Since 2013, Amazon Redshift has given the full power of a data warehouse in the cloud, at a fraction of the on-premises cost. Every architectural generation—from dense compute to Amazon RA3 instances, from provisioned to Amazon Redshift Serverless—has made each query cheaper, faster, and more efficient than the last.

For over a decade, as data volumes have grown and analytics requirements have evolved, organizations increasingly leverage both data warehouse tables for structured, frequently-accessed data and data lakes for cost-effective storage of diverse datasets. Add AI agents to the mix and they query your data warehouse at a scale that dwarfs typical human usage, leading to spiraling operational costs.

Amazon Redshift has doubled down on its core strengths to meet the demands of any workload — whether driven by humans or AI agents. For example, in March 2026, Amazon Redshift improved the performance of business intelligence (BI) dashboards and ETL workloads by speeding up new queries by up to 7 times. This significantly improves the response times of low-latency SQL queries, such as those used in near-real-time analytics applications, BI dashboards, ETL pipelines, and autonomous, goal-seeking AI agents.

Today, we’re announcing Amazon Redshift RG instances, a new instance family powered by AWS Graviton. RG instances deliver better performance, running data warehouse workloads up to 2.2x as fast as RA3 instances at 30% lower price per vCPU. Their integrated data lake query engine lets you run SQL analytics across your data warehouse and data lake from a single engine with performance up to 2.4x as fast as RA3 for Apache Iceberg and up to 1.5x as fast as RA3 for Apache Parquet. This blend of speed, cost efficiency, and an integrated data lake query engine makes Redshift RG instances well-suited to handle the high query volumes and low-latency requirements of today’s analytics and agentic AI workloads.

You can compare new RG instances and current RA3 instances:

This approach reduces total analytics costs for customers running combined data warehouse and data lake workloads, while simplifying operations through a single system for querying both warehouse tables and Amazon Simple Storage Service (Amazon S3) data lakes. We recommend using the AWS Pricing Calculator with your specific workload patterns to estimate savings.

Getting started with Amazon Redshift RG instances

You can launch new clusters or migrate existing clusters through the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS API. The integrated data lake query engine is enabled by default.

In the Amazon Redshift console, you can choose new RG instances when you create a cluster.

You can migrate previous-generation instances to RG instances with optimal paths based on your cluster configuration to estimate costs, validate compatibility, and automate execution.

• Elastic Resize—in-place migration with 10-15 minutes downtime for compatible configurations
• Snapshot and Restore—create a RG cluster from an RA3 snapshot. This is best for customers who want to make configuration changes during the migration

Your external tables, schemas, and query syntax—including existing Spectrum queries—remain unchanged. There is no need to recreate external tables or modify application code. To learn more, visit the Redshift Management Guide.

Amazon Redshift now executes data lake queries on cluster nodes—the same compute that processes data warehouse workloads. As a result, Amazon Redshift Spectrum is no longer required. Data lake queries stay within your VPC boundary, use existing IAM roles, and incur zero per-terabyte scanning charges. This removes the $5/TB Spectrum scanning fees that previously added to total Redshift costs.

Now available

Amazon Redshift RG instances are now available in the following AWS Regions: US East (N. Virginia, Ohio), US West (N. California, Oregon), Asia Pacific (Hong Kong, Hyderabad, Jakarta, Malaysia, Melbourne, Mumbai, Osaka, Seoul, Singapore, Sydney, Taiwan, Tokyo), Canada (Central), Europe (Frankfurt, Ireland, Milan, London, Paris, Spain, Stockholm), and South America (São Paulo). For Regional availability and a future roadmap, visit the AWS Capabilities by Region. For Redshift Provisioned, you can select On-Demand Instances with hourly billing and no commitments or choose Reserved Instances for cost savings. To learn more, visit the Amazon Redshift Pricing page.

Give RG instances a try in the Redshift console and send feedback to AWS re:Post for Amazon Redshift or through your usual AWS Support contacts.

— Channy

Updated 5/12/26: Middle East (UAE) removed from available regions.

The post Amazon Redshift introduces AWS Graviton-based RG instances with an integrated data lake query engine appeared first on The NOKIA Blog.

Today, we’re announcing the ability to access your data in Amazon FSx for NetApp ONTAP file systems using Amazon Simple Storage Service (Amazon S3). With this capability, you can use your enterprise file data to augment generative AI applications with Amazon Bedrock Knowledge Bases for Retrieval Augmented Generation (RAG), train machine learning (ML) models with Amazon SageMaker, generate insights with Amazon S3 integrated third-party services, use comprehensive research capabilities in AI-powered business intelligence (BI) tools such as Amazon Quick Suite, and run analyses using Amazon S3 based cloud-native applications, all while your file data continues to reside in your FSx for NetApp ONTAP file system.

Amazon FSx for NetApp ONTAP is the first and only fully AWS managed NetApp ONTAP file system in the cloud to migrate on-premises applications that rely on NetApp ONTAP or other network-attached storage (NAS) appliances to AWS without having to change how you manage your data. FSx for NetApp ONTAP provides the popular capabilities, high performance, and data management APIs of ONTAP file systems with the added benefits of the AWS Cloud, such as simplified management, on-demand scaling, and seamless integration with other AWS services.

Over the years, AWS has developed a broad range of industry-leading AI, ML, and analytics services and applications that work with data in Amazon S3 that organizations use to innovate faster, discover new insights, and make even better data-driven decisions. However, some organizations want to use these services with their enterprise file data stored in NetApp ONTAP or other NAS appliances.

How to get started

You can create and attach an S3 Access Point to your FSx for ONTAP file system using the Amazon FSx console, the AWS Command Line Interface (AWS CLI), or the AWS SDK.

I have an existing FSx for ONTAP file system demo-create-s3access which I created by following the steps in the Creating file systems in the FSx for ONTAP documentation. Using the Amazon FSx console I now choose the file system ID fs-0c45b011a7f071d70 to access the full details of the file system.

I’ll attach the access point to the volume of the file system. I choose the volume vol1 and then select Create S3 Access Point from the Actions dropdown menu.

