Today, we’re announcing three new capabilities for Amazon S3 Storage Lens that give you deeper insights into your storage performance and usage patterns. With the addition of performance metrics, support for analyzing billions of prefixes, and direct export to Amazon S3 Tables, you have the tools you need to optimize application performance, reduce costs, and make data-driven decisions about your Amazon S3 storage strategy.

New performance metric categories

S3 Storage Lens now includes eight new performance metric categories that help identify and resolve performance constraints across your organization. These are available at organization, account, bucket, and prefix levels. For example, the service helps you identify small objects in a bucket or prefix that can  slow down application performance. This can be mitigated by batching small objects or using the Amazon S3 Express One Zone storage class for higher performance small object workloads.

To access the new performance metrics, you need to enable performance metrics in the S3 Storage Lens advanced tier when creating a new Storage Lens dashboard or editing an existing configuration.

How it works

On the Amazon S3 console I choose Create Storage Lens dashboard to create a new dashboard. You can also edit an existing dashboard configuration. I then configure general settings such as providing a Dashboard name, Status, and the optional Tags. Then, I choose Next.

Next, I define the scope of the dashboard by selecting Include all Regions and Include all buckets and specifying the Regions and buckets to be included.

I opt in to the Advanced tier in the Storage Lens dashboard configuration, select Performance metrics, then choose Next.

Next, I select Prefix aggregation as an additional metrics aggregation, then leave the rest of the information as default before I choose Next.

I select the Default metrics report, then General purpose bucket as the bucket type, and then select the Amazon S3 bucket in my AWS account as the Destination bucket. I leave the rest of the information as default, then select Next.

I review all the information before I choose Submit to finalize the process.

After it’s enabled, I’ll receive daily performance metrics directly in the Storage Lens console dashboard. You can also choose to export report in CSV or Parquet format to any bucket in your account or publish to Amazon CloudWatch. The performance metrics are aggregated and published daily and will be available at multiple levels: organization, account, bucket, and prefix. In this dropdown menu, I choose the % concurrent PUT 503 error for the Metric, Last 30 days for the Date range, and 10 for the Top N buckets.

The Concurrent PUT 503 error count metric tracks the number of 503 errors generated by simultaneous PUT operations to the same object. Throttling errors can degrade application performance. For a single writer, modify retry behavior or use higher performance storage tier such as Amazon S3 Express One Zone to mitigate concurrent PUT 503 errors. For multiple writers scenario, use a consensus mechanism to avoid concurrent PUT 503 errors or use higher performance storage tier such as Amazon S3 Express One Zone.

Complete analytics for all prefixes in your S3 buckets

S3 Storage Lens now supports analytics for all prefixes in your S3 buckets through a new Expanded prefixes metrics report. This capability removes previous limitations that restricted analysis to prefixes meeting a 1% size threshold and a maximum depth of 10 levels. You can now track up to billions of prefixes per bucket for analysis at the most granular prefix level, regardless of size or depth.

The Expanded prefixes metrics report includes all existing S3 Storage Lens metric categories: storage usage, activity metrics (requests and bytes transferred), data protection metrics, and detailed status code metrics.

How to get started

I follow the same steps outlined in the How it works section to create or update the Storage Lens dashboard. In Step 4 on the console, where you select export options, you can select the new Expanded prefixes metrics report. Thereafter, I can export the expanded prefixes metrics report in CSV or Parquet format to any general purpose bucket in my account for efficient querying of my Storage Lens data.

Good to know

This enhancement addresses scenarios where organizations need granular visibility across their entire prefix structure. For example, you can identify prefixes with incomplete multipart uploads to reduce costs, track compliance across your entire prefix structure for encryption and replication requirements, and detect performance issues at the most granular level.

Export S3 Storage Lens metrics to S3 Tables

S3 Storage Lens metrics can now be automatically exported to S3 Tables, a fully managed feature on AWS with built-in Apache Iceberg support. This integration provides daily automatic delivery of metrics to AWS managed S3 Tables for immediate querying without requiring additional processing infrastructure.

How to get started

I start by following the process outlined in Step 5 on the console, where I choose the export destination. This time, I choose Expanded prefixes metrics report. In addition to General purpose bucket, I choose Table bucket.

The new Storage Lens metrics are exported to new tables in an AWS managed bucket aws-s3.

I select the expanded_prefixes_activity_metrics table to view API usage metrics for expanded prefix reports.

I can preview the table on the Amazon S3 console or use Amazon Athena to query the table.

Good to know

S3 Tables integration with S3 Storage Lens simplifies metric analysis using familiar SQL tools and AWS analytics services such as Amazon Athena, Amazon QuickSight, Amazon EMR, and Amazon Redshift, without requiring a data pipeline. The metrics are automatically organized for optimal querying, with custom retention and encryption options to suit your needs.

This integration enables cross-account and cross-Region analysis, custom dashboard creation, and data correlation with other AWS services. For example, you can combine Storage Lens metrics with S3 Metadata to analyze prefix-level activity patterns and identify objects in prefixes with cold data that are eligible for transition to lower-cost storage tiers.

For your agentic AI workflows, you can use natural language to query S3 Storage Lens metrics in S3 Tables with the S3 Tables MCP Server. Agents can ask questions such as ‘which buckets grew the most last month?’ or ‘show me storage costs by storage class’ and get instant insights from your observability data.

Now available

All three enhancements are available in all AWS Regions where S3 Storage Lens is currently offered (except the China Regions and AWS GovCloud (US)).

These features are included in the Amazon S3 Storage Lens Advanced tier at no additional charge beyond standard advanced tier pricing. For the S3 Tables export, you pay only for S3 Tables storage, maintenance, and queries. There is no additional charge for the export functionality itself.

To learn more about Amazon S3 Storage Lens performance metrics, support for billions of prefixes, and export to S3 Tables, refer to the Amazon S3 user guide. For pricing details, visit the Amazon S3 pricing page.

Veliswa Boya.

The post Amazon S3 Storage Lens adds performance metrics, support for billions of prefixes, and export to S3 Tables 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.

