Amazon Bedrock Data Automation (BDA) now supports modality enablement, modality routing by file type, extraction of embedded hyperlinks when processing documents in Standard Output, and an increased overall document page limit of 3,000 pages. These new features give you more control over how your multimodal content is processed and improve BDA’s overall document extraction capabilities.
With Modality Enablement and Routing, you can configure which modalities (Document, Image, Audio, Video) should be enabled for a given project and manually specify the modality routing for specific file types. JPEG/JPG and PNG files can be processed as either Images or Documents based on your specific use case requirements. Similarly, MP4/M4V and MOV files can be processed as either video files or audio files, allowing you to choose the optimal processing path for your content.
Embedded Hyperlink Support enables BDA to detect and return embedded hyperlinks found in PDFs as part of the BDA standard output. This feature enhances the information extraction capabilities from documents, preserving valuable link references for applications such as knowledge bases, research tools, and content indexing systems.
Lastly, BDA now supports processing documents up to 3,000 pages per document, doubling the previous limit of 1,500 pages. This increased limit allows you to process larger documents without splitting them, simplifying workflows for enterprises dealing with long documents or document packets.
Amazon Bedrock Data Automation is generally available in the US West (Oregon) and US East (N. Virginia) AWS Regions.
Starting today, in the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions, you can now deliver events from an Amazon EventBridge Event Bus directly to AWS services in another account. Using multiple accounts can improve security and streamline business processes while reducing the overall cost and complexity of your architecture.
Amazon EventBridge Event Bus is a serverless event broker that enables you to create scalable event-driven applications by routing events between your own applications, third-party SaaS applications, and other AWS services. This launch allows you to directly target services in another account, without the need for additional infrastructure such as an intermediary EventBridge Event Bus or Lambda function, simplifying your architecture and reducing cost. For example, you can now route events from your EventBridge Event Bus directly to a different team’s SQS queue in a different account. The team receiving events does not need to learn about or maintain EventBridge resources and simply needs to grant IAM permissions to provide access to the queue. Events can be delivered cross-account to EventBridge targets that support resource-based IAM policies such as Amazon SQS, AWS Lambda, Amazon Kinesis Data Streams, Amazon SNS, and Amazon API Gateway.
In addition to the AWS GovCloud (US) Regions, direct delivery to cross-account targets is available in all commercial AWS Regions. To learn more, please read our blog post or visit our documentation. Pricing information is available on the EventBridge pricing page.
Today, AWS Resource Groups is adding support for an additional 160 resource types for tag-based Resource Groups. Customers can now use Resource Groups to group and manage resources from services such as AWS Code Catalyst and AWS Chatbot.
AWS Resource Groups enables you to model, manage and automate tasks on large numbers of AWS resources by using tags to logically group your resources. You can create logical collections of resources such as applications, projects, and cost centers, and manage them on dimensions such as cost, performance, and compliance in AWS services such as myApplications, AWS Systems Manager and Amazon CloudWatch.
Resource Groups expanded resource type coverage is available in all AWS Regions, including the AWS GovCloud (US) Regions. You can access AWS Resource Groups through the AWS Management Console, the AWS SDK APIs, and the AWS CLI.
Starting today, Amazon Q Developer operational investigations is available in preview in 11 additional regions. With this launch, Amazon Q Developer operational investigations is now available in US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Europe (Stockholm), Europe (Spain), Asia Pacific (Tokyo), Asia Pacific (Hong Kong), Asia Pacific (Sydney), Asia Pacific (Singapore), and Asia Pacific (Mumbai).
Amazon Q Developer helps you accelerate operational investigations across your AWS environment in just a fraction of the time. With a deep understanding of your AWS cloud environment and resources, Amazon Q Developer looks for anomalies in your environment, surfaces related signals for you to explore, identifies potential root-cause hypotheses, and suggests next steps to help you remediate issues faster.
The new operational investigation capability within Amazon Q Developer is available at no additional cost during preview. To learn more, see getting started and best practices documentation.