I enter details such as the access point name, the type of file system user identity and the network configuration, then choose Create s3 Access Point to finalize the process.

After it’s created, the access point my-s3-accesspoint is ready to allow access to the file data stored in my file system demo-create-s3access from Amazon S3. Amazon Access Points are S3 endpoints that can be attached to Amazon FSx volumes and used to perform Amazon S3 object operations.

I can now bring proprietary data stored in the file system demo-create-s3access to Amazon S3 for use in applications that work with Amazon S3 while my file data continues to reside in the FSx for NetApp ONTAP file system using the access point my-s3-accesspoint (this data remains accessible through the file protocols).

For the walkthrough in this post, I’ll integrate with Quick Suite.

Integrating decades of enterprise file data with the latest AI powered BI tools on AWS

In the Quick Suite Console, in the left navigation pane, I choose Connections, then select Integrations. Before you begin, make sure that you have the correct permissions to the Amazon S3 AWS resource. You can control the AWS resources that Quick Suite can access by following the Amazon Quick Suite user guide.

After I’ve selected the Amazon S3 integration I enter my Amazon S3 Access Point alias as the S3 bucket URL, leave the rest of the information as default, then choose Create and continue.

I finalize the process by providing the Name of the knowledge base, the Description, then choose Create.

After the knowledge base has been created it’s automatically synchronized, it’s now available for interaction.

I want to learn more about the AWS European Sovereign Cloud so I’ve updated the file system (accessed through the S3 Access Point my-s3-accesspoin-iyytkgz83djdjj7abn3u711supfgkuse1b-ext-s3alias) with the AWS whitepaper on this topic. In the chat in Amazon Quick Suite. I start asking the first question “do we have any documentation on the europe sovereignty cloud?“. To answer my question, the chat agent accesses and analyzes various types of data sources I have permission to use, including uploaded files in my current conversation, spaces I have access to, knowledge bases from my integrations, and more.

When I verify the source, I see that the document I uploaded to my file system is listed as one of the sources.

Other use cases of Amazon S3 Access Points for Amazon FSx for NetApp ONTAP

Earlier, we looked at use cases such as connecting an organization’s proprietary file data to Amazon Quick Suite for advanced business intelligence. Additionally, Amazon S3 Access Points for Amazon FSx for NetApp ONTAP can be used to seamlessly integrate enterprise file data with comprehensive analytics services, such as Amazon Athena for serverless SQL queries or AWS Glue for ETL processing, to name a few.

Amazon S3 Access Points for Amazon FSx for NetApp ONTAP are also suitable for data access from serverless compute workloads that are cloud-native with containerized microservices that require flexible access to shared enterprise datasets, such as configuration files, reference data, content libraries, model artifacts, and application assets.

Now available

You can get started today using the Amazon FSx console, AWS CLI, or AWS SDK to attach Amazon S3 Access Points to your Amazon FSx for NetApp ONTAP file systems. The feature is available in the following AWS Regions: Africa (Cape Town), Asia Pacific (Hong Kong, Hyderabad, Jakarta, Melbourne, Mumbai, Osaka, Seoul, Singapore, Sydney, Tokyo), Canada (Central, Calgary), Europe (Frankfurt, Ireland, London, Milan, Paris, Spain, Stockholm, Zurich), Israel (Tel Aviv), Middle East (Bahrain, UAE), South America (Sao Paulo), US East (N. Virginia, Ohio), and US West (N. California Oregon). You’re billed by Amazon S3 for the requests and data transfer costs through your S3 Access Point, in addition to your standard Amazon FSx charges. Learn more on the Amazon FSx for NetApp ONTAP pricing page.

PS: Writing a blog post at AWS is always a team effort, even when you see only one name under the post title. In this case, I want to thank Luke Miller, for his expertise and generous help with technical guidance, which made this overview possible and comprehensive.

– Veliswa Boya.

The post Amazon FSx for NetApp ONTAP now integrates with Amazon S3 for seamless data access appeared first on The NOKIA Blog.

Today, I’m happy to announce Amazon EC2 Capacity Manager, a centralized solution to monitor, analyze, and manage capacity usage across all accounts and AWS Regions from a single interface. This service aggregates capacity information with hourly refresh rates and provides prioritized optimization opportunities, streamlining capacity management workflows that previously required custom automation or manual data collection from multiple AWS services.

Organizations using Amazon Elastic Compute Cloud (Amazon EC2) at scale operate hundreds of instance types across multiple Availability Zones and accounts, using On-Demand Instances, Spot Instances, and Capacity Reservations. This complexity means customers currently access capacity data through various AWS services including the AWS Management Console, Cost and Usage Reports, Amazon CloudWatch, and EC2 describe APIs. This distributed approach can create operational overhead through manual data collection, context switching between tools, and the need for custom automation to aggregate information for capacity optimization analysis.

EC2 Capacity Manager helps you overcome these operational complexities by consolidating all capacity data into a unified dashboard. You can now view cross-account and cross-Region capacity metrics for On-Demand Instances, Spot Instances, and Capacity Reservations across all commercial AWS Regions from a single location, eliminating the need to build custom data collection tools or navigate between multiple AWS services.

This consolidated visibility can help you discover cost savings by highlighting underutilized Capacity Reservations, analyzing usage patterns across instance types, and providing insights into Spot Instance interruption patterns. By having access to comprehensive capacity data in one place, you can make more informed decisions about rightsizing your infrastructure and optimizing your EC2 spending.

Let me show you the capabilities of EC2 Capacity Manager in detail.

Getting started with EC2 Capacity Manager
On the AWS Management Console, I navigate to Amazon EC2 and select Capacity Manager from the navigation pane. I enable EC2 Capacity Manager through the service settings. The service aggregates historical data from the previous 14 days during initial setup.

The main Dashboard displays capacity utilization across all instance types through a comprehensive overview section that presents key metrics at a glance. The capacity overview cards for Reservations, Usage, and Spot show trend indicators and percentage changes to help you identify capacity patterns quickly. You can apply filtering through the date filter controls, which include date range selection, time zone configuration, and interval settings.

