https://aws.amazon.com/sagemaker/ brings together widely adopted AWS machine learning and analytics capabilities, delivering an integrated experience with unified access to all data. Amazon SageMaker Lakehouse supports unified data access, and Amazon SageMaker Catalog, built on Amazon DataZone, offers catalog and governance features to meet enterprise security needs. Amazon SageMaker Catalog now supports metadata rules, allowing organizations to enforce metadata standards across data publishing and subscription workflows. By standardizing metadata practices, organizations can improve compliance, enhance audit readiness, and streamline access workflows for greater efficiency and control. With metadata rules, domain owners can define mandatory metadata fields that data users must complete when publishing assets to the catalog or requesting access to data. For example, a financial services organization can require producers to classify data before publication, and consumers to provide project details and compliance evidence as part of an access request. Healthcare providers can use metadata rules to enforce metadata standards to align with patient data regulations. Metadata rules also enable the creation of custom approval workflows for subscriptions to assets, using collected metadata to facilitate access decisions or auto-fulfillment—outside of Amazon SageMaker. To get started with metadata rules— Read the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/metadata-rules-publishing.html for creating rules in the publishing workflow Read the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/metadata-rules-subscription.html for creating rules in subscription requests

Amazon DataZone is now available in AWS Asia Pacific (Hong Kong), Asia Pacific (Malaysia) and Europe (Zurich) 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. With Amazon DataZone, data producers populate the business data catalog with structured data assets from AWS Glue Data Catalog and Amazon Redshift tables. Data consumers search and subscribe to data assets in the data catalog and share with other collaborators working on the same business use case. Consumers can analyze their subscribed data assets with tools—such as Amazon Redshift or Amazon Athena query editors—that are directly accessed from the Amazon DataZone portal. The integrated publishing and subscription workflow provides access to auditing capabilities across projects. For more information on AWS Regions where Amazon DataZone is available in preview, see https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/. Additionally, Amazon DataZone powers governance in the next generation of Amazon SageMaker, which simplifies the discovery, governance, and collaboration of data and AI across your lakehouse, AI models, and GenAI applications. With Amazon SageMaker Catalog (built on Amazon DataZone), users can securely discover and access approved data and models using semantic search with generative AI–created metadata, or they could just ask Amazon Q Developer using natural language to find their data. For more information on AWS Regions where the next generation of SageMaker is available, see https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html. To learn more about the next generation of SageMaker, visit the https://aws.amazon.com/sagemaker/.

Amazon DataZone is now available in AWS Asia Pacific (Hong Kong), Asia Pacific (Malaysia) and Europe (Zurich) 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. With Amazon DataZone, data producers populate the business data catalog with structured data assets from AWS Glue Data Catalog and Amazon Redshift tables. Data consumers search and subscribe to data assets in the data catalog and share with other collaborators working on the same business use case. Consumers can analyze their subscribed data assets with tools—such as Amazon Redshift or Amazon Athena query editors—that are directly accessed from the Amazon DataZone portal. The integrated publishing and subscription workflow provides access to auditing capabilities across projects. For more information on AWS Regions where Amazon DataZone is available in preview, see supported regions. Additionally, Amazon DataZone powers governance in the next generation of Amazon SageMaker, which simplifies the discovery, governance, and collaboration of data and AI across your lakehouse, AI models, and GenAI applications. With Amazon SageMaker Catalog (built on Amazon DataZone), users can securely discover and access approved data and models using semantic search with generative AI–created metadata, or they could just ask Amazon Q Developer using natural language to find their data. For more information on AWS Regions where the next generation of SageMaker is available, see supported regions. To learn more about the next generation of SageMaker, visit the product webpage.