AWS Resource Explorer now supports AWS PrivateLink in all commercial AWS Regions, allowing you to search for and discover your AWS resources within your Amazon Virtual Private Cloud (VPC) without traversing the public internet.
With AWS Resource Explorer you can search for and discover your AWS resources across AWS Regions and accounts in your organization, either using the AWS Resource Explorer console, the AWS Command Line Interface (AWS CLI), the AWS SDKs, or the unified search bar from wherever you are in the AWS Management Console.
For more information about the AWS Regions where AWS Resource Explorer is available, see the AWS Region table.
The Amazon Connect agent workspace now supports additional capabilities for third-party applications including the ability make outbound calls, accept, transfer, and clear contacts, and update agent status. These enhancements allow you to integrate applications that give agents more intuitive workflows. For example, agents can now initiate one-click outbound calls from a custom-built call history interface that presents their most recent customer interactions.
Third-party applications are available in the following AWS Regions: US East (N. Virginia), US-West (Oregon), Africa (Cape Town), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), and Europe (London).
Starting today, AWS AppSync Events, a fully managed service for serverless WebSocket APIs with full connection management, now supports data source integrations for channel namespaces. This new feature enables developers to associate AWS Lambda functions, Amazon DynamoDB tables, Amazon Aurora databases, and other data sources with channel namespace handlers to process published events and subscription requests. Developers can now connect directly to Lambda functions without writing code and leverage both request/response and event modes for synchronous and asynchronous operations.
With these new capabilities, developers can create sophisticated event processing workflows by transforming and filtering published events using Lambda functions, or save batches of events to DynamoDB using the new AppSyncJS batch utilities for DynamoDB. This integration enables complex interactive flows, making it easier for developers to build rich, real-time applications with features like data validation, event transformation, and persistent storage of events. By simplifying the architecture of real-time applications, this enhancement significantly reduces development time and operational overhead for front-end web and mobile development.
This feature is now available in all AWS Regions where AWS AppSync is offered, providing developers worldwide with access to these powerful new integration capabilities. Powertools for AWS Lambda new AppSync Events integration are also now available to easily write your Lambda functions.
To learn more about AWS AppSync Events and channel namespace integrations, visit thelaunch blog post, the AWS AppSync documentation, and the Powertools for Lambda documentation (TypeScript, Python, .NET). You can get started with these new features through the AWS AppSync console.
In today’s data-driven world, the ability to extract meaningful insights quickly is paramount. Yet, for many, the journey from raw data to actionable intelligence is fraught with challenges. Complex SQL queries, time-consuming iterative analyses, and the gap between technical and non-technical users often hinder progress. BigQuery data canvas is a visual workspace designed to democratize data analysis and empower everyone to unlock the power of their BigQuery data. At Google Cloud Next 25 earlier this month, we introduced a built-in AI-assistive chat experience in data canvas powered by Gemini that encapsulates a variety of workflow analysis processes, ranging from data exploration to visualization, all with a single prompt.
Data canvas isn’t just another feature; it’s a fundamental change in how data practitioners interact with data. By seamlessly integrating visual workflows with BigQuery and Gemini, we’re bridging the gap between raw data and impactful insights.
The data canvas assistant at work
Core features: A deep dive
Let’s take a look at what you can do with the data canvas assistant.
Gemini powers your AI data agent
We integrated Gemini, our powerful AI model, into data canvas to enhance your data exploration experience. With it, you can use natural language to generate and refine queries, ask questions about your data, and receive intelligent suggestions and insights. For example, if you type “Show me the top 10 customers by revenue” data canvas powered by Gemini generates the corresponding query as well as offers insights about the dataset. Gemini also assists in data discovery, suggesting datasets that may be relevant to your questions.
The Gemini-powered AI chat experience encapsulates workflow analysis processes, from data exploration to visualization — all with a single prompt. Don’t know where to start? Use the suggested prompts to start exploring your data. Based on your selected or most used tables, BigQuery Data Canvas uses Gemini to generate natural language questions about your data, along with the corresponding SQL queries to answer them. You can add multiple data sources to the chat context from which Gemini can answer your questions. You can also further ground the chat by passing system instructions to pass domain knowledge about your data, to increase the accuracy of the resulting answers. For example, perhaps your organization’s fiscal year does not run from January to December — you can inform Gemini of this using system instructions. You can also use the system instructions to mold the way your answers are formatted and returned to you, e.g., “always present findings with charts, use green colour for positive and red color for negative.”