You can select different units to analyze data by vCPUs, instance counts, or estimated costs to understand resource consumption patterns. Estimated costs are based on published On-Demand rates and do not include Savings Plans or other discounts. This pricing reference helps you compare the relative impact of underutilized capacity across different instance types—for example, 100 vCPU hours of unused p5 reservations represents a larger cost impact than 100 vCPU hours of unused t3 reservations.

The dashboard includes detailed Usage metrics with both total usage visualization and usage over time charts. The total usage section shows the breakdown between reserved usage, unreserved usage, and Spot usage. The usage over time chart provides visualization that tracks capacity trends over time, helping you identify usage patterns and peak demand periods.

Under Reservation metrics, Reserved capacity trends visualizes used and unused reserved capacity across the selected period, showing the proportion of reserved vCPU hours that remain unutilized compared with those actively consumed, helping you track reservation efficiency patterns and identify periods of consistent low utilization. This visibility can help you reduce costs by identifying underutilized reservations and helping you to make informed decisions about capacity adjustments.

The Unused capacity section lists underutilized capacity reservations by instance type and Availability Zone combinations, displaying specific utilization percentages and instance types across different Availability Zones. This prioritized list helps you identify potential savings with direct visibility into unused capacity costs.

The Usage tab provides detailed historical trends and usage statistics across all AWS Regions for Spot Instances, On-Demand Instances, and Capacity Reservations. Dedicated Hosts usage is not included. The Dimension filter helps you group by and filter capacity data by Account ID, Region, Instance Family, Availability Zone, and Instance Type, creating custom views that reveal usage patterns across your accounts and AWS Organizations. This helps you analyze specific configurations and compare performance across accounts or Regions.

The Aggregations section provides a comprehensive usage table across EC2 and Spot Instances. You can select different units to analyze data by vCPUs, instance counts, or estimated costs to understand resource consumption patterns. The table shows instance family breakdowns with total usage statistics, reserved usage hours, unreserved usage hours, and Spot usage data. Each row includes a View breakdown action for a detailed analysis.

The Capacity usage or estimated cost trends section visualizes usage trends, reserved usage, unreserved usage, and Spot usage. You can filter the displayed data and adjust the unit of measurement to view historical patterns. These filtering and analysis tools help you identify usage trends, compare costs across dimensions, and make informed decisions for capacity planning and optimization.

When you choose View breakdown from the Aggregations table, you access detailed Usage breakdown based on the dimension filters you selected. This breakdown view shows usage patterns for individual instance types within the selected family and Availability Zone combinations, helping you identify specific optimization opportunities.

The Reservations tab displays capacity reservation utilization with automated analysis capabilities that generate prioritized lists of optimization opportunities. Similar to the Usage tab, you can apply dimension filters by Account ID, Region, Instance Family, Availability Zone, and Instance Type along with additional options related to the reservation details. On each of the tabs you can drill down to see data for individual line items. For reservations specifically, you can view specific reservations and access detailed information about On-Demand Capacity Reservations (ODCRs), including utilization history, configuration parameters, and current status. When the ODCR exists in the same account as Capacity Manager, you can modify reservation parameters directly from this interface, eliminating the need to navigate to separate EC2 console sections for reservation management.

The Statistics section provides summary metrics, including total reservations count, overall utilization percentage, reserved capacity totals, used and unused capacity volumes, average scheduled reservations, and counts of accounts, instance families, and Regions with reservations.

This consolidated view helps you understand reservation distribution and utilization patterns across your infrastructure. For example, you might discover that your development accounts consistently show 30% reservation utilization while production accounts exceed 95%, indicating an opportunity to redistribute or modify reservations. Similarly, you could identify that specific instance families in certain Regions have sustained low utilization rates, suggesting candidates for reservation adjustments or workload optimization. These insights help you make data-driven decisions about reservation purchases, modifications, or cancellations to better align your reserved capacity with actual usage patterns.

The Spot tab focuses on Spot Instance usage and displays the amount of time your Spot instances run before being interrupted. This analysis of Spot Instance usage patterns helps you identify optimization opportunities for Spot Instance workloads. You can use Spot placement score recommendations to improve workload flexibility.

For organizations requiring data export capabilities, Capacity Manager includes data exports to Amazon Simple Storage Service (Amazon S3) buckets for capacity analysis. You can view and manage your data exports through the Data exports tab, which helps you create new exports, monitor delivery status, and configure export schedules to analyze capacity data outside the AWS Management Console.

Data exports extend your analytical capabilities by storing capacity data beyond the 90-day retention period available through the console and APIs. This extended retention enables long-term trend analysis and historical capacity planning. You can also integrate exported data with existing analytics workflows, business intelligence tools, or custom reporting systems to incorporate EC2 capacity metrics into broader infrastructure analysis and decision-making processes.

The Settings section provides configuration options for AWS Organizations integration, enabling centralized capacity management across multiple accounts. Organization administrators can enable enterprise-wide capacity visibility or delegate access to specific accounts while maintaining appropriate permissions and access controls.

Now available
EC2 Capacity Manager eliminates the operational overhead of collecting and analyzing capacity data from multiple sources. The service provides automated optimization opportunities, centralized multi-account visibility, and direct access to capacity management tools. You can reduce manual analysis time while improving capacity utilization and cost optimization across your EC2 infrastructure.

Amazon EC2 Capacity Manager is available at no additional cost. To begin using Amazon EC2 Capacity Manager, visit the Amazon EC2 console or access the service APIs. EC2 Capacity Manager is available in all commercial AWS Regions enabled by default.

To learn more, visit the EC2 Capacity Manager documentation.

— Esra

The post Monitor, analyze, and manage capacity usage from a single interface with Amazon EC2 Capacity Manager appeared first on The NOKIA Blog.

Today, we’re announcing Amazon Quick Suite, a new agentic teammate that quickly answers your questions at work and turns those insights into actions for you. Instead of switching between multiple applications to gather data, find important signals and trends, and complete manual tasks, Quick Suite brings AI-powered research, business intelligence, and automation capabilities into a single workspace. You can now analyze data through natural language queries, find critical information across enterprise and external sources in minutes, and automate processes from simple tasks to complex multi-department workflows.

