6/4/2024, 12:00:00 AM ~ 6/5/2024, 12:00:00 AM (UTC)

Recent Announcements

Introducing Amazon EMR Serverless Streaming jobs for continuous processing on streaming data

Amazon EMR Serverless is a serverless option in Amazon EMR that makes it simple for data engineers and data scientists to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers. We are excited to announce a new streaming job mode on Amazon EMR Serverless, enabling you to continuously analyze and process streaming data.\n Streaming has become vital for businesses to gain continuous insights from data sources like sensors, IoT devices, and web logs. However, processing streaming data can be challenging due to requirements such as high availability, resilience to failures, and integration with streaming services. Amazon EMR Serverless Streaming jobs has built-in features to addresses these challenges. It offers high availability through multi-AZ (Availability Zone) resiliency by automatically failing over to healthy AZs. It also offers increased resiliency through automatic job retries on failures and log management features like log rotation and compaction, preventing the accumulation of log files that might lead to job failures. In addition, Amazon EMR Serverless Streaming jobs support processing data from streaming services like self-managed Apache Kafka clusters, Amazon Managed Streaming for Apache Kafka, and now is integrated with Amazon Kinesis Data Streams using a new built-in Amazon Kinesis Data Streams Connector, making it easier to build end-to-end streaming pipelines.

Amazon API Gateway integration timeout limit increase beyond 29 seconds

Amazon API Gateway now enables customers to increase their integration timeout beyond the prior limit of 29 seconds. This setting represents the maximum amount of time API Gateway will wait for a response from the integration to complete. You can raise the integration timeout to greater than 29 seconds for Regional REST APIs and private REST APIs, but this might require a reduction in your account-level throttle quota limit. With this launch, customers with workloads requiring longer timeouts, such as Generative AI use cases with Large Language Models (LLMs), can leverage API Gateway.\n Amazon API Gateway is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. APIs act as the “front door” for applications to access data, business logic, or functionality from your backend services. Using API Gateway, you can create RESTful APIs and WebSocket APIs that enable real-time two-way communication applications. API Gateway supports containerized and serverless workloads, as well as web applications.

Amazon Timestream for LiveAnalytics now an Amazon EventBridge Pipes target

Amazon TimeStream for LiveAnalytics is now an Amazon EventBridge Pipes target, simplifying the ingestion of time-series data from sources such as Amazon Kinesis, Amazon DynamoDB, Amazon SQS, and more. Pipes provides a fully-managed experience, enabling you to easily ingest time-series data into Timestream for LiveAnalytics without the need to write undifferentiated integration code.\n Amazon Timestream for LiveAnalytics is fast, scalable, purpose-built time series database that makes it easy to store and analyze trillions of time series data points per day. Amazon EventBridge Pipes provides a simple, consistent, and cost-effective way to create point-to-point integrations between event producers and consumers. Now, with a few clicks, you can connect your applications generating time-series data to Timestream using Pipes, enabling you to monitor your applications in real time and quickly identify trends and patterns. You can now ingest time-series data from diverse sources using Eventbridge Pipes, making it easier to derive advanced insights.

Amazon Q offers inline completions in the command line

Today, Amazon Q Developer launches AI-powered inline completions in the command line. As developers type in their command line, Q Developer will provide real-time AI-generated code suggestions. For instance, if a developer types git, Q Developer might suggestion push origin main. Developers can accept the suggestion by simply pressing the right arrow.\n To generate accurate suggestions, Q Developer looks at your current shell context and your recent shell history. You can learn more about how Q Developer manages your data here.

AWS DMS now supports Babelfish for Aurora PostgreSQL as a source

AWS Database Migration Service (AWS DMS) now supports Babelfish for Aurora PostgreSQL as a source by enhancing its existing PostgreSQL endpoint to handle Babelfish data types. Babelfish is a feature of Amazon Aurora PostgreSQL-Compatible Edition that enables Aurora to understand commands from applications written for Microsoft SQL Server.\n AWS DMS supports both Full Load and Change Data Capture (CDC) migration modes for Babelfish. Full Load migration copies all of the data from the source database and CDC copies only the data that has changed since the last migration. To migrate your data from Babelfish, you can use the AWS DMS console, AWS CLI, or AWS SDKs. To learn more, refer to using Babelfish for Aurora PostgreSQL as a source for AWS DMS.

Amazon Connect agent workspace launches refreshed look and feel

The Amazon Connect agent workspace now features an updated user interface to improve productivity and focus for your agents. The new user interface is designed to be more intuitive, highly responsive, and increase visual consistency across capabilities, providing your agents with a streamlined user experience. With this launch, you can also easily build and embed third-party applications that have a consistent look and feel with the agent workspace by using Cloudscape Design System components.

Amazon Titan Text Embeddings V2 now available for use with Bedrock Knowledge Bases

Amazon Titan Text Embeddings V2, a new embeddings model in the Amazon Titan family of models, is now available for use with Knowledge Bases for Amazon Bedrock. Using Titan Text Embeddings V2, customers can embed their data into a vector database and use it to retrieve relevant information for tasks such as questions and answers, classification, or personalized recommendations.\n Amazon Text Embeddings V2 is optimized for retrieval augmented generation (RAG) and is an efficient model ideal for high accuracy retrieval tasks at different dimensions. The model supports flexible embeddings sizes (1024, 512 , 256) and maintains accuracy at smaller dimension sizes, helping to reduce storage costs without compromising on accuracy. When reducing from 1,024 to 512 dimensions, Titan Text Embeddings V2 retains approximately 99% retrieval accuracy, and when reducing from 1,024 to 256 dimensions, the model maintains 97% accuracy. Additionally, Titan Text Embeddings V2 includes multilingual support for 100+ languages in pre-training as well as unit vector normalization for improving accuracy of measuring vector similarity.

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