Today at the AWS Summit in New York City, Swami Sivasubramanian, AWS VP of Agentic AI, provided the day’s keynote on how we’re enabling customers to deliver production-ready AI agents at scale. Here’s our roundup of the biggest announcements from the event:

Introducing Amazon Bedrock AgentCore: Securely deploy and operate AI agents at any scale (preview)
Amazon Bedrock AgentCore enables rapid deployment and scaling of AI agents with enterprise-grade security. It provides memory management, identity controls, and tool integration—streamlining development while working with any open-source framework and foundation model.

Announcing Amazon Nova customization in Amazon SageMaker AI
AWS now enables extensive customization of Amazon Nova foundation models through SageMaker AI across all stages of model training. Available as ready-to-use SageMaker recipes, these capabilities allow customers to adapt Nova understanding models across pre-training and post-training, including fine-tuning and alignment recipes to better address business-specific requirements across industries.

AWS Free Tier update: New customers can get started and explore AWS with up to $200 in credits
AWS is enhancing its Free Tier program with up to $200 in credits for new users: $100 upon sign-up and an additional $100 earned by completing activities with services like Amazon EC2, Amazon Bedrock, and AWS Budgets.

TwelveLabs video understanding models are now available in Amazon Bedrock
TwelveLabs video understanding models are now available on Amazon Bedrock and enable customers to search through videos, classify scenes, summarize content, and extract insights with precision and reliability.

Amazon S3 Metadata now supports metadata for all your S3 objects
Amazon S3 Metadata now provides comprehensive visibility into all objects in S3 buckets through live inventory and journal tables, enabling SQL-based analysis of both existing and new objects with automatic updates within an hour of changes.

Introducing Amazon S3 Vectors: First cloud storage with native vector support at scale (preview)
Amazon S3 Vectors is a new cloud object store that provides native support for storing and querying vectors at massive scale, offering up to 90% cost reduction compared to conventional approaches while seamlessly integrating with Amazon Bedrock Knowledge Bases, SageMaker, and OpenSearch for AI applications.

Streamline the path from data to insights with new Amazon SageMaker capabilities
Amazon SageMaker has introduced three new capabilities—Amazon QuickSight integration for dashboard creation, governance, and sharing, Amazon S3 Unstructured Data Integration for cataloging documents and media files, and automatic data onboarding from Lakehouse—that eliminate data silos by unifying structured and unstructured data management, visualization, and governance in a single experience.

Monitor and debug event-driven applications with new Amazon EventBridge logging
Amazon EventBridge now offers enhanced logging capabilities that provide comprehensive event lifecycle tracking, helping users monitor and troubleshoot their event-driven applications with detailed logs that show when events are published, matched against rules, delivered to subscribers, or encounter failures.

Amazon EKS enables ultra scale AI/ML workloads with support for 100K nodes per cluster
Amazon EKS now scales to 100,000 nodes per cluster, enabling massive AI/ML workloads with up to 1.6M AWS Trainium accelerators or 800K NVIDIA GPUs. This allows organizations to efficiently train and run large AI models while maintaining Kubernetes compatibility and existing tooling integration.

The post Top announcements of the AWS Summit in New York, 2025 appeared first on The NOKIA Blog.

Modern organizations manage data across multiple disconnected systems—structured databases, unstructured files, and separate visualization tools—creating barriers that slow analytics workflows and limit insight generation. Separate visualization platforms often create barriers that prevent teams from extracting comprehensive business insights.

These disconnected workflows prevent your organizations from maximizing your data investments, creating delays in decision making and missed opportunities for comprehensive analysis that combines multiple data types.

Starting today, you can use three new capabilities in Amazon SageMaker to accelerate your path from raw data to actionable insights:

• Amazon QuickSight integration – Launch Amazon QuickSight directly from Amazon SageMaker Unified Studio to build dashboards using your project data, then publish them to the Amazon SageMaker Catalog for broader discovery and sharing across your organization.
• Amazon SageMaker adds support for Amazon S3 general purpose buckets and Amazon S3 Access Grants in SageMaker Catalog– Make data stored in Amazon S3 general purpose buckets easier for teams to find, access, and collaborate on all types of data including unstructured data, while maintaining fine-grained access control using Amazon S3 Access Grants.
• Automatic data onboarding from your lakehouse – Automatic onboarding of existing AWS Glue Data Catalog (GDC) datasets from the lakehouse architecture into SageMaker Catalog, without manual setup.

These new SageMaker capabilities address the complete data lifecycle within a unified and governed experience. You get automatic onboarding of existing structured data from your lakehouse, seamless cataloging of unstructured data content in Amazon S3, and streamlined visualization through QuickSight—all with consistent governance and access controls.

Let’s take a closer look at each capability.

Amazon SageMaker and Amazon QuickSight Integration
With this integration, you can build dashboards in Amazon QuickSight using data from your Amazon SageMaker projects. When you launch QuickSight from Amazon SageMaker Unified Studio, Amazon SageMaker automatically creates the QuickSight dataset and organizes it in a secured folder accessible only to project members.

Furthermore, the dashboards you build stay within this folder and automatically appear as assets in your SageMaker project, where you can publish them to the SageMaker Catalog and share them with users or groups in your corporate directory. This keeps your dashboards organized, discoverable, and governed within SageMaker Unified Studio.

To use this integration, both your Amazon SageMaker Unified Studio domain and QuickSight account must be integrated with AWS IAM Identity Center using the same IAM Identity Center instance. Additionally, your QuickSight account must exist in the same AWS account where you want to enable the QuickSight blueprint. You can learn more about the prerequisites on Documentation page. 

After these prerequisites are met, you can enable the blueprint for Amazon QuickSight by navigating to the Amazon SageMaker console and choosing the Blueprints tab. Then find Amazon QuickSight and follow the instructions.

You also need to configure your SQL analytics project profile to include Amazon QuickSight in Add blueprint deployment settings.

To learn more on onboarding setup, refer to the Documentation page.

Then, when you create a new project, you need to use the SQL analytics profile.

With your project created, you can start building visualizations with QuickSight. You can navigate to the Data tab, select the table or view to visualize, and choose Open in QuickSight under Actions.

This will redirect you to the Amazon QuickSight transactions dataset page and you can choose USE IN ANALYSIS to begin exploring the data.

When you create a project with the QuickSight blueprint, SageMaker Unified Studio automatically provisions a restricted QuickSight folder per project where SageMaker scopes all new assets—analyses, datasets, and dashboards. The integration maintains real-time folder permission sync, keeping QuickSight folder access permissions aligned with project membership.

