# Release notes for Amazon SageMaker Unified Studio
The following sections describe the feature releases for Amazon SageMaker Unified Studio.
## April 2026
### April 23, 2026
**Amazon SageMaker supports notebooks and data agent for IdC domains**
Amazon SageMaker Unified Studio now supports serverless notebooks with a built-in data agent for AWS IAM Identity Center (IdC) domains. Previously, the notebook experience and data agent were available only in IAM domains. With this launch, customers who use IdC for authentication and access management can access the high-performance, serverless notebook environment for analytics and machine learning (ML) workloads.
The serverless notebook gives data engineers, analysts, and data scientists one place to perform SQL queries, execute Python code, process large-scale data jobs, run ML workloads, and create visualizations. A built-in AI data agent accelerates development by generating code and SQL statements from natural language prompts and guides users through their tasks. Customers can flexibly combine SQL, Python, and natural language within a single interactive workspace, removing the need to switch between different tools based on the workload. For example, you can start with SQL queries to explore your data, use Python for advanced analytics or to build ML models, or use natural language prompts to generate code automatically. The notebook is backed by Amazon Athena for Apache Spark, scaling from interactive SQL queries to petabyte-scale data processing.
For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/04/amazon-sagemaker-idc/).
**Amazon SageMaker Unified Studio now supports VPC for notebook kernels**
Amazon SageMaker Unified Studio now supports Amazon Virtual Private Cloud (Amazon VPC) for notebook kernels. With this launch, notebook kernels execute within the VPC configured at the domain level, giving enterprises network isolation for interactive data and machine learning (ML) workloads. This helps customers meet security and compliance requirements by keeping applicable notebook compute traffic within their VPC boundaries.
With VPC support for notebook kernels, data engineers, analysts, and data scientists can connect to private resources from their notebooks. The notebook kernel inherits the VPC settings, subnets, and security groups defined at the SageMaker Unified Studio domain level, so administrators can manage network policies centrally. This means you can query private databases, access internal APIs, and work with data sources that are not publicly accessible, all from the same notebook environment that supports SQL, Python, and natural language through the built-in data agent. This VPC configuration only applies to the notebook's interactive compute, where your Python code and dataframes execute. For VPC configurations with other compute engines, refer to the documentation for each individual engine.
For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/04/sagemaker-unified-studio-vpc/).
**Amazon SageMaker Unified Studio now offers CI/CD CLI for data and AI applications**
Amazon SageMaker Unified Studio now offers the CI/CD CLI (aws-smus-cicd-cli), an open-source command line tool that automates deployment of multi-service data and AI applications across development, test, and production. Organizations building applications in SageMaker Unified Studio combine multiple AWS services, including AWS Glue, Amazon Athena, Amazon MWAA, Amazon SageMaker AI, Amazon Bedrock, and Amazon QuickSight, into single applications. The CLI allows data teams to define applications once in a YAML manifest while DevOps teams deploy with a single command, reducing deployment bottlenecks and configuration drift.
The CLI reads a declarative manifest.yaml that maps each pipeline stage to an isolated SageMaker Unified Studio project. At deploy time, it substitutes stage-specific configurations (S3 paths, IAM roles, account IDs, and connection strings) and provisions resources in dependency order. Four commands cover the lifecycle: describe validates permissions and connections, bundle packages an immutable artifact from the source target, deploy writes that artifact to the destination target, and test runs post-deployment validation. It works with existing CI/CD solutions such as GitHub Actions, Jenkins, and GitLab CI. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/04/amazon-smus-ci-cd-cli/).
### April 22, 2026
**Amazon SageMaker Unified Studio now supports multiple code spaces within projects for IAM domains**
Amazon SageMaker Unified Studio now lets data workers create and manage multiple code spaces (individually configured development environments) within a single project for IAM domains. Previously, projects were limited to one JupyterLab space and one Code Editor space embedded in the project. With this launch, you can now parallelly work on different workstreams or experiments with different compute and storage configuration needs, giving developers the flexibility they need as their workloads scale. For instance, data scientists can now work in parallel on any long running data transformation and model training workloads within the same project using separate spaces.
