Tag: Tools

Amazon CloudWatch Logs Insights query language now supports querying log groups using tags, making it easier to analyze logs without listing the log groups explicitly. In addition to querying logs by log group names, data sources, and facets, customers can now query using log group tags. Tags are key-value pairs that customers can assign to log groups to categorize them — for example, Environment: Production, Application: PaymentService, or Owner: TeamName. With this launch, customers can run a query across all log groups that share common tags. As log group tags are added or removed, queries automatically reflect the matching log groups, reducing operational overhead as environments grow. Querying by log group tags is available today in all commercial AWS Regions. To learn more, see the Amazon CloudWatch Logs documentation.

Amazon WorkSpaces Applications now supports host-to-client URL redirection, which automatically launches URLs from streaming sessions in the user's local browser. Administrators can configure allow and deny URL patterns through the AWS Management Console to control which web content is redirected, enabling organizations to keep sensitive applications securely within the streaming environment while offloading resource-intensive content such as video streaming to local devices. With host-to-client URL redirection, organizations reduce the load on streaming infrastructure by shifting bandwidth-heavy web workloads to local devices, lowering infrastructure costs without impacting the end-user experience. The feature works for browser navigation and embedded links in applications such as Microsoft Word, with support for Chrome and Edge web browsers on the streaming host. URLs in the configured allow list open in the user's local default browser automatically. Host-to-cli

AWS Entity Resolution launches support for Machine Learning (ML) based Incremental Matching workflows in General Availability, fundamentally transforming how enterprises process entity resolution at scale. Previously, adding even a single new record required customers to reprocess their entire dataset—a process that could take up to 2 days and cost thousands of dollars. This created a critical bottleneck that forced major businesses to seek costly workarounds or alternative solutions.  With this enhancement, AWS Entity Resolution enables businesses to process only the new records added since their last workflow run. This launch provides dramatic efficiency gains: processing 1M incremental records in less than 1 hour which is a 95% reduction in processing time compared to current workloads , while also significantly reducing infrastructure costs. The feature supports incremental workloads up to 50M incremental records over datasets containing up to 1 billion histo

Amazon FSx, a fully-managed service that makes it easy and cost effective to launch, run, and scale feature-rich, high-performance file systems in the cloud, is now available in the AWS Asia Pacific (New Zealand) Region. Amazon FSx lets you choose between four widely-used file systems: NetApp ONTAP, Windows File Server, Lustre, and OpenZFS. It supports a wide range of workloads with its reliability, security, scalability, and broad set of capabilities. Amazon FSx is built on the latest AWS compute, networking, and disk technologies to provide high performance and lower TCO. And as a fully managed service, it handles hardware provisioning, patching, and backups — freeing you up to focus on your applications, your end users, and your business. To learn more about Amazon FSx, visit our product page, and see the AWS Region Table

Amazon Aurora DSQL introduces support for the PostgreSQL JSON data type with optional compression. With JSON data type support, you can now use code and tools that depend on PostgreSQL's JSON type with Aurora DSQL without modification, making it easier to store semi-structured data alongside relational data. You can use the JSON data type when creating or modifying tables to store semi-structured data such as API payloads, configuration objects, or event logs. With PostgreSQL compression enabled by default, larger JSON payloads are stored more efficiently, helping reduce storage costs. For details on the supported data types, see the Aurora DSQL documentation. Get started with Aurora DSQL for free with the AWS Free Tier. For information about Regional availability, see the

Amazon Quick now generates dashboards from natural language prompts with Generate Analysis. You describe the dashboard you want, select up to three datasets, and review an editable plan before generation. Amazon Quick then produces organized sheets with visuals selected for your data, filter controls for exploring by different dimensions, and calculated fields such as year-over-year growth and month-over-month comparisons.. Generate Analysis reduces dashboard creation from hours of manual configuration to minutes. With Generate Analysis, you can describe goals such as "create a sales performance dashboard with revenue trends, regional comparisons, and month-over-month growth" and receive a dashboard ready for refinement. The output works with existing publishing workflows, embedding, CI/CD pipelines, and point-and-click editing. At launch, Generate Analysis is available to Enterprise subscription/Author Pro users. Authors also have promotional access to this capabili

Amazon Quick now supports Dataset Q&A — a conversational analytics capability that enables users to ask natural language questions directly against their enterprise data. Alongside Dashboard Q&A, Dataset Q&A provides a powerful new way to interact with data in Amazon Quick — letting anyone with dataset access explore their data and get meaningful, actionable insights using natural language, while respecting all governance rules including Row Level and Column Level Security policies set by data owners.. Dataset Q&A is powered by Amazon Quick's text-to-SQL agent, which interprets user questions, identifies the right data, and generates precise SQL — all in a single conversational step. The agent works across various data sources users bring into Amazon Quick — generating engine- and dialect-aware optimized SQL against SPICE or AWS data assets such as Amazon Redshift, Amazon Athena, Aurora PostgreSQL, and Apache Iceberg tables stored in Amazon S3 table b

Today, Amazon EventBridge announces support for logging data plane APIs using AWS CloudTrail, enabling customers to have greater visibility into event bus activity in their AWS account for best practices in security and operational troubleshooting. Amazon EventBridge is a serverless event bus that enables customers to build event-driven applications at scale using events from AWS services, integrated SaaS applications, and custom sources. CloudTrail captures API activities related to Amazon EventBridge as events, including calls from the Amazon EventBridge console and calls made programmatically using Amazon EventBridge APIs. Using the information that CloudTrail collects, you can identify a specific request to an Amazon EventBridge API, the IP address of the requester, the requester's identity, and the date and time of the request. Logging EventBridge APIs using CloudTrail helps you enable operational and risk auditing, governance, and compliance of your AWS account. With

Amazon Quick now supports Amazon S3 table buckets as a data source — enabling users to build dashboards, run conversational analytics, and explore Apache Iceberg tables stored in S3 table buckets. With no intermediate data warehouse or OLAP layers required, users can now interoperate with their lakehouse data in Amazon Quick for both agentic AI and BI workloads — all through a simplified data architecture. Paired with Zero-ETL from sources like Salesforce, SAP, and Amazon Kinesis Data Firehose directly into S3 table buckets, users get near real-time insights with minimal pipeline dependencies. Getting started is straightforward: admins configure S3 table bucket permissions once, and authors can immediately create datasets and start building. S3 table bucket datasets are fully accessible through Amazon Quick's Dataset Q&A — ask a natural language question and get answers grounded in your data lake as the source of truth. Amazon S3 table buckets as a dat

Today, AWS announces the preview of the Amazon Quick extension for Microsoft Outlook, which brings generative AI-powered productivity directly into your email and calendar workflows. With the extension, you can use natural language to summarize unread messages, organize your inbox, schedule meetings, and draft in-line responses all without leaving Outlook. The Quick extension for Outlook helps you focus on what matters most by prioritizing emails, searching for specific discussions, and organizing messages into folders or flagging them for follow-up. Using conversational instructions, you can find optimal meeting times with coworkers and schedule meetings. For email threads, you can generate summaries, extract action items, and draft contextual replies that pull in relevant information from your Amazon Quick spaces and knowledge bases. You can also trigger actions in external applications using your configured integrations directly from Outlook. The Amazon Quick exte