Today, Amazon QuickSight announces three new natural language capabilities enabled by Amazon Q for business users. Launching in preview, these capabilities can summarize dashboards, generate mini dashboards to answer data questions, and build stories explaining data.

Amazon Web Services (AWS) is announcing the public preview of Amazon Q in the AWS Console Mobile Application. AWS customers using the AWS Console Mobile App for Android can now use Amazon Q, the generative AI assistant in the AWS Console Mobile App to ask questions about AWS services, and receive concise, reliable answers in a mobile-friendly user interface with voice input and output capabilities.

Amazon Connect now supports two-way Short Messaging Service (SMS) capabilities, making it easy for you to resolve customer issues via text messaging. SMS offers a ubiquitous and convenient channel for customers to get help, while enabling you to deliver personalized experiences at a lower cost.

Amazon Connect now provides in-app and web voice and video calling capabilities, making it easier to deliver more personalized voice and video experiences in your websites and mobile applications. These voice and video capabilities allow customers to contact you without having to leave your website or mobile application. You can use these capabilities to pass contextual information to Amazon Connect, enabling you to personalize the customer experience based on attributes such as the customer’s profile, authentication status, or actions previously taken within the app.

Amazon Connect Customer Profiles now provides a generative AI powered customer data mapping capability that significantly reduces the time needed to create unified profiles, enabling companies to help provide more personalized customer experiences faster. 

Amazon Connect announces a preview of analytics data lake, a Zero ETL analytics capability that empowers organizations to access the insights needed to understand and optimize key contact center performance metrics (e.g., customer satisfaction) via a unified data source and their choice of Business Intelligence (BI) tool. With the analytics data lake, records are de-duped and ready to query; eliminating the need to build and maintain complex data pipelines to perform extract, transform, and load (ETL) operations to access Amazon Connect data to get it ready for analytics, and artificial intelligence (AI) workloads.

Amazon Aurora zero-ETL integration with Amazon Redshift enables near real-time analytics and machine learning (ML) using Amazon Redshift on petabytes of transactional data from Amazon Aurora. Amazon Aurora PostgreSQL-Compatible Edition database clusters can now be used (in public preview) as a source for zero-ETL integrations. Within seconds of transactional data being written into Aurora, the data is available in Amazon Redshift. You don’t have to build and maintain complex data pipelines to perform extract, transform, and load (ETL) operations.

Today, AWS announces the preview of a new generative AI-based capability in Amazon DataZone to improve data discovery, data understanding and data usage by enriching the business data catalog. With a single click, data producers can generate comprehensive business data descriptions and context, highlight impactful columns, and include recommendations on analytical use cases.

Amazon Relational Database Service (Amazon RDS) for MySQL zero-ETL integration with Amazon Redshift allows you to access transactional data from Amazon RDS for MySQL to run analytics and machine learning (ML) on petabytes of data in Amazon Redshift. With the zero-ETL integration, you don’t need to build and maintain complex data pipelines to perform extract, transform, and load (ETL) operations. The Amazon RDS for MySQL zero-ETL integration with Amazon Redshift is now available in public preview for Amazon Redshift Serverless and Amazon Redshift RA3 instance types.

Amazon DynamoDB now supports zero-ETL integration with Amazon Redshift, enabling customers to run high performance analytics on their DynamoDB data. This zero-ETL integration has no impact on production workloads running on DynamoDB. As data is written into a DynamoDB table, it is seamlessly made available in Amazon Redshift, eliminating the need for customers to build and maintain complex data pipelines for performing extract, transform, and load (ETL) operations.