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.

Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service provides customers advanced search capabilities, such as full-text and vector search, on their Amazon DynamoDB data. With a few button clicks in the AWS console, customers can now seamlessly synchronize their data from Amazon DynamoDB to Amazon OpenSearch Service, eliminating the need to write any custom code to extract, transform, and load the data. Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service is now available for both Amazon OpenSearch Service managed clusters and serverless collections.

Amazon Connect Contact Lens now provides real-time conversational analytics for Amazon Connect Chat , extending the machine learning-powered post-contact analytics (e.g., sentiment analysis, automated contact categorization, etc.) to real-time contact scenarios. These capabilities enable contact center managers to detect customer issues during in-progress chat contacts, and help them resolve customer issues faster. For example, managers can now get a real-time email alert when customer sentiment for a chat contact turns negative, allowing them to join the in-progress contact and help resolve the customer issue.

Amazon Connect now provides a no-code UI builder enabled within the drag-and-drop workflow designer that lets you create and manage the UI shown to agents in step-by-step guides. With this capability you can design a guide that presents what the agent should review or do inside the Amazon Connect agent workspace at any moment during a customer interaction.

Today, Amazon Web Services, Inc. (AWS) announced that Amazon Q can help you select Amazon EC2 instances. Amazon Q uses machine learning to help customers take quick and cost-effective decisions for their compute instance type before building their workloads. Amazon Q generates personalized EC2 instance suggestions for customers using the AWS Management Console or AWS documentation site. Its natural language interface provides an easy way for customers to communicate their requirements and get the best-matched instances.

You can now accelerate data processing and analysis with Apache Spark applications by up to 4.0x than data in S3 Standard using Amazon EMR and the Amazon S3 Express One Zone storage class. S3 Express One Zone is a high-performance, single-Availability Zone storage class purpose-built to deliver consistent, single-digit millisecond data access for your most frequently accessed data and latency-sensitive applications.