Amazon DataZone now supports enforced metadata rules for data access workflows, providing organizations with enhanced capabilities to strengthen governance and compliance with their organization needs. This new feature allows domain owners to define and enforce mandatory metadata requirements, ensuring data consumers provide essential information when requesting access to data assets in Amazon DataZone. By streamlining metadata governance, this capability helps organizations meet compliance standards, maintain audit readiness, and simplify access workflows for greater efficiency and control. With enforced metadata rules, domain owners can establish consistent governance practices across all data subscriptions. For example, financial services organizations can mandate specific compliance-related metadata when data consumers request access to sensitive financial data. Similarly, healthcare providers can enforce metadata requirements to align with regulatory standards for patient data access. This feature simplifies the approval process by guiding data consumers through completing mandatory fields and enabling data owners to make informed decisions, ensuring data access requests meet organizational policies. The feature is supported in https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/?p=ngi&loc=4 where Amazon DataZone is currently available. Check out this https://aws.amazon.com/blogs/big-data/enhance-data-governance-with-enforced-metadata-rules-in-amazon-datazone/ and https://www.youtube.com/watch?v=4zEjwiea45U to learn more about how to set up metadata rules for subscription workflows. Get started with the technical https://docs.aws.amazon.com/datazone/latest/userguide/metadata-rules.html.

AmazonDataZoneが年月日に一般提供されたようなのでまずは学んでみました

データアナリティクス事業本部機械学習チームの鈴木ですreInventのキーノートのうち日本では昨晩行われたAdamSelipskyKeynoteでAmazonDataZone向けに生成系AIベー

はじめにデータアナリティクス事業本部のkobayashiですAmazonDataZoneがGAされRedshiftServerlessへのデータアセット連携設定がDatazoneコンソール画面上で作成することが

Discover more about what's new at AWS with Data Lineage is now generally available in Amazon DataZone and next generation of Amazon SageMaker

はじめに こんにちは一史です。最近自動給水器を買い、ベランダで育てているバジルの水やりを自動化しました。テクノロジーは素晴らしいですね。さて、AWSにはAmazon DataZoneという組織が蓄積した膨大なデータに対して、データの発見、アクセス制御、管理を簡素化するデータ管理サービスがあります。 データドリブンが重要視される昨今、今回はDataZone上にデータカタログの作成を行ってみます。 は...

Today, Amazon DataZone and Amazon SageMaker announced a new user interface (UI) capability allowing a DataZone domain to be upgraded and used directly in the next generation of Amazon SageMaker. This makes the investment customers put into developing Amazon DataZone transferable to Amazon SageMaker. All content created and curated through Amazon DataZone such as assets, metadata forms, glossaries, subscriptions, etc. are available to users through Amazon SageMaker Unified Studio after the upgrade. As an Amazon DataZone administrator, you can choose which of your domains to upgrade to Amazon SageMaker via a UI driven experience. The upgraded domain lets you leverage your existing Amazon DataZone implementation in the new Amazon SageMaker environment and expand to new SQL analytics, data processing and AI uses cases. Additionally, after upgrading both Amazon DataZone and Amazon SageMaker portals remain accessible. This provides administrators flexibility with user rollout of Amazon SageMaker, while ensuring business continuity for users operating within Amazon DataZone. By upgrading to Amazon SageMaker, users can build on their investment from Amazon DataZone by utilizing Amazon SageMaker's unified platform that serves as the central hub for all data, analytics, and AI needs. The domain upgrade capability is available in all https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ where Amazon DataZone and Amazon SageMaker is supported, including: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (Stockholm), Europe (London), South America (São Paulo), Mumbai (BOM), Stockholm (ARN), and Paris (CDG). To learn more, visit https://aws.amazon.com/datazone/ and https://aws.amazon.com/sagemaker/ then get started with the https://docs.aws.amazon.com/datazone/latest/userguide/upgrade-domain.html.

