Release notes for Amazon SageMaker Unified Studio
The following sections describe the feature releases for Amazon SageMaker Unified Studio.
November 2025
November 21, 2025
Introducing one-click onboarding of existing datasets to Amazon SageMaker
Amazon SageMaker Unified Studio now offers one-click onboarding that helps customers
start working with their existing AWS data in minutes. Customers can start directly from
Amazon SageMaker, Amazon Athena, Amazon Redshift, or Amazon S3 Tables, giving them a fast
path from their existing tools and data to the simple experience in SageMaker Unified
Studio. After clicking "Get Started" and specifying an AWS IAM role, SageMaker
automatically creates a project with all existing data permissions intact from AWS Glue
Data Catalog, AWS Lake Formation, and Amazon S3. A notebook and serverless compute are
pre-configured to accelerate first use. For more information, see What's New Post
Announcing notebooks with a built-in AI agent in Amazon SageMaker
The new SageMaker notebooks provide data and AI teams a high-performance, serverless
programming environment for analytics and machine learning jobs. Customers can quickly get
started working with data without pre-provisioning data processing infrastructure. The
notebook gives data engineers, analysts, and data scientists one place to perform SQL
queries, execute Python code, process large-scale data jobs, run machine learning workloads
and create visualizations, without having to switch between tools. It is powered by Amazon
Athena for Apache Spark, automatically scaling from interactive queries to petabyte-scale
processing. A built-in AI agent accelerates development by generating code and SQL
statements from natural language prompts while guiding users through their tasks. For more
information, see What's New Post
Introducing Amazon SageMaker Data Agent for analytics and AI/ML development
SageMaker Data Agent works within new SageMaker notebooks to break down complex
analytics and ML tasks into manageable steps. Customers can describe objectives in natural
language and the agent creates a detailed execution plan and generates the required SQL and
Python code. The agent maintains awareness of the notebook context, including available data
sources, schemas, and catalog information, managing common tasks including data
transformation, statistical analysis, and model development. This helps data engineers,
analysts, and data scientists who spend significant time on manual setup tasks and
boilerplate code build analytics and ML applications faster. For more information, see
What's New Post
Amazon Athena for Apache Spark is now available in Amazon SageMaker notebooks
Amazon SageMaker now supports Amazon Athena for Apache Spark, bringing a new notebook
experience and fast serverless Spark experience together within a unified workspace. Now,
data engineers, analysts, and data scientists can easily query data, run Python code,
develop jobs, train models, visualize data, and work with AI from one place, with no
infrastructure to manage and second-level billing. Athena for Apache Spark scales in seconds
to support any workload, from interactive queries to petabyte-scale jobs. Athena for Apache
Spark now runs on Spark 3.5.6, the same high-performance Spark engine available across
AWS, optimized for open table formats including Apache Iceberg and Delta Lake. It brings
you new debugging features, real-time monitoring in the Spark UI, and secure interactive
cluster communication through Spark Connect. As you use these capabilities to work with your
data, Athena for Spark now enforces table-level access controls defined in AWS Lake
Formation. For more information, see What's New Post
Visual Workflows now supports Amazon MWAA Serverless for IAM-based domains in Amazon SageMaker Unified Studio
Visual Workflows now supports Amazon MWAA Serverless for IAM-based domains in SageMaker
unified studio. Visual Workflows is a drag-and-drop interface for creating and managing
workflows. This low-code feature allows users to visually represent a series of tasks, such
as data processing and analysis, with the option to customize further by switching from
visual to code. Leveraging Amazon Managed Workflows for Apache Airflow (MWAA) Serverless, it
enables users to create, modify, schedule, and monitor workflows without writing code or
managing infrastructure. This addition simplifies workflow management and enhances usability
for data engineers and scientists. For more information, see User Guide, Blog Post
November 14, 2025
Amazon SageMaker Catalog now supports read and write access to Amazon S3
Amazon SageMaker Catalog now supports read and write access to Amazon S3 general purpose
buckets. This capability helps data scientists and analysts search for unstructured data,
process it alongside structured datasets, and share transformed datasets with other teams.