Amazon Simple Storage Service (S3) general purpose buckets integration
Starting today, SageMaker adds support for S3 general purpose buckets in SageMaker Catalog to increase discoverability and allows granular permissions through S3 Access Grants, enabling users to govern data, including sharing and managing permissions. Data consumers, such as data scientists, engineers, and business analysts, can now discover and access S3 assets through SageMaker Catalog. This expansion also enables data producers to govern security controls on any S3 data asset through a single interface.

To use this integration, you need appropriate S3 general purpose bucket permissions, and your SageMaker Unified Studio projects must have access to the S3 buckets containing your data. Learn more about prerequisites on Amazon S3 data in Amazon SageMaker Unified Studio Documentation page.

You can add a connection to an existing S3 bucket.

When it’s connected, you can browse accessible folders and create discoverable assets by choosing on the bucket or a folder and selecting Publish to Catalog.

This action creates a SageMaker Catalog asset of type “S3 Object Collection” and opens an asset details page where users can augment business context to improve search and discoverability. Once published, data consumers can discover and subscribe to these cataloged assets. When data consumers subscribe to “S3 Object Collection” assets, SageMaker Catalog automatically grants access using S3 Access Grants upon approval, enabling cross-team collaboration while ensuring only the right users have the right access.

When you have access, now you can process your unstructured data in Amazon SageMaker Jupyter notebook. Following screenshot is an example to process image in medical use case.

If you have structured data, you can query your data using Amazon Athena or process using Spark in notebooks.

With this access granted through S3 Access Grants, you can seamlessly incorporate S3 data into my workflows—analyzing it in notebooks, combining it with structured data in the lakehouse and Amazon Redshift for comprehensive analytics. You can access unstructured data such as documents, images in JupyterLab notebooks to train ML models, or generate queryable insights.

Automatic data onboarding from your lakehouse
This integration automatically onboards all your lakehouse datasets into SageMaker Catalog. The key benefit for you is to bring AWS Glue Data Catalog (GDC) datasets into SageMaker Catalog, eliminating manual setup for cataloging, sharing, and governing them centrally.

This integration requires an existing lakehouse setup with Data Catalog containing your structured datasets.

When you set up a SageMaker domain, SageMaker Catalog automatically ingests metadata from all lakehouse databases and tables. This means you can immediately explore and use these datasets from within SageMaker Unified Studio without any configuration.

The integration helps you to start managing, governing, and consuming these assets from within SageMaker Unified Studio, applying the same governance policies and access controls you can use for other data types while unifying technical and business metadata.

Additional things to know
Here are a couple of things to note:

• Availability – These integrations are available in all commercial AWS Regions where Amazon SageMaker is supported.
• Pricing – Standard SageMaker Unified Studio, QuickSight, and Amazon S3 pricing applies. No additional charges for the integrations themselves.
• Documentation – You can find complete setup guides in the SageMaker Unified Studio Documentation.

Get started with these new integrations through the Amazon SageMaker Unified Studio console.

Happy building!
— Donnie

The post Streamline the path from data to insights with new Amazon SageMaker Catalog capabilities appeared first on The NOKIA Blog.

Amazon S3 Metadata now provides complete visibility into all your existing objects in your Amazon Simple Storage Service (Amazon S3) buckets, expanding beyond new objects and changes. With this expanded coverage, you can analyze and query metadata for your entire S3 storage footprint.

Today, many customers rely on Amazon S3 to store unstructured data at scale. To understand what’s in a bucket, you often need to build and maintain custom systems that scan for objects, track changes, and manage metadata over time. These systems are expensive to maintain and hard to keep up to date as data grows.

Since the launch of S3 Metadata at re:Invent 2024, you’ve been able to query new and updated object metadata using metadata tables instead of relying on Amazon S3 Inventory or object-level APIs such as ListObjects, HeadObject, and GetObject—which can introduce latency and impact downstream workflows.

To make it easier for you to work with this expanded metadata, S3 Metadata introduces live inventory tables that work with familiar SQL-based tools. After your existing objects are backfilled into the system, any updates like uploads or deletions typically appear within an hour in your live inventory tables.

With S3 Metadata live inventory tables, you get a fully managed Apache Iceberg table that provides a complete and current snapshot of the objects and their metadata in your bucket, including existing objects, thanks to backfill support. These tables are refreshed automatically within an hour of changes such as uploads or deletions, so you stay up to date. You can use them to identify objects with specific properties—like unencrypted data, missing tags, or particular storage classes—and to support analytics, cost optimization, auditing, and governance.

S3 Metadata journal tables, previously known as S3 Metadata tables, are automatically enabled when you configure live inventory tables, provide a near real-time view of object-level changes in your bucket—including uploads, deletions, and metadata updates. These tables are ideal for auditing activity, tracking the lifecycle of objects, and generating event-driven insights. For example, you can use them to find out which objects were deleted in the past 24 hours, identify the requester making the most PUT operations, or monitor updates to object metadata over time.

S3 Metadata tables are created in a namespace name that is similar to your bucket name for easier discovery. The tables are stored in AWS table buckets, grouped by account and Region. After you enable S3 Metadata for a general purpose S3 bucket, the system creates and maintains these tables for you. You don’t need to manage compaction or garbage collection processes—S3 Tables takes care of table maintenance tasks in the background.

These new tables help avoid waiting for metadata discovery before processing can begin, making them ideal for large-scale analytics and machine learning (ML) workloads. By querying metadata ahead of time, you can schedule GPU jobs more efficiently and reduce idle time in compute-intensive environments.

Let’s see how it works
To see how this works in practice, I configure S3 Metadata for a general purpose bucket using the AWS Management Console.

After choosing a general purpose bucket, I choose the Metadata tab, then I choose Create metadata configuration.

For Journal table, I can choose the Server-side encryption option and the Record expiration period. For Live Inventory table, I choose Enabled and I can select the Server-side encryption options.

I configure Record expiration on the journal table. Journal table records expire after the specified number of days, 365 days (one year) in my example.

Then, I choose Create metadata configuration.

S3 Metadata creates the live inventory table and journal table. In the Live Inventory table section, I can observe the Table status: the system immediately starts to backfill the table with existing object metadata. It can take between minutes to hours. The exact time depends on the quantity of objects you have in your S3 bucket.