And coming soon, for complex problems like forecasting and anomaly detection, the chat experience will support advanced analysis using Python. Toggle this feature on in your chat’s settings bar, and based on the complexity of your prompt, Gemini chat assist will use a Python code interpreter to answer your question.
“Data Canvas is a game-changer in BigQuery, allowing data professionals to interactively discover, query, transform, and visualize data using a seamless blend of natural language processing and graphical workflows, all powered by Gemini AI.” – Sameer Zubair, Principal Platform Tech Lead, Virgin Media O2
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Visual query building: Explore multiple paths in one place
When sitting down to do data analysis, imagine a unified hub where you can filter, join, aggregate, or visualize data across multiple tables, each in its own container, all on the same page. Instead of forcing you down a linear path, data canvas uses a DAG (Directed Acyclic Graph) approach, allowing you to branch off at any point to explore alternative angles, circle back to earlier steps, or compare multiple outcomes simultaneously. Adding data is simple: just search for the tables you need, and add them to the canvas. You can start by asking questions of your data using natural language, and data canvas automatically generates the underlying SQL, which you can review or tweak whenever you like. This node-based method lowers the barrier to analysis for experienced SQL pros and newer analysts alike, allowing them to follow insights wherever they lead, without wrestling with complex query syntax.
Interactive visualizations: Uncover insights in real time
Data canvas offers a variety of interactive visualizations, from charts and graphs to tables. It’s easy to customize your visualizations, explore data interactively, and identify trends and anomalies. Want to see the distribution of sales across different regions? Add the “Region” and “Sales” fields onto the canvas, and let data canvas generate a chart for you automatically. Simply select the best visualization for the data, or select your own visualization, and watch as your data comes to life. Furthermore, you can export these visualizations as a PNG or to Looker Studio for further manipulation and sharing.
Putting data canvas to work in the real world
There’s no end of ways you can use new AI assistive capabilities in BigQuery data canvas. Here are a few industry-specific ideas to get your creative juices flowing.
Telecom support and diagnostics: Speeding up service restoration
Imagine a telecom support team that’s troubleshooting customer issues. Support tickets get ingested into BigQuery every hour, and can be queried in data canvas to extract who (customer phone), where (postcode), what (the affected service), when (timestamp), and which (closest cell tower). Each of these data points is handled in its own node, all within a single canvas, so analysts don’t need to toggle across multiple query tabs to perform this analysis. This visual workflow lets them spot localized outages, route technicians to the right towers, and resolve service disruptions faster than ever.
E-commerce analytics: Boosting sales and customer engagement
Picture a marketing team analyzing customer purchase data to optimize campaigns. Using data canvas, they can visually join customer and product tables, filter by purchase history, and visualize sales trends across different demographics. They can quickly identify top-selling products, high-value customer segments, and the effectiveness of their marketing campaigns, to make data-driven decisions.
Supply chain optimization: Streamlining logistics
A logistics manager could use data canvas to track inventory levels, analyze delivery routes, and identify potential bottlenecks. By visualizing this supply chain data, they can optimize delivery schedules, reduce costs, and improve efficiency. They can also create interactive dashboards to monitor key performance indicators and make real-time adjustments.
The future of data exploration is visual and AI-powered
BigQuery data canvas is a significant leap forward in making data accessible and actionable for everyone. By combining visual workflows, the power of BigQuery, and the intelligence of Gemini, we’re empowering you to unlock the full potential of your data. Start your journey today and experience the future of data exploration.
Get started with BigQuery data canvas today with this course. It’s completely free to use.
AWS AppConfig now supports dual-stack endpoints, facilitating connectivity through Internet Protocol Version 6. The existing AWS AppConfig endpoints supporting IPv4 will remain available for backwards compatibility.