Data publishers gain additional controls to support analytics and generative AI workflows
within SageMaker Unified Studio while maintaining security and governance controls over
shared data. For more information, see What’s New Post
November 10, 2025
Amazon SageMaker Unified Studio adds support for catalog notifications
Amazon SageMaker Unified Studio now provides real-time notifications for data catalog
activities, enabling data teams to stay informed of subscription requests, dataset updates,
and access approvals. With this launch, customers receive real-time notifications for
catalog events including new dataset publications, metadata changes, and access approvals
directly within the SageMaker Unified Studio notification center. This launch streamlines
collaboration by keeping teams updated as datasets are published or modified. For more
information, see What’s New Post
November 6, 2025
Custom tags for Project resources in Amazon SageMaker Unified Studio
Amazon SageMaker Unified Studio announced new capability of adding custom tags to resources created through
the Amazon SageMaker Unified Studio projects. This helps customers enforce tagging standards that conform to
Service Control Policies (SCP) and helps enable cost tracking reporting practices on
resources created across the organization. For more information, see What's New Post
October 2025
October 27, 2025
Amazon SageMaker adds additional search context for search results
Amazon SageMaker enhances search results in Amazon SageMaker Unified Studio with
additional context that improves transparency and interpretability. Users can see which
metadata fields matched their query and understand why each result appears, increasing
clarity and trust in data discovery. The capability introduces inline highlighting for
matched terms and an explanation panel that details where and how each match occurred across
metadata fields such as name, description, glossary, schema, and other metadata. The
enhancement reduces time spent evaluating irrelevant assets by presenting match evidence
directly in search results. Users can quickly validate relevance without opening individual
assets. For more information, see What’s New Post
October 12, 2025
Continuous real-time ingestion of metadata from AWS Glue Data Catalog to SageMaker Catalog
We have launched a new capability within SageMaker Catalog that enables continuous real-time ingestion of metadata from AWS Glue Data Catalog to SageMaker Catalog. With this launch, once you onboard your tables and views in AWS Glue Data Catalog to SageMaker Catalog, SageMaker Catalog continuously keeps metadata current via real-time ingestion. Any changes, such as new tables or schema updates made in AWS Glue Data Catalog, are automatically reflected in the SageMaker Catalog. This eliminates the need for periodic ingestion jobs, reduces stale metadata risk, and lowers operational costs for you while ensuring access to the freshest metadata. For more information, see Onboarding data in Amazon SageMaker Unified Studio.
October 11, 2025
Amazon SageMaker Unified Studio now supports remote connections from Visual Studio Code
Amazon SageMaker Unified Studio now supports remote connections from Visual Studio Code.
Use your familiar VS Code environment with SageMaker's scalable compute resources. This
integration lets you keep your development workflows while accessing AWS analytics and
AI/ML services in a unified environment. For more information, see What's New Post
Command Palette for Amazon SageMaker Unified Studio
TUse the Command Palette to navigate Unified Studio and perform actions with your keyboard. Press Cmd + K (macOS) or Ctrl + K (Windows/Linux) to open the Command Palette. The popup dialog lets you execute commands, navigate through Unified Studio, and discover keyboard shortcuts.
September 2025
Septemeber 18, 2025
Format change for Amazon SageMaker Unified Studio domain
New Amazon SageMaker Unified Studio domain identifiers now contain a hyphen character instead of an underscore. This change aligns with established host naming standards. Existing domains with the underscore character in the domain identifier will continue to work without any changes needed. Domain identifiers for newly created domains will have a hyphen. New format example, dzd-123456789abcde.
September 12, 2025
Continuous real-time ingestion of metadata from AWS Glue Data Catalog to SageMaker Catalog
We have launched a new capability within SageMaker Catalog that enables continuous real-time ingestion of metadata from AWS Glue Data Catalog to SageMaker Catalog. With this launch, once you onboard your tables and views in AWS Glue Data Catalog to SageMaker Catalog, SageMaker Catalog continuously keeps metadata current via real-time ingestion. Any changes, such as new tables or schema updates made in AWS Glue Data Catalog, are automatically reflected in the SageMaker Catalog. This eliminates the need for periodic ingestion jobs, reduces stale metadata risk, and lowers operational costs for you while ensuring access to the freshest metadata. For more information, see Onboarding data in Amazon SageMaker Unified Studio.