With multiple spaces, each one maintains its own persistent Amazon EBS volume, ensuring that your files, data, and session state are preserved independently. You can scale compute and storage up or down per space, pause and resume them at any time, and customize the runtime environment for each specific task. Spaces can either be opened in dedicated browser tabs or connected to a local IDE if you prefer your own development environment, with full functionality including Amazon Q paid tier support. This is particularly beneficial for builders who need isolated environments for parallel workstreams while still working within a single collaborative project. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/04/sagemaker-code-spaces/).
### April 21, 2026
**Amazon SageMaker now supports multi-region replication from IAM Identity Center**
Amazon SageMaker now supports multi-region replication from IAM Identity Center (IdC), enabling you to deploy SageMaker Unified Studio domains in different regions from your IdC instance. This new capability empowers enterprise customers, particularly those in regulated industries like financial services and healthcare, to maintain compliance while leveraging centralized workforce identity management.
As an Amazon SageMaker Unified Studio administrator, you can deploy SageMaker domains closer to your workforce based on data residency needs while maintaining seamless single sign-on (SSO) access. Organizations can address use cases such as maintaining IdC in one region while processing sensitive data in compliance-required regions, supporting global operations with centralized identity management, and meeting data sovereignty requirements without compromising SSO capabilities. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/04/smus-identity-center/).
### April 7, 2026
**Amazon SageMaker adds serverless workflows to Identity Center domains**
Amazon SageMaker Unified Studio now supports Serverless Workflows in Identity Center domains. With this launch, customers using Identity Center domains can orchestrate data processing tasks with Apache Airflow (powered by Managed Workflows for Apache Airflow ) without provisioning or managing Airflow infrastructure. Serverless Workflows were previously available only in IAM-based domains.
Serverless Workflows automatically provision compute resources when a workflow runs and release them when it completes, so you only pay for actual workflow run time. Each workflow runs with its own execution role and isolated worker, providing workflow-level security and preventing cross-workflow interference. With Serverless Workflows, Identity Center domain customers also get access to the Visual Workflow experience with support for around 200 operators, including built-in integration with AWS services such as Amazon S3, Amazon Redshift, Amazon EMR, AWS Glue, and Amazon SageMaker AI.
For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/04/amazon-sagemaker-serverless-workflows/).
### April 6, 2026
**Amazon SageMaker Unified Studio adds notebook import/export and developer acceleration features**
Amazon SageMaker Unified Studio notebooks now support import/export capabilities, enabling migration from JupyterLab and other notebook platforms. This release also introduces developer acceleration features including cell reordering, keyboard shortcuts, cell renaming, and multi-line SQL support, designed to enhance productivity for data engineers and data scientists professionals working with notebook-based workflows.
The new import/export functionality supports .ipynb, .json, and .py formats while preserving cell types, metadata, and outputs, making platform migration straightforward. You can export notebooks in four formats including Jupyter notebook with requirements (.zip), standard .ipynb, Python scripts (.py), and SageMaker Unified Studio native format (.json). Developer acceleration features enable you to reorder cells without copy-paste duplication, assign custom names to cells for improved navigation in large notebooks, use familiar keyboard shortcuts for faster development, and execute multiple SQL statements in a single cell with results displayed in separate tabs for easy comparison and analysis. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/04/amazon-sagemaker-unified-studio/).
### April 3, 2026
**Amazon SageMaker Data Agent introduces charting capabilities and support for materialized views**
Amazon SageMaker Data Agent now supports interactive charting, SQL analytics on Snowflake data sources, and materialized view management in Amazon SageMaker Unified Studio notebooks. Data Agent now provides a complete analytics workflow that goes beyond code generation, enabling you to explore AWS and external data sources, visualize results, and optimize query performance, all with natural language prompts.
You can ask "plot monthly revenue trends by region for 2025" and Data Agent generates an interactive chart directly in your notebook, where you can hover over data points, and modify without writing code. When your analysis spans AWS and Snowflake, you can query Snowflake tables through external connections and join them with your AWS Glue Data Catalog data in a single prompt. Additionally, you can ask "analyze my notebook and suggest which queries would benefit from materialized views" and the agent recommends optimizations based on your query patterns, creates the views, and sets refresh schedules. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/03/amazon-sgmkr-dataagent-chart-mv/).