Today, Amazon SageMaker and Amazon DataZone added support for multiple domain features through AWS CloudFormation. Customers can now use AWS CloudFormation to model and manage domain units and their owners. Additionally, customers can set the AWS IAM Identity Center instance for a domain. Programmatically deploying these resources through AWS CloudFormation facilitates secure, efficient, and consistent provisioning of Amazon SageMaker and Amazon DataZone domains. As an Amazon SageMaker or Amazon DataZone administrator, you can now create AWS CloudFormation scripts to assign the domain’s IAM Identity Center instance appropriate for your single sign-on user population. Administrators can then create and manage their domain units and enable users to organize, create, search, and find data assets and projects associated with business units or teams. AWS CloudFormation support for these features is available in all https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ where Amazon SageMaker and Amazon DataZone are available. To learn more, visit https://aws.amazon.com/sagemaker/ and get started with https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/AWS_DataZone.html documentation.

Amazon SageMaker Unified Studio is a single data and AI development platform that brings data together with analytics and AI/ML tools, including Amazon Bedrock and Amazon Q Developer, to streamline analytics and AI application development across virtually any use case.

https://aws.amazon.com/datazone/ is a data management service that makes it faster and easier for customers to catalog, discover, share, and govern data stored across AWS, on premises, and third-party sources. Amazon DataZone now supports metadata rules for data publishing workflows, in addition to existing support for subscription workflows. This enhancement allows organizations to enforce metadata standards consistently across both producer and consumer workflows. By standardizing metadata practices, organizations can improve compliance, enhance audit readiness, and streamline workflows for greater efficiency and control. With metadata rules, domain owners can define mandatory metadata fields that data users must complete when publishing assets to the catalog or requesting access to data. For example, a financial services organization can require producers to classify data before publication, and consumers to provide project details and compliance evidence as part of an access request. Healthcare providers can use metadata rules to enforce metadata standards to align with patient data regulations. Metadata rules also enable the creation of custom approval workflows for subscriptions to assets, using collected metadata to facilitate access decisions or auto-fulfillment—outside of Amazon DataZone. To get started with metadata rules— Read the https://docs.aws.amazon.com/datazone/latest/userguide/metadata-rules-publishing.html for creating rules in the publishing workflow Read the https://docs.aws.amazon.com/datazone/latest/userguide/metadata-rules.html for creating rules in subscription requests

Amazon DataZone is now available in 2 additional commercial regions: Asia Pacific (Mumbai) and Europe (Paris). 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. With Amazon DataZone, data producers populate the business data catalog with structured data assets from AWS Glue Data Catalog and Amazon Redshift tables. Data consumers search and subscribe to data assets in the data catalog and share with other business use case collaborators. Consumers can analyze their subscribed data assets with tools—such as Amazon Redshift or Amazon Athena query editors—that are directly accessed from the Amazon DataZone portal. The integrated publishing-and-subscription workflow provides access-auditing capabilities across projects. For more information on AWS Regions where Amazon DataZone is available in preview, see https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/. Additionally, Amazon DataZone powers governance in the next generation of Amazon SageMaker, which simplifies the discovery, governance, and collaboration for data and AI across your Lakehouse, AI models, and GenAI applications. With Amazon SageMaker Catalog (built on Amazon DataZone) and SageMaker Unified Studio, users can securely discover and access approved data and models using semantic search with generative AI–created metadata, or you could just ask Amazon Q Developer with natural language to find your data. For more information on AWS Regions where the next generation of SageMaker is available, see https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html. To learn more about the next generation of SageMaker, visit the https://aws.amazon.com/sagemaker/.  

Amazon SageMaker Unified Studio is a single data and AI development platform that brings data together with analytics and AI/ML tools, including Amazon Bedrock and Amazon Q Developer, to streamline analytics and AI application development across virtually any use case.