The continuous growth of the internet has created an urgent need for IPv6 adoption, as IPv4 address space reaches its limits. Through AWS AppConfig’s implementation of dual-stack endpoints, organizations can execute a strategic transition to IPv6 architecture on their own timeline. This approach enables companies to satisfy IPv6 regulatory standards while preserving IPv4 connectivity for systems that have not yet moved to IPv6 capabilities.
Amazon SageMaker Lakehouse now supports attribute-based access control (ABAC), using AWS Identity and Access Management (IAM) principal and session tags to simplify data access, grant creation, and maintenance. With ABAC, you can manage permissions using dynamic business attributes associated with user identities.
Previously, SageMaker Lakehouse granted access to lakehouse databases and tables by directly assigning permissions to specific principals such as IAM users and IAM roles, a process that could quickly become unwieldy as the number of users grew. ABAC now allows administrators to grant permissions on a resource with conditions that specify user attribute keys and values. This means that any IAM principal or IAM role with matching principal or session tag keys and values will automatically have access to the resource making the experience more efficient. You can use ABAC though the AWS Lake Formation console to provide access to IAM users and IAM roles for both in-account and cross-account scenarios. For instance, rather than creating individual policies for each developer, administrators can now simply assign them an IAM tag with a key such as “team” and value “developers” and provide access to all developers with a single permission grant. As new developers join with the matching tag and value, no additional policy modifications are required.
This feature is available in all AWS Regions where SageMaker Lakehouse is available. To get started, read the launch blog and read ABAC documentation.
With this launch, VPC Reachability Analyzer and VPC Network Access Analyzer are now available in Europe (Spain) Region.
VPC Reachability Analyzer allows you to diagnose network reachability between a source resource and a destination resource in your virtual private clouds (VPCs) by analyzing your network configurations.For example, Reachability Analyzer can help you identify a missing route table entry in your VPC route table that could be blocking network reachability between an EC2 instance in Account A that is not able to connect to another EC2 instance in Account B in your AWS Organization.
VPC Network Access Analyzer allows you to identify unintended network access to your resources on AWS. Using Network Access Analyzer, you can verify whether network access for your VPC resources meets your security and compliance guidelines. For example, you can create a scope to verify that the VPCs used by your Finance team are separate, distinct, and unreachable from the VPCs used by your Development team.
Yesterday’s databases aren‘t sufficient for tomorrow’s applications, which need to deliver dynamic, AI-driven experiences at unpredictable scale and with zero downtime. To help, at Google Cloud Next 25, we announced new functionality, improved performance, and migration tooling to simplify modernizing database workloads from MySQL to Spanner, Google Cloud’s horizontally scalable, always-on operational database.
MySQL simply wasn’t designed for today’s most pressing scaling and availability needs. Common fixes, like sharding or manual replication, are complex and risky—coming exactly at the time when the business can tolerate it least. Planning and executing scaling on self-managed databases typically require expensive after-market solutions, which can take months to architect and test, diverting development teams from more pressing user-facing features. And because of the overhead of scaling, organizations often provision for peak usage, even if that capacity remains unused most of the time.
Tomorrow’s applications also need to do more than just transaction processing. New experiences like semantic discovery, collaborative recommendations, real-time fraud detection, and dynamic pricing, require different ways of storing and querying data.
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Simpler live migrations from MySQL to Spanner
To help organizations struggling to grow and modernize their apps, Spanner provides a well-defined migration path to safely and easily move production workloads from MySQL with virtually no downtime. Once there, they can take advantage of Spanner’s hands-free reliability and rich graph, full-text search, and integrated AI capabilities.
A key part of this is Spanner migration tool, which automates schema and data migration to support live cutovers, including consolidating petabyte-sized sharded MySQL databases in days, not months. Improved data movement templates provide increased throughput at significantly lower cost as well as new flexibility to transform data as it’s migrated, and updated built-in reverse replication synchronizes data back from Spanner to sharded MySQL instances to allow for near real-time failover in a disaster scenario. Finally, new Terraform configurations and CLI integration provide flexibility to customize implementations.