While waiting, I also upload and delete objects to generate data in the journal table.

Then, I navigate to Amazon Athena to start querying the new tables.

I choose Query table with Athena to start querying the table. I can choose between a couple of default queries on the console.

In Athena, I observe the structure of the tables in the AWSDataCatalog Data source and I start with a short query to check how many records are available in the journal table. I already have 6,488 entries:

SELECT count(*) FROM "b_aws-news-blog-metadata-inventory"."journal";

# _col0
1 6488

Here are a couple of example queries I tried on the journal table:

# Query deleted objects in last 24 hours
# Use is_delete_marker=true for versioned buckets and record_type='DELETE' otherwise
SELECT bucket, key, version_id, last_modified_date
FROM "s3tablescatalog/aws-s3"."b_aws-news-blog-metadata-inventory"."journal"
WHERE last_modified_date >= (current_date - interval '1' day) AND is_delete_marker = true;

# bucket key version_id last_modified_date is_delete_marker
1 aws-news-blog-metadata-inventory .build/index-build/arm64-apple-macosx/debug/index/store/v5/records/G0/NSURLSession.h-JET61D329FG0 
2 aws-news-blog-metadata-inventory .build/index-build/arm64-apple-macosx/debug/index/store/v5/records/G5/cdefs.h-PJ21EUWKMWG5 
3 aws-news-blog-metadata-inventory .build/index-build/arm64-apple-macosx/debug/index/store/v5/records/FX/buf.h-25EDY57V6ZXFX 
4 aws-news-blog-metadata-inventory .build/index-build/arm64-apple-macosx/debug/index/store/v5/records/G6/NSMeasurementFormatter.h-3FN8J9CLVMYG6 
5 aws-news-blog-metadata-inventory .build/index-build/arm64-apple-macosx/debug/index/store/v5/records/G8/NSXMLDocument.h-1UO2NUJK0OAG8 

# Query recent PUT requests IP addresses
SELECT source_ip_address, count(source_ip_address)
FROM "s3tablescatalog/aws-s3"."b_aws-news-blog-metadata-inventory"."journal"
GROUP BY source_ip_address;

#	source_ip_address	_col1
1	my_laptop_IP_address	12488

# Query S3 Lifecycle expired objects in last 7 days
SELECT bucket, key, version_id, last_modified_date, record_timestamp
FROM "s3tablescatalog/aws-s3"."b_aws-news-blog-metadata-inventory"."journal"
WHERE requester = 's3.amazonaws.com' AND record_type = 'DELETE' AND record_timestamp > (current_date - interval '7' day);

(not applicable to my demo bucket)

The results helped me track the specific objects that were removed, including their timestamps.

Now, I look at the live inventory table:

# Distribution of object tags
SELECT object_tags, count(object_tags)
FROM "s3tablescatalog/aws-s3"."b_aws-news-blog-metadata-inventory"."inventory"
GROUP BY object_tags;

# object_tags    _col1
1 {Source=Swift} 1
2 {Source=swift} 1
3 {}             12486

# Query storage class and size for specific tags
SELECT storage_class, count(*) as count, sum(size) / 1024 / 1024 as usage
FROM "s3tablescatalog/aws-s3"."b_aws-news-blog-metadata-inventory"."inventory"
GROUP BY object_tags['pii=true'], storage_class;

# storage_class count   usage
1 STANDARD      124884  165

# Find objects with specific user defined metadata
SELECT key, last_modified_date, user_metadata
FROM "s3tablescatalog/aws-s3"."b_aws-news-blog-metadata-inventory"."inventory"
WHERE cardinality(user_metadata) > 0 ORDER BY last_modified_date DESC;

(not applicable to my demo bucket)

These are just a few examples of what is possible with S3 Metadata. Your preferred queries will depend on your use cases. Refer to Analyzing Amazon S3 Metadata with Amazon Athena and Amazon QuickSight in the AWS Storage Blog for more examples.

Pricing and availability
S3 Metadata live inventory and journal tables are available today in US East (N. Virginia), US East (Ohio), and US West (Oregon).

The journal tables are charged $0.30 per million updates. This is a 33 percent drop from our previous price.

For inventory tables, there’s a one-time backfill cost of $0.30 for a million objects to set up the table and generate metadata for existing objects. There are no additional costs if your bucket has less than one billion objects. For buckets with more than a billion objects, there is a monthly fee of $0.10 per million objects per month.

As usual, the Amazon S3 pricing page has all the details.

With S3 Metadata live inventory and journal tables, you can reduce the time and effort required to explore and manage large datasets. You get an up-to-date view of your storage and a record of changes, and both are available as Iceberg tables you can query on demand. You can discover data faster, power compliance workflows, and optimize your ML pipelines.

You can get started by enabling metadata inventory on your S3 bucket through the AWS console, AWS Command Line Interface (AWS CLI), or AWS SDKs. When they’re enabled, the journal and live inventory tables are automatically created and updated. To learn more, visit the S3 Metadata Documentation page.

— seb

Update 7/15/2025: Revised some code and updated Region list.

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It’s AWS Summit season! Free events are now rolling out worldwide, bringing our cloud computing community together to connect, collaborate, and learn. Whether you prefer joining us online or in-person, these gatherings offer valuable opportunities to expand your AWS knowledge. I’ll be attending the AWS Amsterdam Summit and would love to meet you—if you’re planning to be there, please stop by to say hello! Visit the AWS Summit website today to find events in your area, sign up for registration alerts, and reserve your spot at an AWS Summit near you.

Speaking of AWS news, let’s look at last week’s new announcements.

Last week’s launches
Here are the launches that got my attention.

AWS WAF integration with AWS Amplify Hosting now generally available – You can now directly attach AWS WAF to your AWS Amplify applications through a one-click integration in the Amplify console or using infrastructure as code (IaC). This integration provides access to the full range of AWS WAF capabilities, including managed rules that protect against common web exploits like SQL injection and cross-site scripting (XSS). You can also create custom rules based on your application needs, implement rate-based rules to protect against distributed denial of service (DDoS) attacks by limiting request rates from IP addresses, and configure geo-blocking to restrict access from specific countries. Firewall support is available in all AWS Regions in which Amplify Hosting operates.