AWS announces general availability of Data Lineage in Amazon DataZone and next generation of Amazon SageMaker, a capability that automatically captures lineage from AWS Glue and Amazon Redshift to visualize lineage events from source to consumption. Being OpenLineage compatible, this feature allows data producers to augment the automated lineage with lineage events captured from OpenLineage-enabled systems or through API, to provide a comprehensive data movement view to data consumers. This feature automates lineage capture of schema and transformations of data assets and columns from AWS Glue, Amazon Redshift, and Spark executions in tools to maintain consistency and reduce errors. With in-built automation, domain administrators and data producers can automate capture and storage of lineage events when data is configured for data sharing in the business data catalog. Data consumers can gain confidence in an asset's origin from the comprehensive view of its lineage while data producers can assess the impact of changes to an asset by understanding its consumption. Additionally, the data lineage feature versions lineage with each event, enabling users to visualize lineage at any point in time or compare transformations across an asset's or job's history. This historical lineage provides a deeper understanding of how data has evolved, essential for troubleshooting, auditing, and validating the integrity of data assets. The data lineage feature is generally available in all https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ where Amazon DataZone and next generation of Amazon SageMaker are available. To learn more, visit https://aws.amazon.com/datazone/features/data-discovery/ and next generation of Amazon SageMaker.  

Unify data engineering, analytics, and generative AI in a streamlined studio with enhanced capabilities of Amazon SageMaker.

Realize visual traceability of data origins, transformations, and usage - bolstering trust, governance, and discoverability for strategic data-driven decisions.

Manage data and AI assets through a unified catalog, granular access controls, and a consistent policy enforcement. Establish trust via automation - boost productivity and innovation for data teams.

https://aws.amazon.com/sagemaker/ml-governance/ now provides a new set up experience for https://aws.amazon.com/datazone/features/integrations/ projects, making it easier for customers to govern access to data and machine learning (ML) assets. With this capability, administrators can now set up Amazon DataZone projects by importing their existing authorized users, security configurations, and policies from Amazon SageMaker domains. Today, Amazon SageMaker customers use domains to organize list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud configurations. With this launch, administrators can now accelerate the process of setting up governance for data and ML assets in Amazon SageMaker. They can import users and configurations from existing SageMaker domains to Amazon DataZone projects, mapping SageMaker users to corresponding Amazon DataZone project members. This enables project members to search, discover, and consume ML and data assets within Amazon SageMaker capabilities such as Studio, Canvas, and notebooks. Also, project members can publish these assets from Amazon SageMaker to the DataZone business catalog, enabling other project members to discover and request access to them. This capability is available in all Amazon Web Services regions where Amazon SageMaker and Amazon DataZone are currently available. To get started, see the https://docs.aws.amazon.com/sagemaker/latest/dg/sm-assets-set-up.html.

Today, Amazon DataZone and Amazon SageMaker announced a new user interface (UI) capability allowing a DataZone domain to be upgraded and used directly in the next generation of Amazon SageMaker. This makes the investment customers put into developing Amazon DataZone transferable to Amazon SageMaker. All content created and curated through Amazon DataZone such as assets, metadata forms, glossaries, subscriptions, etc. are available to users through Amazon SageMaker Unified Studio after the upgrade. As an Amazon DataZone administrator, you can choose which of your domains to upgrade to Amazon SageMaker via a UI driven experience. The upgraded domain lets you leverage your existing Amazon DataZone implementation in the new Amazon SageMaker environment and expand to new SQL analytics, data processing and AI uses cases. Additionally, after upgrading both Amazon DataZone and Amazon SageMaker portals remain accessible. This provides administrators flexibility with user rollout of Amazon SageMaker, while ensuring business continuity for users operating within Amazon DataZone. By upgrading to Amazon SageMaker, users can build on their investment from Amazon DataZone by utilizing Amazon SageMaker's unified platform that serves as the central hub for all data, analytics, and AI needs. The domain upgrade capability is available in all AWS Regions where Amazon DataZone and Amazon SageMaker is supported, including: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (Stockholm), Europe (London), South America (São Paulo), Mumbai (BOM), Stockholm (ARN), and Paris (CDG). To learn more, visit Amazon DataZone and Amazon SageMaker then get started with the upgrade domain documentation.