Spanner migration tool architecture
Improved latency with fewer code and query changes
To further reduce the cost and complexity of migrating application code and queries, we introduced a rich new set of relational capabilities in Spanner that map closely to MySQL.
Repeatable read is the default isolation level in MySQL, balancing performance and consistency. We’re excited to bring this flexibility to Spanner as well. New repeatable read isolation, now in preview, complements Spanner’s existing serializable isolation. It will be familiar to MySQL developers and gives them additional tools to significantly improve performance. In fact, most common workloads can see up to a 5x latency improvement compared to what was possible in Spanner previously. In addition, new auto_increment keys, SELECT…FOR UPDATE, and close to 80 new MySQL functions dramatically reduce the changes required to migrate an application to Spanner.
“As our calendar sharing service gained popularity, demand grew steadily. At 55 million users, we hit Aurora MySQL’s scalability limits for both data volume and active connections. But scalability wasn’t the only issue. Our app teams spent too much time managing the database, leaving less for feature development. Fully managed Spanner solved this, significantly cutting costs and enabling future growth. Migrations are challenging, but with Google Cloud support and the Spanner migration tool, we completed it successfully with minimal downtime.” – Eiki Kanai, SRE Manager, TimeTree
A recent Total Economic Impact study from Forrester Consulting also found that Spanner provided a 132% ROI and $7.74M of total benefits over three years for a composite organization representative of interviewed customers. This comes largely from retiring self-managed databases and taking advantage of Spanner’s elastic scalability and built-in, hands-free, high availability operations. Forrester found that decreased disruptions from unplanned downtime and system maintenance with Spanner reduced time to onboard new apps and allowed development teams to address new opportunities without complex re-architecture projects or new capital expenditures.
Get started today
To learn more about how Spanner can take the stress out of your organization’s next growth milestone and set your development teams up for success, visit https://cloud.google.com/spanner. There, you’ll find reference architectures, examples of successful migrations, and a directory of qualified partners to help with a free assessment. Read up on how to run a successful migration from MySQL. Or try Spanner yourself today with a 90-day free trial and production instances starting around $65 USD/month.
How is generative AI actually impacting developers’ daily work, team dynamics, and organizational outcomes? We’ve moved beyond simply asking if organizations are using AI, and instead are focusing on how they’re using it.
That’s why we’re excited to share DORA’s Impact of Generative AI in Software Development report. Based on extensive data and developer interviews, the report moves beyond the hype to offer perspective on AI’s impact on individuals, teams, and organizations.
Let’s take a look at some of the highlights – research-backed ways organizations are already benefitting from AI in their software development, plus five actionable ways to maximize AI’s benefits while mitigating potential risks.
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Understanding the real-world impact
Our research shows real productivity gains, organizational benefits, and grassroots adoption of AI.Here are just a few of the key highlights:
The AI imperative is real: A staggering 89% of organizations are prioritizing the integration of AI into their applications, and 76% of technologists are already using AI in some part of their daily work. This signals both top-down and grassroots adoption solidifying the fact that this isn’t a future trend; it’s happening now.
Productivity gains confirmed: Developers using gen AI report significant increases in flow, productivity, and job satisfaction. For instance, a 25% increase in AI adoption is associated with a 2.1% increase in individual productivity.
Organizational benefits are tangible: Beyond individual gains, we found strong correlations between AI adoption and improvements in crucial organizational metrics. A 25% increase in AI adoption is associated with increases in document quality, code quality, code review speed and approval speed.
How to maximize AI adoption and impact
So how do you make the most of AI in your software development? The report explores five practical approaches for both leaders and practitioners:
Have transparent communications: Our research suggests that organizations that apply this strategy can gain an estimated 11.4% increase in team adoption of AI.
Empower developers with learning and experimentation: Our research shows that giving developers dedicated time during work hours to explore AI leads to a 131% increase in team AI adoption.
Establish clear policies: Our data suggest that organizations with clear AI acceptable-use policies see a 451% increase in AI adoption compared to those without.