Amazon Bedrock Custom Model Import introduces real-time cost transparency – If you’re using Amazon Bedrock Custom Model Import to run your customized foundation models (FMs), you can now access full transparency into compute resources and calculate inference costs in real time. Before model invocation, you can view the minimum compute resources (custom model units or CMUs) required through both the Amazon Bedrock console and Amazon Bedrock APIs. As models scale to handle increased traffic, Amazon CloudWatch metrics provide real-time visibility into total CMUs used, enabling better cost control through near-instant visibility. This helps you make on-the-fly model configuration changes to optimize costs. The feature is available in all Regions where Amazon Bedrock Custom Model Import is supported, with additional details available in Calculate the cost of running a custom model in the Amazon Bedrock User Guide.

Amazon Bedrock Knowledge Bases now supports Amazon OpenSearch Managed Cluster for vector storage – Amazon Bedrock Knowledge Bases securely connects FMs to company data sources for Retrieval Augmented Generation (RAG), delivering more relevant and accurate responses. With this launch, you can use Amazon OpenSearch Managed Cluster as a vector database while using the full suite of Amazon Bedrock Knowledge Bases features. This integration expands the list of supported vector databases, which already includes Amazon OpenSearch Serverless, Amazon Aurora, Amazon Neptune Analytics, Pinecone, MongoDB Atlas, and Redis. The native integration with vector databases helps mitigate the need to build custom data source integrations. This feature is now generally available in all existing Amazon Bedrock Knowledge Bases and OpenSearch Service Regions.

Amazon Bedrock Guardrails announces the general availability of industry-leading image content filters – This new capability offers industry-leading text and image content safeguards that help you block up to 88% of harmful multimodal content without building custom safeguards or relying on error-prone manual content moderation. Image content filters can be applied across all categories within the content filter policy including hate, insults, sexual, violence, misconduct, and prompt attacks. Amazon Bedrock Guardrails provides configurable safeguards to detect and block harmful content and prompt attacks, define topics to deny and disallow specific topics, redact personally identifiable information (PII) such as personal data, and block specific words. It also provides contextual grounding checks to detect and block model hallucinations and to identify the relevance of model responses and claims, and to identify, correct, and explain factual claims in model responses using Automated Reasoning checks. This capability is generally available in the US East (N. Virginia), US West (Oregon), Europe (Frankfurt), and Asia Pacific (Tokyo) Regions. To learn more, visit Amazon Bedrock Guardrails image content filters provide industry-leading safeguards in the AWS Machine Learning Blog and Stop harmful content in models using Amazon Bedrock Guardrails in the Amazon Bedrock User Guide.

Scenarios capability now generally available for Amazon Q in QuickSight – This capability guides you through data analysis by uncovering hidden trends, making recommendations for your business, and intelligently suggesting next steps for deeper exploration using natural language interactions. Now you can explore past trends, forecast future scenarios, and model solutions without needing specialized skill, analyst support, or manual manipulation of data in spreadsheets. With its intuitive interface and step-by-step guidance, the scenarios capability of Amazon Q in QuickSight helps you perform complex data analysis up to 10x faster than spreadsheets. Whether you’re optimizing marketing budgets, streamlining supply chains, or analyzing investments, Amazon Q makes advanced data analysis accessible so you can make data-driven decisions across your organization. This capability is accessible from any Amazon QuickSight dashboard, so you can move seamlessly from visualizing data to asking what-if questions and comparing alternatives. Previous analyses can be easily modified, extended, and reused, helping you quickly adapt to changing business needs.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

We launched existing services and instance types in additional Regions:

• Amazon DataZone is now available in Asia Pacific (Mumbai) and Europe (Paris) AWS Regions – Amazon DataZone is a fully managed data management service to catalog, discover, analyze, share, and govern data between data producers and consumers in your organization.
• The next generation of Amazon SageMaker is now available in Asia Pacific (Mumbai) and Europe (Paris) AWS Regions – Amazon SageMaker is the center for all your data, analytics, and AI. SageMaker Unified Studio provides a single development environment that consolidates AWS analytics and AI/ML tools.
• Amazon Redshift Query Editor V2 is now available in Mexico (Central) and Asia Pacific (Thailand) AWS Regions – Amazon Redshift Query Editor V2 makes data in your Amazon Redshift data warehouse and data lake more accessible with a web-based tool for SQL users such as data analysts, data scientists, and database developers.
• Amazon Keyspaces expands Multi-Region Replication to support all AWS Regions – Amazon Keyspaces (for Apache Cassandra) is a scalable, highly available, managed Cassandra-compatible database service that helps you run your Cassandra workloads on AWS using existing application code and developer tools.
• AWS Network Firewall is now available in the Asia Pacific (Thailand) and Mexico (Central) AWS Regions – AWS Network Firewall is a managed firewall service that automatically scales with traffic, requires no infrastructure maintenance, and integrates with AWS Firewall Manager for centralized policy control across AWS accounts.
• Amazon CloudWatch RUM is now generally available in Israel (Tel Aviv) and Asia Pacific (Hong Kong) AWS Regions – CloudWatch RUM monitors web applications by collecting real-time client-side performance data and providing dashboards that show end-user experience metrics, including page load anomalies, core web vitals, and errors across different geolocations, browsers, and devices.
• Amazon VPC IP Address Manager is now available in Asia Pacific (Thailand) and Mexico (Central) AWS Regions – Amazon Virtual Private Cloud (Amazon VPC) IP Address Manager (Amazon VPC IPAM) makes it easier to plan, track, and monitor IP addresses for AWS workloads, helping you organize addresses based on routing and security needs and set simple business rules to govern IP address assignments.
• Amazon Q Business now available in Asia Pacific (Sydney) AWS Region – Amazon Q Business is the most capable generative AI–powered assistant for finding information, gaining insight, and taking action at work. It can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems.
• Amazon EC2 P5en instances are now available in US East (N. Virginia) and Asia Pacific (Jakarta) AWS Regions – P5en instances feature 8 H200 GPUs with 1.7x memory size, paired with 4th Gen Intel Xeon processors and Gen5 PCIe for 4x CPU-GPU bandwidth. This helps improve collective communications performance for distributed training workloads such as deep learning, generative AI, real-time data processing, and high performance computing (HPC) applications.
• Amazon EC2 R8g instances now available in US West (N. California) AWS Region – These instances offer larger instance sizes with up to 3x more vCPU (up to 48xlarge) and memory (up to 1.5 TB) than AWS Graviton3 based R7g instances. These instances are up to 30% faster for web applications, 40% faster for databases, and 45% faster for large Java applications compared to Graviton3 based R7g instances.
• Amazon EC2 C8g instances now available in Asia Pacific (Tokyo) AWS Region – These instances offer larger instance sizes with up to 3x more vCPUs and memory compared to Graviton3 based Amazon C7g instances. AWS Graviton4 processors are up to 40% faster for databases, 30% faster for web applications, and 45% faster for large Java applications than AWS Graviton3 processors.
• Amazon SageMaker AI is now available in Mexico (Central) and Asia Pacific (Thailand) AWS Regions – Amazon SageMaker AI is a fully managed platform that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.
• Amazon ElastiCache now supports AWS PrivateLink in Asia Pacific (Jakarta) and Asia Pacific (Hyderabad) AWS Regions – AWS PrivateLink provides private connectivity between VPCs, AWS services, and on-premises networks without exposing traffic to the public internet and securing your network traffic. To use AWS PrivateLink with Amazon ElastiCache, you create an interface VPC endpoint for Amazon ElastiCache in your VPC using the Amazon VPC console, AWS SDK, or AWS Command Line Interface (AWS CLI).

