as Amazon ElastiCache, Amazon EKS, Amazon ECS, Amazon Relational Database Service (RDS), Amazon EMR, Amazon Aurora, and Amazon OpenSearch Service, and your application can benefit from Graviton with minimal efforts. A French customer told me recently they migrated a significant portion of their Amazon EMR clusters to Graviton by doing just one line change in their Terraform scripts; all the rest worked as-is.

For those of you building with serverless, we have also released Graviton support for AWS Fargate and AWS Lambda, extending the price, efficiency, and performance benefits of Graviton to serverless workloads. Lambda functions using Graviton2 can see up to 34 percent better price/performance.

Reducing the carbon footprint of your organization is also of paramount importance. Reducing the carbon footprint of cloud-based workloads is a shared responsibility between you and us. We do our part by innovating at all levels: from the materials used to build our facilities, the usage of water for cooling, and the production of renewable energy, down to inventing new silicons that are more energy efficient. To help you meet your own sustainability goals, we added a sustainability pillar to the AWS Well-Architected framework, and we released the Customer Carbon Footprint tool. Graviton3 fits into that context. It uses up to 60 percent less energy for the same performance as comparable EC2 instances.

We do our part in this shared responsibility model, and now, it is your turn. You can use our innovations and tools to help you optimize your workloads and only use the resources you need. Take the occasion to write clever code that uses fewer CPU cycles, less storage, or less network bandwidth. And be sure to select energy-efficient options, such as Graviton3-based instance types or managed services, when deploying your code.

To help you to get started migrating your applications to Graviton instance types today, we curated this list of technical resources. Have a look at it. To learn more about Graviton-based instances, visit the Graviton page or the C7g page and check out this video:

If you’d like to get started with Graviton-based instances for free, we also just reintroduced the free trial on T4g.small instances for up to 750 hours/month until the end of this year (December 31, 2022).

And now, go build 😉

— seb

AWS Week In Review – May 23, 2022

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/aws-week-in-review-may-27-2022/

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!

This is the right place to quickly learn about recent AWS news from last week, in just about five minutes or less. This week, I have collected a couple of news items that might be of interest to you, the IT professionals, developers, system administrators, or any type of builders that have their hands on the AWS console, the CLI, or that are writing code.

Last Week’s Launches
The launches that caught my attention last week are the following:

EC2 now supports NitroTPM and SecureBoot – A Trusted Platform Module is often a discrete chip in a computer where you can store secrets and release them to the operating system only when the system is in a known good state. You typically use TPM modules to store operating-system-level volume encryption keys, such as the ones used by BitLocker on Windows or LUKS. NitroTPM is a virtual TPM module available on selected instance families that allows you to deploy your workloads depending on TPM functionalities on EC2 instances.

Amazon EC2 Auto Scaling now backfills predictive scaling forecasts so you can quickly validate forecast accuracy. Auto Scaling Predictive Scaling is a capability of Auto Scaling that allows you to scale your fleet in and out based on observed usage patterns. It uses AI/ML to predict when your fleet needs more or less capacity. It allows you to scale a fleet in advance of the scaling event and have the fleet prepared at peak times. The new backfills shows you how predictive scaling would have scaled your fleet during the last 14 days. This allows you to quickly decide if the predictive scaling policy is accurate for your applications by comparing the demand and capacity forecasts against actual demand immediately after you create a predictive scaling policy.

AWS Backup adds support for two new managed file systems, Amazon FSx for OpenZFS and Amazon Fsx for NetApp ONTAP. These additions helps you meet your centralized data protection and regulatory compliance needs. You can now use AWS Backup’s policy-based capabilities to centrally protect Amazon FSx for NetApp ONTAP or Amazon Fsx for OpenZFS, along with the other AWS services for storage, database, and compute that AWS Backup supports.

AWS App Mesh now supports IPv6 AWS App Mesh is a service mesh that provides application-level networking to make it easy for your services to communicate with each other across multiple types of compute infrastructure. The new support for IPv6 allows you to support workloads running in IPv6 networks and to invoke App Mesh APIs over IPv6. This helps you meet IPv6 compliance requirements, and removes the need for complex networking configuration to handle address translation between IPv4 and IPv6.

Amazon Chime SDK now supports video background replacement and blur on iOS and Android. When you want to integrate audio and video call capabilities in your mobile applications, the Chime SDK is the easiest way to get started. It provides an easy-to-use API that uses the scalable and robust Amazon Chime backend to power your communications. For example, Slack is using Chime as backend for the communications in their apps. The Chime SDK client libraries for iOS and Android now include video background replacement and blur, which developers can use to reduce visual distractions and help increase visual privacy for mobile users on iOS and Android.

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

Other AWS News
Some other updates and news that you may have missed:

Amazon Redshift: Ten years of continuous reinvention. This is an Amazon Redshift research paper that will be presented at a leading international forum for database researchers. The authors reflect on how far the first petabyte-scale cloud data warehouse has advanced since it was announced ten years ago.

Improve Your Security at the Edge with AWS IoT Services is a new blog post on the IoT channel. We understand the risks associated with operating at the edge and that you need additional capabilities to ensure that your data is protected. AWS IoT services can help you with end-to-end data protection, device security, and device identification to create the foundation of an expanded information security model and confidently operate at the edge.