Here’s a look into Quick Suite.

Business users often need to gather data across multiple applications—pulling customer details, checking performance metrics, reviewing internal product information, and performing competitive intelligence. This fragmented process often requires consultation with specialized teams to analyze advanced datasets, and in some cases, must be repeated regularly, reducing efficiency and leading to incomplete insights for decision-making.

Quick Suite helps you overcome these challenges by combining agentic teammates for research, business intelligence, and automation into a unified digital workspace for your day-to-day work.

Integrated capabilities that power productivity 
Quick Suite includes the following integrated capabilities:

• Research – Quick Research accelerates complex research by combining enterprise knowledge, premium third-party data, and data from the internet for more comprehensive insights.
• Business intelligence – Quick Sight provides AI-powered business intelligence capabilities that transform data into actionable insights through natural language queries and interactive visualizations, helping everyone make faster decisions and achieve better business outcomes.
• Automation – Quick Flows and Quick Automate help users and technical teams to automate any business process from simple, routine tasks to complex multi-department workflows, enabling faster execution and reducing manual work across the organization.

Let’s dive into some of these key capabilities.

Quick Index: Your unified knowledge foundation
Quick Index creates a secure, searchable repository that consolidates documents, files, and application data to power AI-driven insights and responses across your organization.

As a foundational component of Quick Suite, Quick Index operates in the background to bring together all your data—from databases and data warehouses to documents and email. This creates a single, intelligent knowledge base that makes AI responses more accurate and reduces time spent searching for information.

Quick Index automatically indexes and prepares any uploaded files or unstructured data you add to your Quick Suite, enabling efficient searching, sorting, and data access. For example, when you search for a specific project update, Quick Index instantly returns results from uploaded documents, meeting notes, project files, and reference materials—all from one unified search instead of checking different repositories and file systems.

To learn more, visit the Quick Index overview page.

Quick Research: From complex business challenges to expert-level insights
Quick Research is a powerful agent that conducts comprehensive research across your enterprise data and external sources to deliver contextualized, actionable insights in minutes or hours — work that previously could take longer.

Quick Research systematically breaks down complex questions into organized research plans. Starting with a simple prompt, it automatically creates detailed research frameworks that outline the approach and data sources needed for comprehensive analysis.

After Quick Research creates the plan, you can easily refine it through natural language conversations. When you are happy with the plan, it works in the background to gather information from multiple sources, using advanced reasoning to validate findings and provide thorough analysis with citations.

Quick Research integrates with your enterprise data connected to Quick Suite, the unified knowledge foundation that connects to your dashboards, documents, databases, and external sources, including Amazon S3, Snowflake, Google Drive, and Microsoft SharePoint. Quick Research grounds key insights to original sources and reveals clear reasoning paths, helping you verify accuracy, understand the logic behind recommendations, and present findings with confidence. You can trace findings back to their original sources and validate conclusions through source citations. This makes it ideal for complex topics requiring in-depth analysis.

To learn more, visit the Quick Research overview page.

Quick Sight: AI-powered business intelligence
Quick Sight provides AI-powered business intelligence capabilities that transform data into actionable insights through natural language queries and interactive visualizations.

You can create dashboards and executive summaries using conversational prompts, reducing dashboard development time while making advanced analytics accessible without specialized skills.

Quick Sight helps you ask questions about your data in natural language and receive instant visualizations, executive summaries, and insights. This generative AI integration provides you with answers from your dashboards and datasets without requiring technical expertise.

Using the scenarios capability, you can perform what-if analysis in natural language with step-by-step guidance, exploring complex business scenarios and finding answers faster than before.

Additionally, you can respond to insights with one-click actions by creating tickets, sending alerts, updating records, or triggering automated workflows directly from your dashboards without switching applications.

To learn more, visit Quick Sight overview page.

Quick Flows: Automation for everyone
With Quick Flows, any user can automate repetitive tasks by describing their workflow using natural language without requiring any technical knowledge. Quick Flows fetches information from internal and external sources, takes action in business applications, generates content, and handles process-specific requirements.

Starting with straightforward business requirements, it creates a multi-step flow including input steps for gathering information, reasoning groups for AI-powered processing, and output steps for generating and presenting results.

After the flow is configured, you can share it with a single click to your coworkers and other teams. To execute the flow, users can open it from the library or invoke it from chat, provide the necessary inputs, and then chat with the agent to refine the outputs and further customize the results.

To learn more, visit the Quick Flows overview page.

Quick Automate: Enterprise-scale process automation
Quick Automate helps technical teams build and deploy sophisticated automation for complex, multistep processes that span departments, systems, and third-party integrations. Using AI-powered natural language processing, Quick Automate transforms complex business processes into multi-agent workflows that can be created merely by describing what you want to automate or uploading process documentation.

While Quick Flows handles straightforward workflows, Quick Automate is designed for comprehensive and complex business processes like customer onboarding, procurement automations, or compliance procedures that involve multiple approval steps, system integrations, and cross-departmental coordination. Quick Automate offers advanced orchestration capabilities with extensive monitoring, debugging, versioning, and deployment features.

Quick Automate then generates a comprehensive automation plan with detailed steps and actions. You will find a UI agent that understands natural language instructions to autonomously navigate websites, complete form inputs, extract data, and produces structured outputs for downstream automation steps.

Additionally, you can define a custom agent, complete with instructions, knowledge, and tools, to complete process-specific tasks using the visual building experience – no code required.

Quick Automate includes enterprise-grade features such as user role management and human-in-the-loop capabilities that route specific tasks to users or groups for review and approval before continuing workflows. The service provides comprehensive observability with real-time monitoring, success rate tracking, and audit trails for compliance and governance.

To learn more, visit the Quick Automate overview page.

Additional foundational capabilities
Quick Suite includes other foundational capabilities that deliver seamless data organization and contextual AI interactions across your enterprise.