Rethink performance metrics: Shift the focus from hours worked to outcomes and value delivered. Acknowledge the labor involved in effectively working with AI, including prompt engineering and refining AI-generated output.
Embrace fast feedback loops: Implement mechanisms that enable faster feedback for continuous integration, code reviews, and testing. These loops are becoming even more critical as we venture into workflows with AI agents.
The future of software development is here
Generative AI is poised to revolutionize software development. But realizing its full potential requires a strategic, thoughtful, and human-centered approach.
Consumer packaged goods brands invest significantly in advertising, driving brand affinity to boost sales now and in the future. Campaigns are often optimized as they run by monitoring media-in-progress metrics against strategies like targeting specific audiences cohorts. However, because most sales happen in physical stores, accurately linking media sales lift to target audiences while ads are running can be a challenge.
Many solutions use“total ad sales” for measurement, but this metric doesn’t always correlate toincremental sales,which is Mars Wrigley’s gold standard key performance indicator (KPI) for media effectiveness.
So how do you know if your current ad spend is paying off while it’s still possible to optimize your in-flight campaigns?
Mars Wrigley is working with EPAM, and using Google Cloud Cortex Framework, to make significant progress tackling this issue with an approach that introduces an agile way to accurately measure in-flight audience effectiveness based on incremental sales.
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The Mars Wrigley approach: Connecting data for actionable audience insights
After exploring many solutions, Mars Wrigley decided to look inward and harness the power of its own data. However, this data was siloed in various media and retailer platforms.
To solve this, the company adopted Cortex Framework, using its pre-built data connectors and standardized data models to quickly integrate media data from sources like YouTube with sales information from retailers, creating a unified view of ad impact within a central, AI-ready cloud data foundation in BigQuery.
By combining data in BigQuery and using built-in data science tools like BQML, Mars Wrigley can now better understand how specific audience targeting strategies in its media investments are driving incremental sales lift across key customer groups.
For example, by identifying stores with similar sales patterns, the company can create geo-targeted control and expose Designated Market Areas (DMAs) for running audience testing.
By dividing its audiences into distinct segments, each with a control group, Mars Wrigley can experiment and monitor live campaign performance to optimize its investments for maximum sales lift.
Google Cloud Cortex Framework: Accelerating insights and decisions
The accelerated access to a consolidated AI-enabled data core represents a valuable addition to Mars Wrigley’s portfolio of media effectiveness tools. Cortex Framework provides instant insights with its predefined and customizable analytics content as well as seamless integration with major media platforms like Google Ads, YouTube, TikTok, Meta, and more.
“Before, we were struggling to get an accurate in-flight view of our audiences’ performance. With Google Cloud Cortex Framework, we realized that the answer was within our internal data. We partnered with EPAM Systems to harness the synergy of our internal data sources, enabling us to run timely experimentation based on actual sales lift. This filled an important gap within our portfolio of measurement tools and allowed us to continue making data-driven decisions when it matters.” – Lía Inoa Pimentel – Sr. Global Manager, Brand Experience & Media Measurement, Mars Wrigley.
By embracing Cortex Framework, Mars Wrigley is not only gaining a clearer understanding of media impact on sales but also paving the way for a more data-driven and agile approach to marketing in the consumer packaged goods industry.
This approach includes some of the following key benefits:
Agile hypothesis testing: Bringing insights in-house significantly accelerates the ability to test hypotheses and adapt strategies quickly.
Scalability: The architecture allows for easy expansion to encompass more media investment insights and a broader range of retail customers.
Versatility: Beyond audience testing, Mars Wrigley can also leverage Cortex Framework for other use cases, such as media formats, content variations, shopper media, and more.
To learn more about solutions that can help accelerate your marketing journey in the cloud visit the EPAM and Google Cloud Cortex Framework websites.