Other AWS events
Check your calendar and sign up for upcoming AWS events.

AWS GenAI Lofts are collaborative spaces and immersive experiences that showcase AWS expertise in cloud computing and AI. They provide startups and developers with hands-on access to AI products and services, exclusive sessions with industry leaders, and valuable networking opportunities with investors and peers. Find a GenAI Loft location near you and don’t forget to register.

Browse all upcoming AWS led in-person and virtual events here.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

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The post AWS Weekly Roundup: Amazon Bedrock, Amazon QuickSight, AWS Amplify, and more (March 31, 2025) appeared first on The NOKIA Blog.

At re:Invent 2024, we launched Amazon S3 Tables, the first cloud object store with built-in Apache Iceberg support to streamline storing tabular data at scale, and Amazon SageMaker Lakehouse to simplify analytics and AI with a unified, open, and secure data lakehouse. We also previewed S3 Tables integration with Amazon Web Services (AWS) analytics services for you to stream, query, and visualize S3 Tables data using Amazon Athena, Amazon Data Firehose, Amazon EMR, AWS Glue, Amazon Redshift, and Amazon QuickSight.

Our customers wanted to simplify the management and optimization of their Apache Iceberg storage, which led to the development of S3 Tables. They were simultaneously working to break down data silos that impede analytics collaboration and insight generation using the SageMaker Lakehouse. When paired with S3 Tables and SageMaker Lakehouse in addition to built-in integration with AWS analytics services, they can gain a comprehensive platform unifying access to multiple data sources enabling both analytics and machine learning (ML) workflows.

Today, we’re announcing the general availability of Amazon S3 Tables integration with Amazon SageMaker Lakehouse to provide unified S3 Tables data access across various analytics engines and tools. You can access SageMaker Lakehouse from Amazon SageMaker Unified Studio, a single data and AI development environment that brings together functionality and tools from AWS analytics and AI/ML services. All S3 tables data integrated with SageMaker Lakehouse can be queried from SageMaker Unified Studio and engines such as Amazon Athena, Amazon EMR, Amazon Redshift, and Apache Iceberg-compatible engines like Apache Spark or PyIceberg.

With this integration, you can simplify building secure analytic workflows where you can read and write to S3 Tables and join with data in Amazon Redshift data warehouses and third-party and federated data sources, such as Amazon DynamoDB or PostgreSQL.

You can also centrally set up and manage fine-grained access permissions on the data in S3 Tables along with other data in the SageMaker Lakehouse and consistently apply them across all analytics and query engines.

S3 Tables integration with SageMaker Lakehouse in action
To get started, go to the Amazon S3 console and choose Table buckets from the navigation pane and select Enable integration to access table buckets from AWS analytics services.

Now you can create your table bucket to integrate with SageMaker Lakehouse. To learn more, visit Getting started with S3 Tables in the AWS documentation.

1. Create a table with Amazon Athena in the Amazon S3 console
You can create a table, populate it with data, and query it directly from the Amazon S3 console using Amazon Athena with just a few steps. Select a table bucket and select Create table with Athena, or you can select an existing table and select Query table with Athena.

When you want to create a table with Athena, you should first specify a namespace for your table. The namespace in an S3 table bucket is equivalent to a database in AWS Glue, and you use the table namespace as the database in your Athena queries.

Choose a namespace and select Create table with Athena. It goes to the Query editor in the Athena console. You can create a table in your S3 table bucket or query data in the table.

2. Query with SageMaker Lakehouse in the SageMaker Unified Studio
Now you can access unified data across S3 data lakes, Redshift data warehouses, third-party and federated data sources in SageMaker Lakehouse directly from SageMaker Unified Studio.

To get started, go to the SageMaker console and create a SageMaker Unified Studio domain and project using a sample project profile: Data Analytics and AI-ML model development. To learn more, visit Create an Amazon SageMaker Unified Studio domain in the AWS documentation.

After the project is created, navigate to the project overview and scroll down to project details to note down the project role Amazon Resource Name (ARN).

Go to the AWS Lake Formation console and grant permissions for AWS Identity and Access Management (IAM) users and roles. In the in the Principals section, select the <project role ARN> noted in the previous paragraph. Choose Named Data Catalog resources in the LF-Tags or catalog resources section and select the table bucket name you created for Catalogs. To learn more, visit Overview of Lake Formation permissions in the AWS documentation.

When you return to SageMaker Unified Studio, you can see your table bucket project under Lakehouse in the Data menu in the left navigation pane of project page. When you choose Actions, you can select how to query your table bucket data in Amazon Athena, Amazon Redshift, or JupyterLab Notebook.

When you choose Query with Athena, it automatically goes to Query Editor to run data query language (DQL) and data manipulation language (DML) queries on S3 tables using Athena.

Here is a sample query using Athena:

select * from "s3tablecatalog/s3tables-integblog-bucket”.”proddb"."customer" limit 10;

To query with Amazon Redshift, you should set up Amazon Redshift Serverless compute resources for data query analysis. And then you choose Query with Redshift and run SQL in the Query Editor. If you want to use JupyterLab Notebook, you should create a new JupyterLab space in Amazon EMR Serverless.

