Amazon Managed Grafana Is Now Generally Available with Many New Features

In December, we introduced the preview of Amazon Managed Grafana, a fully managed service developed in collaboration with Grafana Labs that makes it easy to use the open-source and the enterprise versions of Grafana to visualize and analyze your data from multiple sources. With Amazon Managed Grafana, you can analyze your metrics, logs, and traces without having to provision servers, or configure and update software.
During the preview, Amazon Managed Grafana was updated with new capabilities. Today, I am happy to announce that Amazon Managed Grafana is now generally available with additional new features:

Grafana has been upgraded to version 8 and offers new data sources, visualizations, and features, including library panels that you can build once and re-use on multiple dashboards, a Prometheus metrics browser to quickly find and query metrics, and new state timeline and status history visualizations.
To centralize the querying of additional data


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New – AWS Step Functions Workflow Studio – A Low-Code Visual Tool for Building State Machines

AWS Step Functions allow you to build scalable, distributed applications using state machines. Until today, building workflows on Step Functions required you to learn and understand Amazon State Language (ASL). Today, we are launching Workflow Studio, a low-code visual tool that helps you learn Step Functions through a guided interactive interface and allows you to prototype and build workflows faster.
In December 2016, when Step Functions was launched, I was in the middle of a migration to serverless. My team moved all the business logic from applications that were built for a traditional environment to a serverless architecture. Although we tried to have functions that did one thing and one thing only, when we put all the state management from our applications into the functions, they became very complex. When I saw that Step Functions was launched, I realized they would reduce the complexity of the serverless application we were


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Troubleshoot Boot and Networking Issues with New EC2 Serial Console

Fixing production issues is one of the key responsibilities of system and network administrators. In fact, I’ve always found it to be one of the most interesting parts of infrastructure engineering. Diving as deep as needed into the problem at hand, not only do you (eventually) have the satisfaction of solving the issue, you also learn a lot of things along the way, which you probably wouldn’t have been exposed to under normal circumstances.
Operating systems certainly present such opportunities. Over time, they’ve grown ever more complex, forcing administrators to master a zillion configuration files and settings. Although infrastructure as code and automation have greatly improved provisioning and managing servers, there’s always room for mistakes and breakdowns that prevent a system from starting correctly. The list is endless: missing hardware drivers, misconfigured file systems, invalid network configuration, incorrect permissions, and so on. To make things worse, many issues can effectively


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New – Amazon S3 Replication Adds Support for Multiple Destination Buckets

Amazon Simple Storage Service (S3) supports many types of replication, including S3 Same-Region Replication (SRR), which launched in 2019 and S3 Cross-Region Replication (CRR), which has been around since 2015. Today, we are happy to announce S3 Replication support for multiple destination buckets. S3 Replication now gives you the ability to replicate data from one source bucket to multiple destination buckets. With S3 Replication (multi-destination) you can replicate data in the same AWS Regions using S3 SRR or across different AWS Regions by using S3 CRR, or a combination of both.
Before this launch, if you needed to have multiple copies of your data in different S3 buckets, you had to build your own S3 replication service by monitoring S3 events, identifying created objects, and using AWS Lambda functions to copy objects to each destination bucket.
This launch removes the need for you to develop your own solutions to replicate


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S3 Intelligent-Tiering Adds Archive Access Tiers

We launched S3 Intelligent-Tiering two years ago, which added the capability to take advantage of S3 without needing to have a deep understanding of your data access patterns. Today we are launching two new optimizations for S3 Intelligent-Tiering that will automatically archive objects that are rarely accessed. These new optimizations will reduce the amount of […]


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Introducing Amazon SNS FIFO – First-In-First-Out Pub/Sub Messaging

When designing a distributed software architecture, it is important to define how services exchange information. For example, the use of asynchronous communication decouples components and simplifies scaling, reducing the impact of changes and making it easier to release new features.
The two most common forms of asynchronous service-to-service communication are message queues and publish/subscribe messaging:
With message queues, messages are stored on the queue until they are processed and deleted by a consumer. On AWS, Amazon Simple Queue Service (SQS) provides a fully managed message queuing service with no administrative overhead.
With pub/sub messaging, a message published to a topic is delivered to all subscribers to the topic. On AWS, Amazon Simple Notification Service (SNS) is a fully managed pub/sub messaging service that enables message delivery to a large number of subscribers. Each subscriber can also set a filter policy to receive only the messages that it cares about.
You can use topics


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Amazon Transcribe Now Supports Automatic Language Identification

In 2017, we launched Amazon Transcribe, an automatic speech recognition service that makes it easy for developers to add a speech-to-text capability to their applications. Since then, we added support for more languages, enabling customers globally to transcribe audio recordings in 31 languages, including 6 in real-time.
A popular use case for Amazon Transcribe is transcribing customer calls. This allows companies to analyze the transcribed text using natural language processing techniques to detect sentiment or to identify the most common call causes. If you operate in a country with multiple official languages or across multiple regions, your audio files can contain different languages. Thus, files have to be tagged manually with the appropriate language before transcription can take place. This typically involves setting up teams of multi-lingual speakers, which creates additional costs and delays in processing audio files.
The media and entertainment industry often uses Amazon Transcribe to convert media content


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Amazon ECS Now Supports EC2 Inf1 Instances

As machine learning and deep learning models become more sophisticated, hardware acceleration is increasingly required to deliver fast predictions at high throughput. Today, we’re very happy to announce that AWS customers can now use the Amazon EC2 Inf1 instances on Amazon ECS, for high performance and the lowest prediction cost in the cloud. For a few weeks now, these instances have also been available on Amazon Elastic Kubernetes Service.
A primer on EC2 Inf1 instancesInf1 instances were launched at AWS re:Invent 2019. They are powered by AWS Inferentia, a custom chip built from the ground up by AWS to accelerate machine learning inference workloads.
Inf1 instances are available in multiple sizes, with 1, 4, or 16 AWS Inferentia chips, with up to 100 Gbps network bandwidth and up to 19 Gbps EBS bandwidth. An AWS Inferentia chip contains four NeuronCores. Each one implements a high-performance systolic array matrix multiply engine,


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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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