Amazon Redshift now supports history mode for zero-ETL integrations with eight third-party applications including Salesforce, ServiceNow, and SAP. This addition complements existing history mode support for Amazon Aurora PostgreSQL-compatible and MySQL-compatible, DynamoDB, and RDS for MySQL databases. The expansion enables you to track historical data changes without Extract, Transform, and Load (ETL) processes, simplifying data management across AWS and third-party applications.
History Mode for zero-ETL integrations with third-party applications lets customers easily run advanced analytics on historical data from their applications, build comprehensive lookback reports, and perform trend analysis and data auditing across multiple zero-ETL data sources. This feature preserves the complete history of data changes without maintaining duplicate copies across various external data sources, allowing organizations to meet data retention requirements while significantly reducing storage needs and operational costs. Available for both existing and new integrations, history mode offers enhanced flexibility by allowing selective enabling of historical tracking for specific tables within third-party application integrations, giving businesses precise control over their data analysis and storage strategies.
To learn more about history mode for zero-ETL integrations in Amazon Redshift and how it can benefit your data analytics workflows, visit the history mode documentation. To learn more about the supported third-party applications, visit the AWS Glue documentation. To get started with zero-ETL integrations, visit the getting started guides for Amazon Redshift.
In November 2024, we launched Prompt Optimization in Amazon Bedrock to accelerate prompt creation and engineering for foundation models (FMs). Today, we’re announcing its general availability and pricing.
Prompt engineering is the process of designing prompts to guide FMs to generate relevant responses. These prompts must be customized for each FM according to its best practices and guidelines, which is a time-consuming process that delays application development. With Prompt Optimization in Amazon Bedrock, you can now automatically rewrite prompts for better performance and more concise responses on Anthropic, Llama, Nova, DeepSeek, Mistral and Titan models. You can compare optimized prompts against original versions without deployment and save them in Amazon Bedrock Prompt Management for prompt lifecycle management. You can also use Prompt Optimization in Bedrock Playground, or directly via API.
Prompt Optimization is now generally available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Sydney), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), South America (São Paulo). To get started, see the following resources:
AWS Database Migration Service Serverless (AWS DMS Serverless) now offers storage scaling. With this enhancement you never have to worry about exceeding the DMS Serverless 100GB default replication storage capacity limit when processing very large transaction volumes or using detailed logging.
You can now use AWS DMS Serverless for replicating even the highest of transaction volumes since there is no longer any storage capacity limits. AWS DMS Severless will automatically increase the storage for your replications any time the existing capacity reaches it limits.
The demand for software as a service (SaaS) based solutions is exploding, fueled by AI-driven hyper-personalization, the rise of specialized vertical solutions, and a no-code revolution. However, building and scaling a successful SaaS can be daunting for would-be SaaS providers. Challenges include difficulty in creating personalized experiences, management complexity across thousands of instances, and dealing with diverse infrastructure to meet performance and reliability goals. These operational burdens often distract from what matters most: innovating and delivering exceptional customer experiences.
At Google Cloud Next 25, we announced the preview of SaaS Runtime, a fully managed Google Cloud service management platform for SaaS providers to simplify and automate the complexities of infrastructure operations, empowering them to focus on their core business. Based on our own internal platform for serving millions of users across multiple tenants, SaaS Runtime leverages our extensive experience managing services at Google scale. SaaS Runtime helps you model your SaaS environment, accelerates deployments, and streamlines operations with a rich set of tools to manage at scale, with automation at its core.
With SaaS Runtime, our vision is for software providers to be able to:
Launch quickly, customize and iterate: SaaS Runtime empowers you with pre-built customizable blueprints, allowing for rapid iteration and deployment. You can easily integrate AI architecture blueprints into existing systems through simple data model abstractions.
Automate operations, observe and scale tenants: As a fully managed service, SaaS Runtime allows automation at scale. Starting from your current continuous integration/continuous delivery (CI/CD) pipeline, onboard to SaaS Runtime and then scale it to simplify service management, tenant observability and operations across both cloud and edge environments.
Integrate, optimize and expand rapidly: SaaS Runtime is deeply integrated into the Google Cloud ecosystem. Developers can design applications with the new Application Design Center, offer them in Google Cloud Marketplace, and once they are deployed across tenants, monitor their performance with Cloud Observability and App Hub. This integrated approach offers developers a unified, application-layer view that’s enriched with full business context, facilitating rapid integration, optimization, and scaling.