3. Join data from other sources with S3 Tables data
With S3 Tables data now available in SageMaker Lakehouse, you can join it with data from data warehouses, online transaction processing (OLTP) sources like relational or non-relational database, Iceberg tables, and other third party sources to gain more comprehensive and deeper insights.

For example, you can add connections to data sources such as Amazon DocumentDB, Amazon DynamoDB, Amazon Redshift, PostgreSQL, MySQL, Google BigQuery, or Snowflake and combine data using SQL without extract, transform, and load (ETL) scripts.

Now you can run the SQL query in the Query editor to join the data in the S3 Tables with the data in the DynamoDB.

Here is a sample query to join between Athena and DynamoDB:

select * from "s3tablescatalog/s3tables-integblog-bucket"."blogdb"."customer", 
              "dynamodb1"."default"."customer_ddb" where cust_id=pid limit 10;

To learn more about this integration, visit Amazon S3 Tables integration with Amazon SageMaker Lakehouse in the AWS documentation.

Now available
S3 Tables integration with SageMaker Lakehouse is now generally available in all AWS Regions where S3 Tables are available. To learn more, visit the S3 Tables product page and the SageMaker Lakehouse page.

Give S3 Tables a try in the SageMaker Unified Studio today and send feedback to AWS re:Post for Amazon S3 and AWS re:Post for Amazon SageMaker or through your usual AWS Support contacts.

In the annual celebration of the launch of Amazon S3, we will introduce more awesome launches for Amazon S3 and Amazon SageMaker. To learn more, join the AWS Pi Day event on March 14.

— Channy

—

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Happy New Year! We are witnessing technology augment human ingenuity in inspiring ways. In the coming years, using technology for positive impact will redefine the way we think about success. Amazon CTO, Dr. Werner Vogels, offers five forward-looking tech predictions for 2025, and beyond:

• The workforce of tomorrow is mission-driven
• A new era of energy efficiency drives innovation
• Technology tips the scales in the discovery of truth
• Open data drives decentralized disaster preparedness
• Intention-driven consumer technology takes hold

Download the Werner Vogels’ Tech Predictions for 2025 and Beyond ebook, or read Werner’s All Things Distributed blog to learn more about how these technological trends are shaping our world and paving the way for a more innovative, efficient, and purposeful future.

AWS re:Invent 2025 videos and re:Caps
If you’re looking to catch up on re:Invent announcements or delve deeper into the latest AWS innovations, you have several options available:

Launches from the last few weeks
Since our last week in review on December 16, 2024, I’d like to highlight some launches from year end, as well as from last week:

Availability of Llama 3.3 70B in Amazon SageMaker JumpStart and in Amazon Bedrock – Meta’s Llama 3.3 70B represents a significant advancement in model efficiency and performance optimization. Llama 3.3 70B is a text-only instruction-tuned model that provides enhanced performance relative to Llama 3.1 70B and Llama 3.2 90B, when used for text-only applications. You can now use the model in both Amazon SageMaker JumpStart, as well as Amazon Bedrock.

Availability of Stable Diffusion 3.5 Large in Amazon Bedrock – Stable Diffusion 3.5 Large by Stability AI is the most powerful model in the Stable Diffusion family at 8.1 billion parameters trained on Amazon SageMaker HyperPod. You can now use Stable Diffusion 3.5 Large in Amazon Bedrock to generate high-quality images from text descriptions.

New Amazon Kinesis source connector for Apache Flink – The Apache Flink community has released version 5.0.0 of AWS services connectors, an AWS open source contribution. This release introduces the Kinesis Streams Source, a new connector for reading data from Amazon Kinesis Data Streams, replacing the previous Kinesis Consumer.

Support of AWS Global Accelerator in Amazon WorkSpaces Personal – Amazon WorkSpaces Personal now integrates with AWS Global Accelerator (AGA) to enhance WorkSpaces connection performance by optimizing streaming traffic through the AWS Global Network and edge locations. This feature particularly benefits customers whose end users connect to WorkSpaces across long distances. You can enable the AGA feature at either the WorkSpaces directory level or for individual WorkSpaces running Amazon DCV protocol.

New resource insights in AWS Resource Explorer – With the enhanced Resource Explorer experience, relevant data and insights from multiple AWS services is centralized for supported resource types. You can use the new features to take actions on resources directly from the Resource Explorer console, such as manage tags, add resources to applications, and get additional information about a resource with Amazon Q.

General availability of custom billing view in AWS Billing and Cost Management – Custom billing view allows you to provide application and business unit owners access to relevant cost management data across multiple AWS accounts using a single view in AWS Cost Explorer without granting access to the AWS management account. You can create filtered views of cost management data based on cost allocation tags or specific AWS accounts.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS at CES 2025 (January 7-10) – AWS will be representing some of the latest cloud services and solutions that are purpose built for the automotive, mobility, transportation, and manufacturing industries. Join us to learn about the latest cloud capabilities across generative AI, software define vehicles, product engineering, sustainability, new digital customer experiences, connected mobility, autonomous driving, and so much more, in Amazon Experience Area.

AWS at NRF 2025 (January 12-14) – Join AWS at the retail’s big show to see generative AI and cutting-edge technologies in action. Listen to innovative big ideas sessions, curated TechTalks, and experience the latest in retail trends, technologies, and more.

Amazon QuickSight Learning Series – Kick off your 2025 by supercharging your data skills. Join the online Learning Series in January to learn about the cutting-edge features of Amazon QuickSight unveiled at re:Invent 2024.

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

–Prasad

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

The post Happy New Year! AWS Weekly Roundup: 2025 Tech Predictions, Llama 3.3 70B, Stable Diffusion 3.5 Large, custom billing view, and more (January 6, 2025) 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.

With the arrival of September, AWS re:Invent 2024 is now 3 months away and I am very excited for the new upcoming services and announcements at the conference. I remember attending re:Invent 2019, just before the COVID-19 pandemic. It was the biggest in-person re:Invent with 60,000+ attendees and it was my second one. It was amazing to be in that atmosphere! Registration is now open for AWS re:Invent 2024. Come join us in Las Vegas for five exciting days of keynotes, breakout sessions, chalk talks, interactive learning opportunities, and career-changing connections!