### April 1, 2026
**Amazon SageMaker Data Agent now supports geo-specific inference for Japan and Australia**
Amazon SageMaker Data Agent now supports cross-region inference profiles for Japan and Australia through Amazon Bedrock. With this update, inference requests from Data Agent in the Asia Pacific (Tokyo) and Asia Pacific (Sydney) regions are processed within their respective geographies, supporting data sovereignty requirements for customers in Japan and Australia.
Data Agent provides an AI-powered conversational experience for data exploration, Python and SQL code generation, troubleshooting, and analytics directly within Amazon SageMaker Unified Studio Notebook and Query Editor. With geo-specific inference through JP-CRIS (Japan Cross-Region Inference) and AU-CRIS (Australia Cross-Region Inference), you can use Data Agent with confidence that your inference requests are routed exclusively within your geography over the AWS Global Network. Customers in regulated industries such as financial services, healthcare, and the public sector can meet data residency requirements while using the full set of Data Agent capabilities. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/03/sage-maker-da-infr-jp-au/).
## March 2026
### March 31, 2026
**Amazon SageMaker Unified Studio adds Observability for AWS Glue jobs via CloudWatch metrics**
Amazon SageMaker Unified Studio adds Observability for jobs, it now displays Amazon CloudWatch metrics for AWS Glue jobs directly alongside job logs in a single, unified interface. This enhancement adds observability to SageMaker Unified Studio, enabling data engineers and ETL developers to streamline their troubleshooting processes.
With this feature, teams can diagnose performance issues faster by correlating resource utilization patterns—including DPU utilization, memory consumption, CPU load, and data movement size—directly with job log output. Specific use cases include identifying compute bottlenecks, detecting memory pressure or out-of-memory conditions, optimizing resource allocation, and monitoring data pipeline performance at scale. By consolidating metrics and logs into one workspace, organizations can significantly reduce mean time to resolution (MTTR) for ETL pipeline issues and improve overall operational efficiency. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/03/sagemaker-unified-studio-metrics/).
### March 30, 2026
**Amazon SageMaker Data Agent is now available in the Amazon SageMaker Unified Studio Query Editor**
Amazon SageMaker Data Agent is now available in the Query Editor in Amazon SageMaker Unified Studio, extending beyond notebook experience. With Data Agent in Query Editor, you can generate SQL queries from natural language, debug failed queries, and explore your data through a conversational, interactive experience.
Data Agent brings the same conversational experience available in notebooks to your SQL analytics workflow. You can ask "calculate quarterly revenue growth rate by product category for 2025," and the agent proposes a step-by-step plan for you to review before generating contextually accurate SQL for Amazon Redshift and Amazon Athena. This helps you build analytics queries faster, going from question to executable SQL without writing complex joins and aggregations manually. When a query fails, you can use Fix with AI to analyze the error and get suggested corrections. Data Agent maintains awareness of your connected data sources and schema information, so follow-up questions and modifications build on your previous context. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/03/amazon-sagemaker-data-agent-query-editor/).
### March 25, 2026
**Amazon SageMaker Unified Studio launches support for remote connection from Cursor IDE**
Today, AWS announces remote connection from Cursor IDE to Amazon SageMaker Unified Studio via the AWS Toolkit extension. This new capability allows data scientists, ML engineers, and developers to leverage their Cursor setup - including its AI-powered code completion, natural language editing, and multi-file editing capabilities - while accessing the scalable compute resources of Amazon SageMaker. By connecting Cursor to SageMaker Unified Studio using the AWS Toolkit extension, you can eliminate context switching between your local IDE and cloud infrastructure, maintaining your existing AI-assisted development workflows within a single environment for all your AWS analytics and AI/ML services. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/03/sagemaker-unified-studio-cursor-ide/).
### March 18, 2026
**Amazon SageMaker Unified Studio adds custom metadata filters**
Amazon SageMaker Unified Studio adds custom metadata search filters, enabling customers to narrow catalog search results using organization-specific attributes. This helps customers find the right assets faster by filtering on fields like business region, data classification, or study name, in addition to existing keyword and semantic search.