Septemeber 08, 2025
Enhanced AI assistance through improved Amazon Q Developer chat and integrations with Amazon Q CLI
Amazon SageMaker Unified Studio now offers enhanced AI assistance through improved
Amazon Q Developer chat and integrations with Amazon Q CLI. By integrating with Model
Context Protocol (MCP) servers, Amazon Q Developer is aware of your SageMaker Unified Studio
project resources, including data, compute, and code, and provides personalized, relevant
responses for data engineering and machine learning development. Users can now receive AI
assistance for tasks such as code refactoring, file modification, and more with full
transparency into the AI's reasoning and actions. These new capabilities are included at no
additional cost with the Amazon Q Developer Free Tier and are available in all AWS Regions
where SageMaker Unified Studio is supported. To make even more use of these features, users
can enable Amazon Q Developer Pro. For more information, see What’s New Post
Custom blueprints in Amazon SageMaker Unified Studio
Custom blueprint capability in Amazon SageMaker Unified Studio, allows customers to use
their own managed policies or roles as per their corporate security policies for creating
the project in SageMaker Unified Studio. Organizations can incorporate their specific
dependencies, security controls using their own managed AWS Identity and Access Management
(https://aws.amazon.com/iam/) (IAM) policies, and best practices, making it straightforward
for them to align with internal standards. Because Custom Blueprints are defined through
infrastructure as code (IaC), they are straightforward to version control, share across
teams, and evolve over time. This speeds up onboarding and keeps projects consistent and
governed, no matter how big or distributed customers’ data organization becomes. For
enterprises, this means more time focusing on insights, models, and innovation. The custom
blueprints feature is designed to help teams move faster and stay consistent while
maintaining their organization’s security controls and best practices. Sample templates can
be found here(https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/custom-blueprints.html).
For more information, see What’s New Post
Introducing restricted classification terms for governed classification in Amazon SageMaker Catalog
AWS announced restricted classification terms in SageMaker Catalog. This new
capability allows domain administrators to define governance-controlled terms and enforce
which teams and users are authorized to apply them. Restricted classification terms are
designed to allow organizations to set standards for consistent classification of sensitive
data, help prevent misuse of regulatory tags, and enable downstream workflows such as
automatic access grants across the enterprise. For more information, see Blog Post
August 2025
August 22, 2025
Amazon SageMaker Unified Studio adds S3 file sharing options to projects experience
Amazon SageMaker Unified Studio now supports Trusted Identity Propagation (TIP) for SQL
analytics use cases with Amazon Redshift and Amazon Athena. This feature enables
administrators to grant permissions based on user attributes from their corporate identity
center. Users can single-sign into databases with permissions based on their identity or
group membership, while auditors can track access across Unified Studio query editor and
services like Redshift, Athena, and Lake Formation. To implement, create or update your
domain with TIP enabled in the project profile. TIP automatically enables for Amazon Athena
workgroup, while Amazon Redshift requires creating a connection with IAM Identity Center
connection type. For more information, see User Guide.For more information, see What's New
August 7, 2025
Amazon SageMaker Unified Studio Redshift Managed Workgroup experience
Amazon SageMaker Unified Studio launched Redshift Managed Workgroup, a collection of Amazon Redshift compute resources that AWS Glue manages. It becomes visible as a managed workgroup in Amazon Redshift when users register a serverless namespace to AWS Glue Data Catalog and create a Lake Formation catalog. All management of these workgroups must be done through the AWS Glue Data Catalog interface. This eliminates the need for dedicated Redshift compute resources when querying Lakehouse catalogs, reducing costs, preserving data continuity, and accelerating project setup time.
August 5, 2025
Project deletion progress experience
Users can now track project deletion progress through a visual interface, providing clear visibility and status updates until the process is complete, enhancing the project management experience.