Today, Amazon SageMaker and Amazon DataZone added support for multiple domain features through AWS CloudFormation. Customers can now use AWS CloudFormation to model and manage domain units and their owners. Additionally, customers can set the AWS IAM Identity Center instance for a domain. Programmatically deploying these resources through AWS CloudFormation facilitates secure, efficient, and consistent provisioning of Amazon SageMaker and Amazon DataZone domains. As an Amazon SageMaker or Amazon DataZone administrator, you can now create AWS CloudFormation scripts to assign the domain’s IAM Identity Center instance appropriate for your single sign-on user population. Administrators can then create and manage their domain units and enable users to organize, create, search, and find data assets and projects associated with business units or teams. AWS CloudFormation support for these features is available in all AWS Regions where Amazon SageMaker and Amazon DataZone are available. To learn more, visit Amazon SageMaker and get started with AWS CloudFormation documentation.

The next generation of SageMaker brings together widely adopted AWS machine learning and analytics capabilities, delivering an integrated experience with unified access to all data. Amazon SageMaker Lakehouse supports unified data access, and Amazon SageMaker Catalog, built on Amazon DataZone, offers catalog and governance features to meet enterprise security needs. Amazon SageMaker Catalog now supports metadata rules, allowing organizations to enforce metadata standards across data publishing and subscription workflows. By standardizing metadata practices, organizations can improve compliance, enhance audit readiness, and streamline access workflows for greater efficiency and control. With metadata rules, domain owners can define mandatory metadata fields that data users must complete when publishing assets to the catalog or requesting access to data. For example, a financial services organization can require producers to classify data before publication, and consumers to provide project details and compliance evidence as part of an access request. Healthcare providers can use metadata rules to enforce metadata standards to align with patient data regulations. Metadata rules also enable the creation of custom approval workflows for subscriptions to assets, using collected metadata to facilitate access decisions or auto-fulfillment—outside of Amazon SageMaker. To get started with metadata rules— Read the user guide for creating rules in the publishing workflow Read the user guide for creating rules in subscription requests

Amazon DataZone is a data management service that makes it faster and easier for customers to catalog, discover, share, and govern data stored across AWS, on premises, and third-party sources. Amazon DataZone now supports metadata rules for data publishing workflows, in addition to existing support for subscription workflows. This enhancement allows organizations to enforce metadata standards consistently across both producer and consumer workflows. By standardizing metadata practices, organizations can improve compliance, enhance audit readiness, and streamline workflows for greater efficiency and control. With metadata rules, domain owners can define mandatory metadata fields that data users must complete when publishing assets to the catalog or requesting access to data. For example, a financial services organization can require producers to classify data before publication, and consumers to provide project details and compliance evidence as part of an access request. Healthcare providers can use metadata rules to enforce metadata standards to align with patient data regulations. Metadata rules also enable the creation of custom approval workflows for subscriptions to assets, using collected metadata to facilitate access decisions or auto-fulfillment—outside of Amazon DataZone. To get started with metadata rules— Read the user guide for creating rules in the publishing workflow Read the user guide for creating rules in subscription requests

Amazon DataZone is now available in 2 additional commercial regions: Asia Pacific (Mumbai) and Europe (Paris). 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. With Amazon DataZone, data producers populate the business data catalog with structured data assets from AWS Glue Data Catalog and Amazon Redshift tables. Data consumers search and subscribe to data assets in the data catalog and share with other business use case collaborators. Consumers can analyze their subscribed data assets with tools—such as Amazon Redshift or Amazon Athena query editors—that are directly accessed from the Amazon DataZone portal. The integrated publishing-and-subscription workflow provides access-auditing capabilities across projects. For more information on AWS Regions where Amazon DataZone is available in preview, see supported regions. Additionally, Amazon DataZone powers governance in the next generation of Amazon SageMaker, which simplifies the discovery, governance, and collaboration for data and AI across your Lakehouse, AI models, and GenAI applications. With Amazon SageMaker Catalog (built on Amazon DataZone) and SageMaker Unified Studio, users can securely discover and access approved data and models using semantic search with generative AI–created metadata, or you could just ask Amazon Q Developer with natural language to find your data. For more information on AWS Regions where the next generation of SageMaker is available, see supported regions. To learn more about the next generation of SageMaker, visit the product webpage.