With custom metadata search filters, customers can add filters based on any custom metadata fields available in their catalog, such as sample type or study ID. Filters support string fields with a "contains" operator and numeric fields (Integer, Long) with equals, greater than, and less than operators. Customers can also filter by asset name, description, and date range. Multiple filters can be combined, and filter selections persist across browser sessions.
For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/03/amazon-smus-custom-metadata-search/).
### March 17, 2026
**Amazon SageMaker Unified Studio supports aggregated view of data lineage**
Amazon SageMaker Unified Studio now provides an aggregated view of data lineage, displaying all jobs contributing to your dataset. The aggregated view gives you a complete picture of data transformations and dependencies across your entire lineage graph, helping you quickly identify all upstream sources and downstream consumers of your datasets.
Previously, SageMaker Unified Studio showed the lineage graph as it existed at a specific point in time, which is useful for troubleshooting and investigating specific data processing events. The aggregated view now provides a complete picture of data transformations and dependencies across multiple levels of the lineage graph. You can use this view to understand the full scope of jobs impacting your datasets and to identify all upstream sources and downstream consumers.
The aggregated view is available as the default lineage view in Amazon SageMaker Unified Studio for IdC-based domains. You can switch to the previous view by toggling the "display in event timestamp order" option. You can also query the lineage graph using the new QueryGraph API, which provides lineage node graphs with metadata and augmented business context.
For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/03/amazon-sagemaker-unified-studio-aggregated-lineage/).
### March 9, 2026
**Amazon SageMaker Unified Studio now supports faster data preview in Visual ETL**
Amazon SageMaker Unified Studio introduces data preview v2.0 for Visual ETL, a new data preview mode that delivers near-instant results when building and iterating on visual ETL jobs. With data preview v2.0, data engineers and analysts can see the output of each transform in about one second, with no session startup required and at no additional compute cost.
Data preview v2.0 uses an in-browser query engine to load and process data locally, removing the dependency on server-side Spark sessions for preview operations. Source data is fetched once and cached in the browser, so subsequent transforms apply instantly without re-querying the underlying data source. For Amazon Redshift users, this means you can iterate on transforms without additional queries against your Redshift cluster, keeping your preview workflow fast and your cluster resources focused on production workloads. Data preview v2.0 supports CSV, Parquet, and JSON files from Amazon S3, in addition to data from Amazon Redshift, Amazon S3 Tables, AWS Glue Data Catalog, and third-party sources including Snowflake, MySQL, PostgreSQL, SQL Server, Oracle, Google BigQuery, Amazon DynamoDB, and Amazon DocumentDB. A toggle in the Visual ETL editor gives you the option to switch between data preview v2.0 and the original Spark-based preview at any time. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/03/amazon-sagemaker-unified-studio-faster-data-preview/).
### March 5, 2026
**Amazon SageMaker Unified Studio adds light mode support for IAM-based domains**
Today, AWS announces light mode support in Amazon SageMaker Unified Studio for IAM-based domains. Customers can now configure the visual interface mode to match their preference, choosing between dark and light themes.
Light mode helps improve readability in bright environments and provides a familiar visual experience for customers who prefer lighter interfaces. Combined with the existing dark mode, this update gives you full control over your development environment's appearance, improving accessibility and reducing eye strain across varying lighting conditions. In SageMaker Unified Studio settings, you can click on 'customize appearance' under your Profile settings to choose between visual modes including dark and light. The setting persists across browsers and devices. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/03/sagemaker-unified-studio-light-mode/).
### March 3, 2026
**Amazon SageMaker Unified Studio adds metadata sync with third-party catalogs**
Amazon SageMaker Unified Studio now supports metadata and context sync across Atlan, Collibra, and Alation. These integrations synchronize catalog metadata between Amazon SageMaker Catalog and each partner platform, giving teams a consistent view of their data and AI assets regardless of which tool they use day to day. Organizations can maintain aligned glossary terms, asset descriptions, and ownership information across platforms without manual reconciliation.