Spaces – Spaces provide a straightforward way for every business user to add their own context by uploading files or connecting to specific datasets and repositories specific to their work or to a particular function. For example, you might create a space for quarterly planning that includes budget spreadsheets, market research reports, and strategic planning documents. Or you could set up a product launch space that connects to your project management system and customer feedback databases. Spaces can scale from personal use to enterprise-wide deployment while maintaining access permissions and seamless integration with Quick Suite capabilities.

Chat agents – Quick Suite includes insights agents that you can use to interact with your data and workflows through natural language. Quick Suite includes a built-in agent to answer questions across all of your data and custom chat agents that you can configure with specific expertise and business context. Custom chat agents can be tailored for particular departments or use cases—such as a sales agent connected to your product catalog data and pricing information stored in a space or a compliance agent configured with your regulatory requirements and actions to request approvals.

Additional things to know
If you’re an existing Amazon QuickSight customer – Amazon QuickSight customers will be upgraded to Quick Suite, a unified digital workspace that includes all your existing QuickSight business intelligence capabilities (now called “Quick Sight”) plus new agentic AI capabilities. This is an interface and capability change—your data connectivity, user access, content, security controls, user permissions, and privacy settings remain exactly the same. No data is moved, migrated, or changed.

Quick Suite offers per-user subscription-based pricing with consumption-based charges for the Quick Index and other optional features. You can find more detail on the Quick Suite pricing page.

Now available
Amazon Quick Suite gives you a set of agentic teammates that helps you get the answers you need using all your data and move instantly from answers to action so you can focus on high value activities that drive better business and customer outcomes.

Visit the getting started page to start using Amazon Quick Suite today.

Happy building
— Esra and Donnie

The post Announcing Amazon Quick Suite: your agentic teammate for answering questions and taking action appeared first on The NOKIA Blog.

Starting today, you can attach Amazon S3 Access Points to your Amazon FSx for OpenZFS file systems to access your file data as if it were in Amazon Simple Storage Service (Amazon S3). With this new capability, your data in FSx for OpenZFS is accessible for use with a broad range of Amazon Web Services (AWS) services and applications for artificial intelligence, machine learning (ML), and analytics that work with S3. Your file data continues to reside in your FSx for OpenZFS file system.

Organizations store hundreds of exabytes of file data on premises and want to move this data to AWS for greater agility, reliability, security, scalability, and reduced costs. Once their file data is in AWS, organizations often want to do even more with it. For example, they want to use their enterprise data to augment generative AI applications and build and train machine learning models with the broad spectrum of AWS generative AI and machine learning services. They also want the flexibility to use their file data with new AWS applications. However, many AWS data analytics services and applications are built to work with data stored in Amazon S3 as data lakes. After migration, they can use tools that work with Amazon S3 as their data source. Previously, this required data pipelines to copy data between Amazon FSx for OpenZFS file systems and Amazon S3 buckets.

Amazon S3 Access Points attached to FSx for OpenZFS file systems remove data movement and copying requirements by maintaining unified access through both file protocols and Amazon S3 API operations. You can read and write file data using S3 object operations including GetObject, PutObject, and ListObjectsV2. You can attach hundreds of access points to a file system, with each S3 access point configured with application-specific permissions. These access points support the same granular permissions controls as S3 access points that attach to S3 buckets, including AWS Identity and Access Management (IAM) access point policies, Block Public Access, and network origin controls such as restricting access to your Virtual Private Cloud (VPC). Because your data continues to reside in your FSx for OpenZFS file system, you continue to access your data using Network File System (NFS) and benefit from existing data management capabilities.

You can use your file data in Amazon FSx for OpenZFS file systems to power generative AI applications with Amazon Bedrock for Retrieval Augmented Generation (RAG) workflows, train ML models with Amazon SageMaker, and run analytics or business intelligence (BI) with Amazon Athena and AWS Glue as if the data were in S3, using the S3 API. You can also generate insights using open source tools such as Apache Spark and Apache Hive, without moving or refactoring your data.

To get started
You can create and attach an S3 Access Point to your Amazon FSx for OpenZFS file system using the Amazon FSx console, the AWS Command Line Interface (AWS CLI), or the AWS SDK.

To start, you can follow the steps in the Amazon FSx for OpenZFS file system documentation page to create the file system, then, using the Amazon FSx console, go to Actions and select Create S3 access point. Leave the standard configuration and then create.

To monitor the creation progress, you can go to the Amazon FSx console.

Once available, choose the name of the new S3 access point and review the access point summary. This summary includes an automatically generated alias that works anywhere you would normally use S3 bucket names.

Using the bucket-style alias, you can access the FSx data directly through S3 API operations.

• List objects using the ListObjectsV2 API

• Get files using the GetObject API

• Write data using the PutObject API

The data continues to be accessible via NFS.

Beyond accessing your FSx data through the S3 API, you can work with your data using the broad range of AI, ML, and analytics services that work with data in S3. For example, I built an Amazon Bedrock Knowledge Base using PDFs containing airline customer service information from my travel support application repository, WhatsApp-Powered RAG Travel Support Agent: Elevating Customer Experience with PostgreSQL Knowledge Retrieval, as the data source.

To create the Amazon Bedrock Knowledge Base, I followed the connection steps in Connect to Amazon S3 for your knowledge base user guide. I chose Amazon S3 as the data source, entered my S3 access point alias as the S3 source, then configured and created the knowledge base.

Once the knowledge base is synchronized, I can see all documents and the Document source as S3.

Finally, I ran queries against the knowledge base and verified that it successfully used the file data from my Amazon FSx for OpenZFS file system to provide contextual answers, demonstrating seamless integration without data movement.

Things to know
Integration and access control – Amazon S3 Access Points for Amazon FSx for OpenZFS file systems support standard S3 API operations (such as GetObject, ListObjectsV2, PutObject) through the S3 endpoint, with granular access controls through AWS Identity and Access Management (IAM) permissions and file system user authentication. Your S3 Access Point includes an automatically generated access point alias for data access using S3 bucket names, and public access is blocked by default for Amazon FSx resources.

Data management – Your data stays in your Amazon FSx for OpenZFS file system while becoming accessible as if it were in Amazon S3, eliminating the need for data movement or copies, with file data remaining accessible through NFS file protocols.