AWS Open Source News and Updates – Ricardo Sueiras, my colleague from the AWS Developer Relation team, runs this newsletter. It brings you all the latest open-source projects, posts, and more. Read edition #113 here.

Upcoming AWS Events
CDK Day, on May 26 is a one-day fully virtual event dedicated to the AWS Cloud Development Kit. With four versions of the CDK released (AWS, Terraform, CDK8s, and Projen), we tought the CDK deserves its own full-fledged conference. We will take one day and showcase the brightest and best of CDK from across the whole product family. Let’s talk serverless, Kubernetes and multi-cloud all on the same day! CDK Day will take place on May 26, 2022 and will be fully virtual, live-streamed to our YouTube channel. Book your ticket now, it’s free.

The AWS Summit season is mostly over in Europe, but there are upcoming Summits in North America and the Asia Pacific Regions. Here are some virtual and in-person Summits that might be close to you:

More to come in July, August, and September.

You can register for re:MARS to get fresh ideas on topics such as machine learning, automation, robotics, and space. The conference will be in person in Las Vegas, June 21–24.

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

— seb

AWS Backup Now Supports Amazon FSx for NetApp ONTAP

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-backup-now-supports-amazon-fsx-for-netapp-ontap/

If you are a long-time reader of this blog, you know that I categorize some posts as “chocolate and peanut butter” in homage to an ancient (1970 or so) series of TV commercials for Reese’s Peanut Butter Cups. Today, I am happy to bring you the latest such post, combining AWS Backup and Amazon FSx for NetApp ONTAP. Before I dive into the specifics, let’s review each service:

AWS Backup helps you to automate and centrally manage your backups (read my post, AWS Backup – Automate and Centrally Manage Your Backups, for a detailed look). After you create policy-driven plans, you can monitor the status of on-going backups, verify compliance, and find/restore backups, all from a central console. We launched in 2019 with support for Amazon EBS volumes, Amazon EFS file systems, Amazon RDS databases, Amazon DynamoDB tables, and AWS Storage Gateway volumes. After that, we added support for EC2 instances, Amazon Aurora clusters, Amazon FSx for Lustre and Amazon FSx for Window File Server file systems, Amazon Neptune databases, VMware workloads, Amazon DocumentDB clusters, and Amazon S3.

Amazon FSx for NetApp ONTAP gives you the features, performance, and APIs of NetApp ONTAP file systems with the agility, scalability, security, and resiliency of AWS (again, read my post, New – Amazon FSx for NetApp ONTAP to learn more). ONTAP is an enterprise data management product that is designed to provide high-performance storage suitable for use with Oracle, SAP, VMware, Microsoft SQL Server, and so forth. Each file system supports multi-protocol access and can scale up to 176 PiB, along with inline data compression, deduplication, compaction, thin provisioning, replication, and point-in-time cloning. We launched with a multi-AZ deployment type, and introduced a single-AZ deployment type earlier this year.

Chocolate and Peanut Butter
AWS Backup now supports Amazon FSx for NetApp ONTAP file systems. All of the existing AWS Backup features apply, and you can add this support to an existing backup plan or you can create a new one.

Suppose I have a couple of ONTAP file systems:

I go to the AWS Backup Console and click Create Backup plan to get started:

I decide to Start with a template, and choose Daily-Monthly-1yr-Retention, then click Create plan:

Next, I examine the Resource assignments section of my plan and click Assign resources:

I create a resource assignment (Jeff-ONTAP-Resources), and select the FSx resource type. I can leave the assignment as-is in order to include all of my Amazon FSx volumes in the assignment, or I can uncheck All file systems, and then choose volumes on the file systems that I showed you earlier:

I review all of my choices, and click Assign resources to proceed. My backups will be performed in accord with the backup plan.

I can also create an on-demand backup. To do this, I visit the Protected resources page and click Create on-demand backup:

I choose a volume, set a one week retention period for my on-demand backup, and click Create on-demand backup:

The backup job starts within seconds, and is visible on the Backup jobs page:

After the job completes I can examine the vault and see my backup. Then I can select it and choose Restore from the Actions menu:

To restore the backup, I choose one of the file systems from it, enter a new volume name, and click Restore backup.

Also of Interest
We recently launched two new features for AWS Backup that you may find helpful. Both features can now be used in conjunction with Amazon FSx for ONTAP:

AWS Backup Audit Manager – You can use this feature to monitor and evaluate the compliance status of your backups. This can help you to meet business and regulatory requirements, and lets you generate reports that you can use to demonstrate compliance to auditors and regulators. To learn more, read Monitor, Evaluate, and Demonstrate Backup Compliance with AWS Backup Audit Manager.

AWS Backup Vault Lock – This feature lets you prevent your backups from being accidentally or maliciously deleted, and also enhances protection against ransomware. You can use this feature to make selected backup values WORM (write-once-read-many) compliant. Once you have done this, the backups in the vault cannot be modified manually. You can also set minimum and maximum retention periods for each vault. To learn more, read Enhance the security posture of your backups with AWS Backup Vault Lock.

Available Now
This new feature is available now and you can start using it today in all regions where AWS Backup and Amazon FSx for NetApp ONTAP are available.

Jeff;