All three integrations synchronize key metadata elements including projects, assets, descriptions, glossary terms, and their hierarchies. With the Collibra integration, you can synchronize metadata in both directions between SageMaker Catalog and the partner platform, so updates you make in one are reflected in the other. Also, you can manage SageMaker Unified Studio data access requests from Collibra. With the Atlan and Alation integration, you can ingest metadata from SageMaker Catalog into Alation with additional enhancements coming soon. You set up these integrations by setting up a connection to SageMaker Unified Studio from within Atlan and Alation, while the Collibra integration is available as an open-source solution on GitHub. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/03/amazon-sagemaker-unified-studio-3p-catalogs/).
**Amazon SageMaker Unified Studio now supports AWS Glue 5.1 for data processing jobs**
Amazon SageMaker Unified Studio now supports AWS Glue 5.1 for Visual ETL, notebook, and code-based data processing jobs. With AWS Glue 5.1 in Amazon SageMaker Unified Studio, data engineers and data scientists can run jobs on Apache Spark 3.5.6 with Python 3.11 and Scala 2.12.18, and use updated open table format libraries including Apache Iceberg 1.10.0, Apache Hudi 1.0.2, and Delta Lake 3.3.2.
You can use AWS Glue 5.1 in Amazon SageMaker Unified Studio when creating data processing jobs by selecting Glue 5.1 from the version dropdown in job settings. This applies to Visual ETL jobs, notebook jobs, and code-based jobs, so you can take advantage of the latest Spark runtime and open table format libraries across all your data processing workflows.
For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/03/amazon-sagemaker-unified-studio-aws-glue-5-1/).
**Amazon SageMaker Unified Studio launches support for remote connection from Kiro IDE**
Today, AWS announces the ability to remotely connect from Kiro IDE to Amazon SageMaker Unified Studio. This new capability allows data scientists, ML engineers, and developers to leverage their Kiro setup - including its spec-driven development, conversational coding, and automated feature generation capabilities - while accessing the scalable compute resources of Amazon SageMaker. By connecting Kiro to SageMaker Unified Studio using the AWS toolkit extension, you can eliminate context switching between your local IDE and cloud infrastructure, maintaining your existing agentic development workflows within a single environment for all your AWS analytics and AI/ML services. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/03/amazon-sagemaker-unified-studio-kiro-ide/).
## February 2026
### February 4, 2026
**Apache Spark lineage now available in Amazon SageMaker Unified Studio for IDC based domains**
Amazon SageMaker announces general availability of Data Lineage for Apache Spark jobs executed on Amazon EMR and AWS Glue in SageMaker Unified Studio for IDC based domains. Data Lineage provides you with the information you need to identify the root cause of complex issues and understand the impact of changes.
This feature supports lineage capture of schema and transformations of data assets and columns from Spark executions in EMR-EC2, EMR-Serverless, EMR-EKS, and AWS Glue. You can then explore this lineage visually as a graph in SageMaker Unified Studio or query it using APIs. You can also use lineage to compare transformations across Spark job's history.
For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/02/apache-spark-lineage-amazon-sageMaker-unified-studio).
## January 2026
### January 30, 2026
**Amazon SageMaker Unified Studio now supports AWS PrivateLink**
Today, Amazon SageMaker announced a new capability allowing you to establish connectivity between your Amazon Virtual Private Cloud (VPC) and Amazon SageMaker Unified Studio without customer data traffic going through the public internet. Customers needing to go beyond the standard data transfer protocol (HTTPS/TLS2) can choose to configure their VPC so data transfer stays within the AWS network.
Through AWS PrivateLink , Network Administrators can now onboard AWS service endpoints to their VPC used by Amazon SageMaker Unified Studio. With the endpoints are onboarded, IAM policies used by Amazon SageMaker will enforce that customer data stay within the AWS network. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/01/amazon-sagemaker-unified-studio-aws-privatelink/).
### January 20, 2026
**SageMaker Unified Studio adds cross-Region subscriptions support for IDC based domains**
Amazon SageMaker Unified Studio now supports cross-Region subscriptions for comprehensive and flexible data access and governance. With cross-Region support, you can subscribe to AWS Glue tables and views, as well as Amazon Redshift tables and views published in a different AWS Region than your project. This capability helps break down data silos and enable better collaboration across your organization by allowing teams to access curated data assets from any AWS Region without manual replication. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2026/01/sagemaker-unified-studio-adds-cross-region-iam/).