July 2025
July 16, 2025
Amazon SageMaker streamlines the S3 Tables workflow experience
This update simplifies table creation and management by eliminating the need to navigate
multiple AWS consoles. Users can now create tables, load data, and run queries directly
within Amazon SageMaker Unified Studio using either Query Editor or Jupyter Notebook. The update enables
administrators to configure analytics integration with Amazon S3 and create custom profiles,
allowing project owners to set up pre-configured catalogs and S3 Tables support more
efficiently. For more information, see What's New
July 15, 2025
Automatic approval of subscription requests based on project membership
A subscription request is now automatically approved if the requester is already authorized to approve it manually. Specifically, if they are a member of both the project that published the asset and the project requesting access. To qualify for auto-approval, the requester must be listed as an owner or contributor in the project where the asset was originally published. And the requester must also be listed as an owner or contributor in the project making the subscription request. For more information, see User Guide.
July 16, 2025
Data lineage enhancements
Amazon SageMaker Unified Studio has now contributed a 'custom transport' to the OpenLineage community that
allows builders to download the transport along with OpenLineage plugins to augment and
automate lineage events captured from OpenLineage-enabled systems. With this, customers can
automate lineage capture and send these lineage events to the Amazon SageMaker unified
domain, enhancing data governance and traceability within their data workflows. Additionally
lineage enhancements include automated lineage events capture for additional data sources
using AWS Glue crawlers, improved its SQL lineage support for stored procedures and
materialized views in Amazon Redshift, and from tools such as vETL processes and notebooks
(interactive executions and remote workflows). For more information, see What's New
July 15, 2025
Amazon QuickSight Integration
Amazon QuickSight Integration is now available, enabling users to seamlessly build and
visualize dashboards using project data directly from Amazon SageMaker Unified Studio. The integration
automatically creates secured QuickSight datasets, organizes dashboards within project
folders, and allows publishing to the SageMaker Catalog for broader discovery and reuse.
This keeps your dashboards organized, discoverable, shareable, and governed, making
cross-team collaboration and asset reuse much easier. For more information, see What's New
July 15, 2025
Data management with automated Lakehouse onboarding and metadata ingestion
Data management is simplified with the introduction of two key capabilities in Amazon
SageMaker. First, automated metadata ingestion for datasets into SageMaker Catalog during
domain creation or updates, and direct sharing of assets between projects. The automated
onboarding eliminates manual IAM configuration and metadata ingestion tasks, making datasets
immediately available for governance and analysis. Second, direct sharing enables seamless
cross-team collaboration while maintaining governance controls, reducing administrative
overhead and accelerating project timelines. For more information, see What's New
July 15, 2025
Amazon SageMaker Catalog adds support for Amazon S3 general purpose buckets
Amazon SageMaker Catalog now supports Amazon S3 general purpose buckets to enable data
producers to share unstructured data as "S3 Object Collections" within the SageMaker
Catalog. Users can curate these assets with business metadata, making them discoverable and
accessible to data scientists, engineers, and analysts while maintaining granular security
controls. This feature streamlines cross-team data sharing, enhances data governance, and
allows consumers to subscribe to assets for ongoing access and updates. For more
information, see What's New
July 15, 2025
Unified Metadata Governance with SageMaker-Collibra solution
Unified Metadata Governance with SageMaker-Collibra solution addresses organizational
challenges by connecting Amazon SageMaker Catalog with Collibra, enabling centralized
metadata management and streamlined access workflows. This integration reduces metadata
duplication, enhances governance controls, and builds data trust across analytics and AI
platforms. For more information, see AWS Big Data Blog
July 15, 2025
Visual Workflows
Visual Workflows introduces a drag-and-drop interface for creating and managing
workflows within Amazon SageMaker Unified Studio. This low-code feature allows users to visually represent a
series of tasks, such as data processing and analysis, with the option to customize further
by switching from visual to code. Leveraging Amazon Managed Workflows for Apache Airflow
(MWAA), it enables users to create, modify, schedule, and monitor workflows easily. This
addition simplifies workflow management and enhances usability for data engineers and
scientists. For more information, see What's New
July 15, 2025
Data processing jobs experience
A new data processing jobs experience is available within Amazon SageMaker Unified Studio. Jobs enables users
to author, manage, monitor and troubleshoot data processing workloads across their
organization and collaborate in projects to securely build and share data processing jobs
and workflows. Users can create jobs through multiple methods including ETL scripts,
interactive notebooks, or the Visual ETL editor, with options for on-demand execution,
scheduling, or workflow orchestration. The feature includes monitoring capabilities and
AI-powered troubleshooting for failed jobs. For more information, see What's New
July 3, 2025
Enhanced Data Explorer object actions
Data Explorer is being enhanced with user-friendly actions that streamline database operations, including a UI-based table definition creator, auto-generation of common SQL queries, a quick actions menu, and direct object manipulation capabilities. These features reduce the need for manual query writing and boost overall productivity.