Performance – Amazon S3 Access Points for Amazon FSx for OpenZFS file systems deliver first-byte latency in the tens of milliseconds range, consistent with S3 bucket access. Performance scales with your Amazon FSx file system’s provisioned throughput, with maximum throughput determined by your underlying FSx file system configuration.

Pricing – You’re billed by Amazon S3 for the requests and data transfer costs through your S3 Access Point, in addition to your standard Amazon FSx charges. Learn more on the Amazon FSx for OpenZFS pricing page.

You can get started today using the Amazon FSx console, AWS CLI, or AWS SDK to attach Amazon S3 Access Points to your Amazon FSx for OpenZFS file systems. The feature is available in the following AWS Regions: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, Stockholm), and Asia Pacific (Hong Kong, Singapore, Sydney, Tokyo).

— Eli

The post Amazon FSx for OpenZFS now supports Amazon S3 access without any data movement appeared first on The NOKIA Blog.

Today, we announced a new capability of Amazon Q in QuickSight that helps users perform scenario analyses to find answers to complex problems quickly. This AI-assisted data analysis experience helps business users find answers to complex problems by guiding them step-by-step through in-depth data analysis—suggesting analytical approaches, automatically analyzing data, and summarizing findings with suggested actions—using natural language prompts. This new capability eliminates hours of tedious and error-prone manual work traditionally required to perform analyses using spreadsheets or other alternatives. In fact, Amazon Q in QuickSight enables business users to perform complex scenario analysis up to 10x faster than spreadsheets. This capability expands upon existing data Q&A capabilities of Amazon QuickSight so business professionals can start their analysis by simply asking a question.

How it works
Business users are often faced with complex questions that have traditionally required specialized training and days or weeks of time analyzing data in spreadsheets or other tools to address. For example, let’s say you’re a franchisee with multiple locations to manage. You might use this new capability in Amazon Q in QuickSight to ask, “How can I help our new Chicago store perform as well as the flagship store in New York?” Using an agentic approach, Amazon Q would then suggest analytical approaches needed to address the underlying business goal, automatically analyze data, and present results complete with visualizations and suggested actions. You can conduct this multistep analysis in an expansive analysis canvas, giving you the flexibility to make changes, explore multiple analysis paths simultaneously, and adapt to situations over time.

This new analysis experience is part of Amazon QuickSight meaning it can read from QuickSight dashboards which connect to sources such as Amazon Athena, Amazon Aurora, Amazon Redshift, Amazon Simple Storage Service (Amazon S3), and Amazon OpenSearch Service. Specifically, this new experience is part of Amazon Q in QuickSight, which allows it to seamlessly integrate with other generative business intelligence (BI) capabilities such as data Q&A. You can also upload either a .csv or a single-table, single-sheet .xlsx file to incorporate into your analysis.

Here’s a visual walkthrough of this new analysis experience in Amazon Q in QuickSight.

I’m planning a customer event, and I’ve received an Excel spreadsheet of all who’ve registered to attend the event. I want to learn more about the attendees, so I analyze the spreadsheet and ask a few questions. I start by describing what I want to explore.

I upload the spreadsheet to start my analysis. Firstly, I want to understand how many people have registered for the event.

To design an agenda that’s suitable for the audience, I want to understand the various roles that will be attending. I select on the + icon to add a new block for asking a question following along the thread from the previous block.

I can continue to ask more questions. However, there are suggested questions for analyzing my data even further, and I now select one of these suggested questions. I want to increase marketing efforts at companies that don’t currently have a lot of attendees in this case, companies with fewer than two attendees.

Amazon Q executes the required analysis and keeps me updated of the progress. Step 1 of the process identifies companies that have fewer than two attendees and lists them.

Step 2 gives an estimate of how many more attendees I might get from each company if marketing efforts are increased.

In Step 3 I can see the potential increase in total attendees (including the percentage increase) in line with the increase in marketing efforts.

Lastly, Step 4 goes even further to highlight companies I should prioritize for these increased marketing efforts.

To increase the potential number of attendees even more, I wanted to change the analysis to identify companies with fewer than three attendees instead of two attendees. I choose the AI sparkle icon in the upper right to launch a modal that I then use to provide more context and make specific changes to the previous result.

This change resulted in new projections, and I can choose to consider them for my marketing efforts or keep to the previous projections.

Now available
Amazon Q in QuickSight Pro users can use this new capability in preview in the following AWS Regions at launch: US East (N. Virginia) and US West (Oregon). Get started with a free 30-day trial of QuickSight today. To learn more, visit the Amazon QuickSight User Guide. You can submit your questions to AWS re:Post for Amazon QuickSight, or through your usual AWS Support contacts.

– Veliswa.

The post Solve complex problems with new scenario analysis capability in Amazon Q in QuickSight appeared first on The NOKIA Blog.

Today, Amazon Bedrock introduces four enhancements that streamline how you can analyze data with generative AI:

Amazon Bedrock Data Automation (preview) – A fully managed capability of Amazon Bedrock that streamlines the generation of valuable insights from unstructured, multimodal content such as documents, images, audio, and videos. With Amazon Bedrock Data Automation, you can build automated intelligent document processing (IDP), media analysis, and Retrieval-Augmented Generation (RAG) workflows quickly and cost-effectively. Insights include video summaries of key moments, detection of inappropriate image content, automated analysis of complex documents, and much more. You can customize outputs to tailor insights into your specific business needs. Amazon Bedrock Data Automation can be used as a standalone feature or as a parser when setting up a knowledge base for RAG workflows.

Amazon Bedrock Knowledge Bases now processes multimodal data –To help build applications that process both text and visual elements in documents and images, you can configure a knowledge base to parse documents using either Amazon Bedrock Data Automation or use a foundation model (FM) as the parser. Multimodal data processing can improve the accuracy and relevancy of the responses you get from a knowledge base which includes information embedded in both images and text.

Amazon Bedrock Knowledge Bases now supports GraphRAG (preview) – We now offer one of the first fully-managed GraphRAG capabilities. GraphRAG enhances generative AI applications by providing more accurate and comprehensive responses to end users by using RAG techniques combined with graphs.