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How SaaS Runtime works: Model, Deploy, Operate
SaaS Runtime simplifies SaaS lifecycle management through a streamlined, three-step process: Model, Deploy, and Operate.
Imagine you have an Inventory Management Application (IMS) that you want to offer as SaaS to enable retail businesses, so they can optimize stock, predict demand, and reduce waste across each of their stores. As a SaaS provider, you want to provide pricing tiers and augment your application with AI capabilities. Let’s look at how to do that on Google Cloud.
1. Model
As the SaaS provider, you can start by defining or importing the SaaS architecture using SaaS Runtime’s opinionated data model. SaaS providers who are looking to use SaaS Runtime to scale can re-use their existing architecture by using Saas Runtime’s opinionated model framework, which lets you package components that deploy and update together into separate blueprints. This structured approach abstracts the complexities of the application and infrastructure, so you can optimize for performance and reliability.
First, create a Blueprint by importing Terraform modules via popular source repositories (e.g., GitHub, GitLab, Bitbucket). These blueprints act as the building blocks for the SaaS product, and make it easy to scale to thousands of tenant instances.
For our Inventory Management SaaS scenario, to enable independent deployments and rapid iteration, you’d employ a layered, two-blueprint structure: a base infrastructure blueprint, and a dependent IMS application blueprint. While this example uses two blueprints, SaaS Runtime’s composable blueprint model allows for flexible, customized groupings and dependencies so you can meet diverse business goals, with reliability at its core.
2. Deploy
SaaS Runtime automates provisioning and orchestrating blueprints, helping to ensure that they are consistent and reliable. In order to deploy the Inventory Management SaaS, SaaS Runtime creates two releases — one each for the Base and IMS blueprints.
As the SaaS provider, you can now provision an instance of Inventory Management SaaS (Tenant Instance) for each retailer by using the releases you created, each with the necessary personalization for each retailer.
3. Operate
SaaS Runtime’s comprehensive service management tools allow you as the SaaS provider to manage, observe and optimize your SaaS operations. These tools let you:
Roll out existing releases at scale – In order to roll out the Base and IMS releases to all the retailers, use the rollout capability. Select each release one at a time and roll out to all tenants at once. Alternately, you can roll out to one region at a time, for safer and more reliable rollouts.
Roll out new releases – To roll out an update to the Inventory Management SaaS application with a new AI capability such as Dynamic Pricing Optimization, create a new AI blueprint that depends on the IMS blueprint. Create a new release that is rolled out to all or selected retailer tenants.
Roll out new features with Feature Flag configuration – To roll out a new feature such as promotional pricing to select retailer tenants, use SaaS Runtime’s Feature Flag capability. You can enable the feature by changing the configuration for the “PromoPricing” flag to true without rolling out a new binary update.
Proactively monitor and control progress of rollouts and deployments – You can also dry-run your rollout across a small number of instances of your SaaS to validate its functionality and reliability before rolling it out across all your tenants. Additionally, you can observe the progress of your rollouts across all the regions and tenants. If there are issues during the rollout, you can always pause or resume the rollout, or even cancel the rollout, roll back, or decide to roll out at another time.
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Get started today
SaaS Runtime lets software providers innovate and scale SaaS offerings quickly. With it, you can build sophisticated SaaS, such as our Inventory Management Application example using a streamlined three-step process.The platform’s composable blueprint model allows for independent development and rapid iteration of each layer. Deployment of these blueprints enables tailored experiences for individual tenants. Finally, SaaS Runtime simplifies operational complexity at scale, offering precise control over version and feature rollouts across diverse tenant populations.
Google Cloud’s SaaS Runtime is available in preview to simplify and automate your SaaS management. Watch the recording of the presentation from Google Cloud Next 25.
Join our launch partners by visiting our product page to learn more or try it out on the Cloud console and accelerate your SaaS transformation today.
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