July 3, 2025
Bring S3 buckets to your project
To bring in Amazon S3 data either in the same account or a different account to your project, you must first gain access to the data and then add the data to your project. You can gain access to the data by using the project role or an access role. Then you have to create a S3 connection using the Add Data option from data explorer. For more information, see User Guide.
July 3, 2025
Uploading data from your desktop to create a Redshift table
You can now upload data from your desktop in CSV, JSON, Parquet, or Delimiter formats to create either a data lake or Amazon Redshift table using Amazon SageMaker Unified Studio. You can select Add data option from the data explorer and then select create table. For more information, see User Guide.
July 25, 2025
Text search capability of Query history experience
Amazon SageMaker Unified Studio introduces text search capability for query history in both Athena and Redshift engines. This enhancement enables users to efficiently search through their historical queries, improving workflow productivity and query reusability. For more information, see User Guide.
July 23, 2025
Trusted identity propagation experience
Amazon SageMaker Unified Studio now supports Trusted Identity Propagation (TIP) for SQL analytics use cases with Amazon Redshift and Amazon Athena. This feature enables administrators to grant permissions based on user attributes from their corporate identity center. Users can single-sign into databases with permissions based on their identity or group membership, while auditors can track access across Unified Studio query editor and services like Redshift, Athena, and Lake Formation. To implement, create or update your domain with TIP enabled in the project profile. TIP automatically enables for Amazon Athena workgroup, while Amazon Redshift requires creating a connection with IAM Identity Center connection type. For more information, see User Guide.For more information, see User Guide and Admin Guide.
July 2, 2025
Support of SQL generation actions in Data Explorer experience
Amazon SageMaker Unified Studio now supports SQL generation actions in Data Explorer, introducing automated generation of table definitions and common SQL queries (INSERT/UPDATE/SELECT/DROP). The feature includes a quick actions menu for frequently used operations and direct object manipulation capabilities. These enhancements significantly reduce manual query writing effort and improve overall productivity for data analysts and scientists.
June 2025 (Additional)
June 30, 2025
Next Generation Amazon SageMaker user guide
Next Generation Amazon SageMaker user guide was launched on 6/30 and it introduces the main components of Next Generation Amazon SageMaker and includes 7 new use case-based tutorials to help customers get started. For more information, see Next Generation Amazon SageMaker user guide.
June 25, 2025
Automatic file synchronization between project Git repositories and Amazon S3 buckets
Amazon SageMaker Unified Studio now offers automatic file synchronization between project Git repositories and
Amazon S3 buckets. This new capability streamlines data and AI development workflows by
ensuring your Git repository files are automatically mirrored to S3. For more information,
see What's New
June 2025
June 12, 2025
Workflow Improvements
Enhanced default workflow template: The default start_date now uses a static date instead of a relative date. This prevents potential workflow trigger failures, improves workflow reliability and predictability.
Notebook output capabilities: Download notebook output directly, export results for offline analysis and share findings more easily with your team.
June 19, 2025
Auto complete support for large number of tables and columns experience
Amazon SageMaker Unified Studio now delivers enhanced autocomplete capabilities for enterprise-scale Amazon Redshift databases. The feature supports seamless autocomplete functionality for massive databases containing up to 200,000 tables, while eliminating previous restrictions on the number of schemas or columns that can be indexed and suggested. Using real-time database metadata, it provides comprehensive coverage across your entire Redshift environment, including both user-defined native tables and critical system tables. This enables data analysts, engineers, and scientists working with complex, large-scale data warehouses to navigate their database structures effortlessly, dramatically reducing query development time and minimizing syntax errors.