Amazon Bedrock Knowledge Bases now supports structured data retrieval – This capability extends a knowledge base to support natural language querying of data warehouses and data lakes so that applications can access business intelligence (BI) through conversational interfaces and improve the accuracy of the responses by including critical enterprise data. Amazon Bedrock Knowledge Bases provides one of the first fully-managed out-of-the-box RAG solutions that can natively query structured data from where it resides. This capability helps break data silos across data sources and accelerates building generative AI applications from over a month to just a few days.

These new capabilities make it easier to build comprehensive AI applications that can process, understand, and retrieve information from structured and unstructured data sources. For example, a car insurance company can use Amazon Bedrock Data Automation to automate their claims adjudication workflow to reduce the time taken to process automobile claims, improving the productivity of their claims department.

Similarly, a media company can analyze TV shows and extract insights needed for smart advertisement placement such as scene summaries, industry standard advertising taxonomies (IAB), and company logos. A media production company can generate scene-by-scene summaries and capture key moments in their video assets. A financial services company can process complex financial documents containing charts and tables and use GraphRAG to understand relationships between different financial entities. All these companies can use structured data retrieval to query their data warehouse while retrieving information from their knowledge base.

Let’s take a closer look at these features.

Introducing Amazon Bedrock Data Automation
Amazon Bedrock Data Automation is a capability of Amazon Bedrock that simplifies the process of extracting valuable insights from multimodal, unstructured content, such as documents, images, videos, and audio files.

Amazon Bedrock Data Automation provides a unified, API-driven experience that developers can use to process multimodal content through a single interface, eliminating the need to manage and orchestrate multiple AI models and services. With built-in safeguards, such as visual grounding and confidence scores, Amazon Bedrock Data Automation helps promote the accuracy and trustworthiness of the extracted insights, making it easier to integrate into enterprise workflows.

Amazon Bedrock Data Automation supports 4 modalities (documents, images, video, and audio). When used in an application, all modalities use the same asynchronous inference API, and results are written to an Amazon Simple Storage Service (Amazon S3) bucket.

For each modality, you can configure the output based on your processing needs and generate two types of outputs:

Standard output – With standard output, you get predefined default insights that are relevant to the input data type. Examples include semantic representation of documents, summaries of videos by scene, audio transcripts and more. You can configure which insights you want to extract with just a few steps.

Custom output – With custom output, you have the flexibility to define and specify your extraction needs using artifacts called “blueprints” to generate insights tailored to your business needs. You can also transform the generated output into a specific format or schema that is compatible with your downstream systems such as databases or other applications.

Standard output can be used with all formats (audio, documents, images, and videos). During the preview, custom output can only be used with documents and images.

Both standard and custom output configurations can be saved in a project to reference in the Amazon Bedrock Data Automation inference API. A project can be configured to generate both standard output and custom output for each processed file.

Let’s look at an example of processing a document for both standard and custom outputs.

Using Amazon Bedrock Data Automation
On the Amazon Bedrock console, I choose Data Automation in the navigation pane. Here, I can review how this capability works with a few sample use cases.

Then, I choose Demo in the Data Automation section of the navigation pane. I can try this capability using one of the provided sample documents or by uploading my own. For example, let’s say I am working on an application that needs to process birth certificates.

I start by uploading a birth certificate to see the standard output results. The first time I upload a document, I’m asked to confirm to create an S3 bucket to store the assets. When I look at the standard output, I can tailor the result with a few quick settings.

I choose the Custom output tab. The document is recognized by one of the sample blueprints and information is extracted across multiple fields.

Most of the data for my application is there but I need a few customizations. For example, the date the birth certificate was issued (JUNE 10, 2022) is in a different format than the other dates in the document. I also need the state that issued the certificate and a couple of flags that tell me if the child last name matches the one from the mother or the father.

Most of the fields in the previous blueprint use the Explicit extraction type. That means they’re extracted as they are from the document.

If I want a date in a specific format, I can create a new field using the Inferred extraction type and add instructions on how to format the result starting from the content of the document. Inferred extractions can be used to perform transformations, such as date or Social Security number (SSN) format, or validations, for example, to check if a person is over 21 based on today’s date.

Sample blueprints cannot be edited. I choose Duplicate blueprint to create a new blueprint that I can edit and then Add field from the Fields drop down.

I add four fields with extraction type Inferred and these instructions:

1. The date the birth certificate was issued in MM/DD/YYYY format
2. The state that issued the birth certificate 
3. Is ChildLastName equal to FatherLastName
4. Is ChildLastName equal to MotherLastName

The first two fields are strings and the last two booleans.

After I create the new fields, I can apply the new blueprint to the document I previously uploaded.

I choose Get result and look for the new fields in the results. I see the date formatted as I need, the two flags, and the state.

Now that I have created this custom blueprint tailored to the needs of my application, I can add it to a project. I can associate multiple blueprints with a project for the different document types I want to process, such as a blueprint for passports, a blueprint for birth certificates, a blueprint for invoices, and so on. When processing documents, Amazon Bedrock Data Automation matches each document to a blueprints within the project to extract relevant information.

I can also create a new blueprint form scratch. In that case, I can start with a prompt where I declare any fields I expect to find in the uploaded document and perform normalizations or validations.

Amazon Bedrock Data Automation can also process audio and video files. For example, here’s the standard output when uploading a video from a keynote presentation by Swami Sivasubramanian VP, AI and Data at AWS.

It takes a few minutes to get the output. The results include a summarization of the overall video, a summary scene by scene, and the text that appears during the video. From here, I can toggle the options to have a full audio transcript, content moderation, or Interactive Advertising Bureau (IAB) taxonomy.

I can also use Amazon Bedrock Data Automation as a parser when creating a knowledge base to extract insights from visually rich documents and images, for retrieval and response generation. Let’s see that in the next section.

Using multimodal data processing in Amazon Bedrock Knowledge Bases
Multimodal data processing support enables applications to understand both text and visual elements in documents.

With multimodal data processing, applications can use a knowledge base to:

• Retrieve answers from visual elements in addition to existing support of text.
• Generate responses based on the context that includes both text and visual data.
• Provide source attribution that references visual elements from the original documents.

