New – Create Amazon RDS DB Instances on AWS Outposts

Late last year I told you about AWS Outposts and invited you to Order Yours Today. As I told you at the time, this is a comprehensive, single-vendor compute and storage offering that is designed to meet the needs of customers who need local processing and very low latency in their data centers and on factory floors. Outposts uses the hardware that we use in AWS public regions
I first told you about Amazon RDS back in 2009. This fully managed service makes it easy for you to launch, operate, and scale a relational database. Over the years we have added support for multiple open source and commercial databases, along with tons of features, all driven by customer requests.
DB Instances on AWS Outposts Today I am happy to announce that you can now create RDS DB Instances on AWS Outposts. We are launching with support for MySQL and PostgreSQL,


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Find Your Most Expensive Lines of Code – Amazon CodeGuru Is Now Generally Available

Bringing new applications into production, maintaining their code base as they grow and evolve, and at the same time respond to operational issues, is a challenging task. For this reason, you can find many ideas on how to structure your teams, on which methodologies to apply, and how to safely automate your software delivery pipeline.
At re:Invent last year, we introduced in preview Amazon CodeGuru, a developer tool powered by machine learning that helps you improve your applications and troubleshoot issues with automated code reviews and performance recommendations based on runtime data. During the last few months, many improvements have been launched, including a more cost-effective pricing model, support for Bitbucket repositories, and the ability to start the profiling agent using a command line switch, so that you no longer need to modify the code of your application, or add dependencies, to run the agent.

You can use CodeGuru in two ways:
CodeGuru Reviewer uses


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New – Building a Continuous Integration Workflow with Step Functions and AWS CodeBuild

Automating your software build is an important step to adopt DevOps best practices. To help you with that, we built AWS CodeBuild, a fully managed continuous integration service that compiles source code, runs tests, and produces packages that are ready for deployment.
However, there are so many possible customizations in our customers’ build processes, and we have seen developers spend time in creating their own custom workflows to coordinate the different activities required by their software build. For example, you may want to run, or not, some tests, or skip static analysis of your code when you need to deploy a quick fix. Depending on the results of your unit tests, you may want to take different actions, or be notified via SNS.
To simplify that, we are launching today a new AWS Step Functions service integration with CodeBuild. Now, during the execution of a state machine, you can start or stop a build, get build report summaries,


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New – Amazon Simple Email Service (SES) for VPC Endpoints

Although chat and messaging applications have been popular, the email has retained its place as a ubiquitous channel with the highest Return on Investment (ROI) because of its low barrier to entry, affordability and ability to target specific recipients. To ensure that organization’s marketing and transactional messages are received by the end customer in a timely manner and to drive deeper engagement with them, you need to partner with a mature and trusted email service provider that has built specialized expertise in delivering email at scale.
Amazon Simple Email Services(SES) has been the trustworthy, flexible and affordable email service provider for developers and digital marketers since 2011. Amazon SES is a reliable, cost-effective service for businesses of all sizes that use email to keep in contact with their customers. Many businesses operate in industries that are highly secure and have strict security policies. So we have enhanced security and compliance


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AWS Chatbot – ChatOps for Slack and Chime

Last year, my colleague Ilya Bezdelev wrote Introducing AWS Chatbot: ChatOps for AWS to launch the public beta of AWS Chatbot. He also participated in the re:Invent 2019 Launchpad and did an in-depth AWS Chatbot demo:

In his initial post, Ilya showed you how you can practice ChatOps within Amazon Chime or Slack, receiving AWS notifications and executing commands in an environment that is intrinsically collaborative. In a later post, Running AWS commands from Slack using AWS Chatbot, Ilya showed how to configure AWS Chatbot in a Slack channel, display CloudWatch alarms, describe AWS resources, invoke a Lambda function and retrieve the logs, and create an AWS Support case. My colleagues Erin Carlson and Matt Cowsert wrote about AWS Budgets Integration with Chatbot and walked through the process of setting up AWS Budget alerts and arranging for notifications from within AWS Chatbot. Finally, Anushri Anwekar showed how to Receive


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New – T3 Instances on Dedicated Single-Tenant Hardware

T3 instances use a burst pricing model that allows you to host general purpose workloads at low cost, with access to sustainable, full-core performance when needed. You can choose from seven different sizes and receive an assured baseline amount of processing power, courtesy of custom high frequency Intel® Xeon® Scalable Processors.
Our customers use them to host many different types of production and development workloads including microservices, small and medium databases, and virtual desktops. Some of our customers launch large fleets of T3 instances and use them to test applications in a wide range of conditions, environments, and configurations.
We launched the first EC2 Dedicated Instances way back in 2011. Dedicated Instances run on single-tenant hardware, providing physical isolation from instances that belong to other AWS accounts. Our customers use Dedicated Instances to further their compliance goals (PCI, SOX, FISMA, and so forth), and also use them to run software


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New for Amazon EFS – IAM Authorization and Access Points

When building or migrating applications, we often need to share data across multiple compute nodes. Many applications use file APIs and Amazon Elastic File System (EFS) makes it easy to use those applications on AWS, providing a scalable, fully managed Network File System (NFS) that you can access from other AWS services and on-premises resources.
EFS scales on demand from zero to petabytes with no disruptions, growing and shrinking automatically as you add and remove files, eliminating the need to provision and manage capacity. By using it, you get strong file system consistency across 3 Availability Zones. EFS performance scales with the amount of data stored, with the option to provision the throughput you need.
Last year, the EFS team focused on optimizing costs with the introduction of the EFS Infrequent Access (IA) storage class, with storage prices up to 92% lower compared to EFS Standard. You can quickly start reducing your costs by


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Check out The Amazon Builders’ Library – This is How We Do It!

Amazon customers often tell us that they want to know more about how we build and run our business. On the retail side, they tour Amazon Fulfillment Centers and see how we we organize our warehouses. Corporate customers often ask about our Leadership Principles, and sometimes adopt (and then adapt) them for their own use. I regularly speak with customers in our Executive Briefing Center (EBC), and talk to them about working backwards, PRFAQs, narratives, bar-raising, accepting failure as part of long-term success, and our culture of innovation.
The same curiosity that surrounds our business surrounds our development culture. We are often asked how we design, build, measure, run, and scale the hardware and software systems that underlie Amazon.com, AWS, and our other businesses.
New Builders’ Library Today I am happy to announce The Amazon Builders’ Library. We are launching with a collection of detailed articles that will tell you


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Amazon SageMaker Processing – Fully Managed Data Processing and Model Evaluation

Today, we’re extremely happy to launch Amazon SageMaker Processing, a new capability of Amazon SageMaker that lets you easily run your preprocessing, postprocessing and model evaluation workloads on fully managed infrastructure.
Training an accurate machine learning (ML) model requires many different steps, but none is potentially more important than preprocessing your data set, e.g.:
Converting the data set to the input format expected by the ML algorithm you’re using,
Transforming existing features to a more expressive representation, such as one-hot encoding categorical features,
Rescaling or normalizing numerical features,
Engineering high level features, e.g. replacing mailing addresses with GPS coordinates,
Cleaning and tokenizing text for natural language processing applications,
And more!
These tasks involve running bespoke scripts on your data set, (beneath a moonless sky, I’m told) and saving the processed version for later use by your training jobs. As you can guess, running them manually


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