June 5, 2025
CloudFormation template
Amazon SageMaker Unified Studio now provides an CloudFormation template
June 2, 2025
Amazon DataZone and Amazon SageMaker integration
Amazon DataZone and Amazon SageMaker introduced a new UI feature that enables DataZone
domains to be upgraded and utilized within the next generation of Amazon SageMaker. This
update preserves customers' Amazon DataZone investments when transferring to Amazon
SageMaker. The integration allows all content and assets created in DataZone, including
metadata forms, glossaries, and subscriptions, to remain accessible through Amazon SageMaker Unified Studio
post-upgrade. This upgrade pathway demonstrates Amazon's commitment to providing continuity
and value preservation for customers using their machine learning and data management
services. For more information, see What's New
May 2025
May 30, 2025
Increased concurrent spaces support
Amazon SageMaker Unified Studio now supports up to 6,000 concurrent spaces per customer account, representing a 2.4x increase from the previous limit. This significant scaling improvement enables organizations to support larger teams and more concurrent workloads within their Amazon SageMaker Unified Studio environment.
May 30, 2025
Visual ETL improvements
Amazon SageMaker Unified Studio visual ETL now supports cloning a visual ETL flow as a Jupyter notebook, allowing users to continue editing the flow with code. This streamlines the workflow for users who prefer to start authoring ETL pipelines visually and then transition to a code-based approach. Visual ETL also added support for automatically inferring schemas from CSV files within S3 nodes, improving the authoring process and reducing manual work needed.
May 29, 2025
Native Redshift table creation enhancements
You can now create native Redshift tables directly through local file uploads, supporting multiple file formats (CSV, JSON, PARQUET) with various compression options and increased file size limit to 100MB. The enhancement includes automatic schema inference with preview capabilities and adds PARQUET support in Glue upload flow. This eliminates the previous multi-step process that required manual S3 file copying, table creation, and data insertion, making the workflow more efficient and less error-prone.
May 23, 2025
Customer Managed Custom Master (CM-CMK) support for Persistent App UI
Customer Managed Custom Master (CM-CMK) support for Persistent App UI, an essential tool for debugging big data applications. This feature extends SSE encryption applied to logs managed in the EMR service bucket to honor Customer Managed Custom Master Key (CM-CMK). This feature enables customers to manage accessibility of logs after their clusters terminate. For more information, see About Amazon EMR Releases and Configure Amazon EMR cluster logging and debugging.
May 22, 2025
Global Search Bar
Amazon SageMaker Unified Studio introduced a new Global Search Bar, providing a persistent search interface in the top navigation and landing page, simplifying discover and access data without navigating through multiple menus. The intuitive search functionality currently supports business catalog data and projects, offering clear visual indicators for search result scope. This user-centric enhancement represents AWS's commitment to improving the overall SageMaker Studio experience, making data and resources more accessible to users.
May 12, 2025
Code Editor and Multiple Spaces support
AWS launched two complementary capabilities in the next generation of Amazon SageMaker
that enhance the development experience for analytics, machine learning (ML), and GenAI
teams: Code Editor and Multiple Spaces support. The Code Editor, based on Code-OSS (Open
Source Software) like VS Code, offers a powerful IDE experience with familiar shortcuts,
terminal access, and advanced development tools, while supporting thousands of VS
Code-compatible extensions from Open VSX. It enables seamless version control through major
Git platforms and comes preconfigured with Amazon SageMaker distribution for ML frameworks.
To maximize the benefits of Code Editor alongside other coding interfaces in Unified Studio,
including JupyterLab, SageMaker now supports multiple spaces per user per project, allowing
users to manage parallel workstreams with different computational needs. For more
information, see What's New
May 12, 2025
Bring Your Own Image (BYOI)
Amazon SageMaker Unified Studio now allows you to bring your own image (BYOI). This feature benefits customers
who have regulatory and compliance requirements or who prefer not to use the framework
containers that come with the default SageMaker Distribution image. For more information,
see What's New