Now let’s look at the last week’s new announcements.

Last week’s launches
Here are the launches that got my attention.

Announcing AWS Parallel Computing Service – AWS Parallel Computing Service (AWS PCS) is a new managed service that lets you run and scale high performance computing (HPC) workloads on AWS. You can build scientific and engineering models and run simulations using a fully managed Slurm scheduler with built-in technical support and a rich set of customization options. Tailor your HPC environment to your specific needs and integrate it with your preferred software stack. Build complete HPC clusters that integrates compute, storage, networking, and visualization resources, and seamlessly scale from zero to thousands of instances. To learn more, visit AWS Parallel Computing Service and read Channy’s blog post.

Amazon EC2 status checks now support reachability health of attached EBS volumes – You can now use Amazon EC2 status checks to directly monitor if the Amazon EBS volumes attached to your instances are reachable and able to complete I/O operations. With this new status check, you can quickly detect attachment issues or volume impairments that may impact the performance of your applications running on Amazon EC2 instances. You can further integrate these status checks within Auto Scaling groups to monitor the health of EC2 instances and replace impacted instances to ensure high availability and reliability of your applications. Attached EBS status checks can be used along with the instance status and system status checks to monitor the health of your instances. To learn more, refer to the Status checks for Amazon EC2 instances documentation.

Amazon QuickSight now supports sharing views of embedded dashboards – You can now share views of embedded dashboards in Amazon QuickSight. This feature allows you to enable more collaborative capabilities in your application with embedded QuickSight dashboards. Additionally, you can enable personalization capabilities such as bookmarks for anonymous users. You can share a unique link that displays only your changes while staying within the application, and use dashboard or console embedding to generate a shareable link to your application page with QuickSight’s reference encapsulated using the QuickSight Embedding SDK. QuickSight Readers can then send this shareable link to their peers. When their peer accesses the shared link, they are taken to the page on the application that contains the embedded QuickSight dashboard. For more information, refer to Embedded view documentation.

Amazon Q Business launches IAM federation for user identity authentication – Amazon Q Business is a fully managed service that deploys a generative AI business expert for your enterprise data. You can use the Amazon Q Business IAM federation feature to connect your applications directly to your identity provider to source user identity and user attributes for these applications. Previously, you had to sync your user identity information from your identity provider into AWS IAM Identity Center, and then connect your Amazon Q Business applications to IAM Identity Center for user authentication. At launch, Amazon Q Business IAM federation will support the OpenID Connect (OIDC) and SAML2.0 protocols for identity provider connectivity. To learn more, visit Amazon Q Business documentation.

Amazon Bedrock now supports cross-Region inference – Amazon Bedrock announces support for cross-Region inference, an optional feature that enables you to seamlessly manage traffic bursts by utilizing compute across different AWS Regions. If you are using on-demand mode, you’ll be able to get higher throughput limits (up to 2x your allocated in-Region quotas) and enhanced resilience during periods of peak demand by using cross-Region inference. By opting in, you no longer have to spend time and effort predicting demand fluctuations. Instead, cross-Region inference dynamically routes traffic across multiple Regions, ensuring optimal availability for each request and smoother performance during high-usage periods. You can control where your inference data flows by selecting from a pre-defined set of Regions, helping you comply with applicable data residency requirements and sovereignty laws. Find the list at Supported Regions and models for cross-Region inference. To get started, refer to the Amazon Bedrock documentation or this Machine Learning blog.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

We launched existing services and instance types in additional Regions:

• Amazon EC2 C6gd and R6gd instances are now available in AWS Europe (Spain) Region. C6gd instances are ideal for compute-intensive workloads such as high performance computing (HPC), batch processing, and CPU-based machine learning inference. R6gd instances are built for running memory-intensive workloads such as open-source databases, in-memory caches, and real time big data analytics.
• Amazon OpenSearch Serverless now available in the AWS GovCloud (US-West) Region. OpenSearch Serverless is a serverless deployment option for Amazon OpenSearch Service that makes it simple to run search and analytics workloads without the complexities of infrastructure management.
• AWS Global Accelerator launches new edge location in Cairo, Egypt. AWS Global Accelerator is a networking service that helps you improve the availability and performance of the applications that you offer to your global users.
• Amazon Redshift Serverless is now available in the AWS Asia Pacific (Jakarta) Region. Amazon Redshift Serverless is a serverless option of Amazon Redshift that makes it more efficient to run and scale analytics in seconds without the need to set up and manage data warehouse infrastructure.
• Amazon OpenSearch Service now supports Graviton3 (C7g, M7g, R7g, R7gd) instances. Amazon OpenSearch Service now supports AWS Graviton3 instances, which deliver up to 25% better performance over Graviton2-based instances.
• AWS Config conformance packs now available in additional AWS Regions. Conformance packs allow you to bundle AWS Config rules and their associated remediation actions into a single package, simplifying deployment at scale. You can deploy and manage these conformance packs throughout your AWS environment. With this launch, AWS Config conformance packs are now available in 10 additional Regions, and organization-level management capabilities for conformance packs and individual AWS Config rules are now available in 12 additional Regions. To learn more about AWS Config conformance packs and Region availability, see our conformance packs documentation.

Other AWS events
AWS GenAI Lofts are collaborative spaces and immersive experiences that showcase AWS’s cloud and AI expertise, while providing startups and developers with hands-on access to AI products and services, exclusive sessions with industry leaders, and valuable networking opportunities with investors and peers. Find a GenAI Loft location near you and don’t forget to register.

credit: Antje Barth

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

AWS Summits are free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. AWS Summits for this year are coming to an end. There are 3 more left that you can still register: Jakarta (September 5), Toronto (September 11), and Ottawa (October 9).

AWS Community Days feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world. While AWS Summits 2024 are almost over, AWS Community Days are in full swing. Upcoming AWS Community Days are in Belfast (September 6), SF Bay Area (September 13), where our own Antje Barth is a keynote speaker, Argentina (September 14), and Armenia (September 14).

Browse all upcoming AWS led in-person and virtual events here.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

The post AWS Weekly Roundup: AWS Parallel Computing Service, Amazon EC2 status checks, and more (September 2, 2024) | Amazon Web Services appeared first on The NOKIA Blog.