When creating a knowledge base in the Amazon Bedrock console, I now have the option to select Amazon Bedrock Data Automation as Parsing strategy.

When I select Amazon Bedrock Data Automation as parser, Amazon Bedrock Data Automation handles the extraction, transformation, and generation of insights from visually rich content, while Amazon Bedrock Knowledge Bases manages ingestion, retrieval, model response generation, and source attribution.

Alternatively, I can use the existing Foundation models as a parser option. With this option, there’s now support for Anthropic’s Claude 3.5 Sonnet as parser, and I can use the default prompt or modify it to suit a specific use case.

In the next step, I specify the Multimodal storage destination on Amazon S3 that will be used by Amazon Bedrock Knowledge Bases to store images extracted from my documents in the knowledge base data source. These images can be retrieved based on a user query, used to generate the response, and cited in the response.

When using the knowledge base, the information extracted by Amazon Bedrock Data Automation or FMs as parser is used to retrieve information about visual elements, understand charts and diagrams, and provide responses that reference both textual and visual content.

Using GraphRAG in Amazon Bedrock Knowledge Bases
Extracting insights from scattered data sources presents significant challenges for RAG applications, requiring multi-step reasoning across these data sources to generate relevant responses. For example, a customer might ask a generative AI-powered travel application to identify family-friendly beach destinations with direct flights from their home location that also offer good seafood restaurants. This requires a connected workflow to identify suitable beaches that other families have enjoyed, match these to flight routes, and select highly-rated local restaurants. A traditional RAG system may struggle to synthesize all these pieces into a cohesive recommendation because the information lives in disparate sources and is not interlinked.

Knowledge graphs can address this challenge by modeling complex relationships between entities in a structured way. However, building and integrating graphs into an application requires significant expertise and effort.

Amazon Bedrock Knowledge Bases now offers one of the first fully managed GraphRAG capabilities that enhances generative AI applications by providing more accurate and comprehensive responses to end users by using RAG techniques combined with graphs.

When creating a knowledge base, I can now enable GraphRAG in just a few steps by choosing Amazon Neptune Analytics as database, automatically generating vector and graph representations of the underlying data, entities and their relationships, and reducing development effort from several weeks to just a few hours.

I start the creation of new knowledge base. In the Vector database section, when creating a new vector store, I select Amazon Neptune Analytics (GraphRAG). If I don’t want to create a new graph, I can provide an existing vector store and select a Neptune Analytics graph from the list. GraphRAG uses Anthropic’s Claude 3 Haiku to automatically build graphs for a knowledge base.

After I complete the creation of the knowledge base, Amazon Bedrock automatically builds a graph, linking related concepts and documents. When retrieving information from the knowledge base, GraphRAG traverses these relationships to provide more comprehensive and accurate responses.

Using structured data retrieval in Amazon Bedrock Knowledge Bases
Structured data retrieval allows natural language querying of databases and data warehouses. For example, a business analyst might ask, “What were our top-selling products last quarter?” and the system automatically generates and runs the appropriate SQL query for a data warehouse stored in an Amazon Redshift database.

When creating a knowledge base, I now have the option to use a structured data store.

I enter a name and description for the knowledge base. In Data source details, I use Amazon Redshift as Query engine. I create a new AWS Identity and Access Management (IAM) service role to manage the knowledge base resources and choose Next.

I choose Redshift serverless in Connection options and the Workgroup to use. Amazon Redshift provisioned clusters are also supported. I use the previously created IAM role for Authentication. Storage metadata can be managed with AWS Glue Data Catalog or directly within an Amazon Redshift database. I select a database from the list.

In the configuration of the knowledge base, I can define the maximum duration for a query and include or exclude access to tables or columns. To improve the accuracy of query generation from natural language, I can optionally add a description for tables and columns and a list of curated queries that provides practical examples of how to translate a question into a SQL query for my database. I choose Next, review the settings, and complete the creation of the knowledge base

After a few minutes, the knowledge base is ready. Once synced, Amazon Bedrock Knowledge Bases handles generating, running, and formatting the result of the query, making it easy to build natural language interfaces to structured data. When invoking a knowledge base using structured data, I can ask to only generate SQL, retrieve data, or summarize the data in natural language.

Things to know
These new capabilities are available today in the following AWS Regions:

• Amazon Bedrock Data Automation is available in preview in US West (Oregon).
• Multimodal data processing support in Amazon Bedrock Knowledge Bases using Amazon Bedrock Data Automation as parser is available in preview in US West (Oregon). FM as a parser is available in all Regions where Amazon Bedrock Knowledge Bases is offered.
• GraphRAG in Amazon Bedrock Knowledge Bases is available in preview in all commercial Regions where Amazon Bedrock Knowledge Bases and Amazon Neptune Analytics are offered.
• Structured data retrieval is available in Amazon Bedrock Knowledge Bases in all commercial Regions where Amazon Bedrock Knowledge Bases is offered.

As usual with Amazon Bedrock, pricing is based on usage:

• Amazon Bedrock Data Automation charges per images, per page for documents, and per minute for audio or video.
• Multimodal data processing in Amazon Bedrock Knowledge Bases is charged based on the use of either Amazon Bedrock Data Automation or the FM as parser.
• There is no additional cost for using GraphRAG in Amazon Bedrock Knowledge Bases but you pay for using Amazon Neptune Analytics as the vector store. For more information, visit Amazon Neptune pricing.
• There is an additional cost when using structured data retrieval in Amazon Bedrock Knowledge Bases.

For detailed pricing information, see Amazon Bedrock pricing.

Each capability can be used independently or in combination. Together, they make it easier and faster to build applications that use AI to process data. To get started, visit the Amazon Bedrock console. To learn more, you can access the Amazon Bedrock documentation and send feedback to AWS re:Post for Amazon Bedrock. You can find deep-dive technical content and discover how our Builder communities are using Amazon Bedrock at community.aws. Let us know what you build with these new capabilities!

— Danilo

The post New Amazon Bedrock capabilities enhance data processing and retrieval appeared first on The NOKIA Blog.