## December 2025
### December 2, 2025
**Amazon SageMaker Catalog now exports asset metadata as queryable dataset**
Amazon SageMaker Catalog now exports asset metadata as an Apache Iceberg table through Amazon S3 Tables. This allows data teams to query catalog inventory and answer questions such as, "How many assets were registered last month?", "Which assets are classified as confidential?", or "Which assets lack business descriptions?" using standard SQL without building custom ETL infrastructure for reporting.
This capability automatically converts catalog asset metadata into a queryable table accessible from Amazon Athena, SageMaker Unified Studio notebooks, AI agents, and other analytics and BI tools. The exported table includes technical metadata (such as resource\_id, resource\_type), business metadata (such as asset\_name, business\_description), ownership details, and timestamps. Data is partitioned by snapshot\_date for time travel queries and automatically appears in SageMaker Unified Studio under the aws-sagemaker-catalog bucket. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2025/12/sagemaker-catalog-asset-metadata-queryable-dataset/).
## 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](https://aws.amazon.com/about-aws/whats-new/2025/11/amazon-sagemaker-one-click-onboarding-existing-datasets/).
**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](https://aws.amazon.com/about-aws/whats-new/2025/11/notebooks-built-in-ai-agent-amazon-sagemaker/).
**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](https://aws.amazon.com/about-aws/whats-new/2025/11/amazon-sagemaker-data-agent-analytics-ai-ml-development/).
**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](https://aws.amazon.com/about-aws/whats-new/2025/11/amazon-athena-apache-spark-sagemaker-notebooks/).
**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](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/workflows-iam-domains.html)), [Blog Post](https://aws.amazon.com/blogs/big-data/orchestrating-data-processing-tasks-with-a-serverless-visual-workflow-in-amazon-sagemaker-unified-studio/))), and [IAM-based domains and projects](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/iam-based-domains.html).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/11/amazon-sagemaker-catalog-read-write-access-amazon-s3/).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/11/amazon-sagemaker-unified-studio-catalog-notifications/).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/11/amazon-sagemaker-custom-tags-project-resources/) and [or the API Reference documentation](https://docs.aws.amazon.com/datazone/latest/APIReference/Welcome.html/).
## 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](https://aws.amazon.com/blogs/big-data/enhanced-search-with-match-highlights-and-explanations-in-amazon-sagemaker/) and [AWS Big Data Blog](https://aws.amazon.com/blogs/big-data/enhanced-search-with-match-highlights-and-explanations-in-amazon-sagemaker/).
### 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](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/data-onboarding.html).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/09/sagemaker-unified-studio-vs-code/), [User Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/local-ide-support.html), [Admin Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/local-ide-support.html), and [Blog post](https://aws.amazon.com/blogs/big-data/accelerate-your-data-and-ai-workflows-by-connecting-to-amazon-sagemaker-unified-studio-from-visual-studio-code/).
**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](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/data-onboarding.html).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/09/improved-ai-assistance-amazon-sagemaker-unified-studio/), [Admin Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/amazonq.html), and [Blog Post](https://aws.amazon.com/blogs/big-data/introducing-enhanced-ai-assistance-in-amazon-sagemaker-unified-studio-agentic-chat-amazon-q-developer-cli-and-mcp-integration/).
**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](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/custom-blueprints.html)). For more information, see [What’s New Post](https://aws.amazon.com/about-aws/whats-new/2025/09/amazon-sagemaker-unified-studio-custom-blueprints/), [Admin Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/custom-blueprint.html)), and [Blog Post](https://aws.amazon.com/blogs/big-data/tailor-amazon-sagemaker-unified-studio-project-environments-to-your-needs-using-custom-blueprints/).
**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](https://aws.amazon.com/blogs/big-data/introducing-restricted-classification-terms-for-governed-classification-in-amazon-sagemaker-catalog/)).
## 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](https://aws.amazon.com/about-aws/whats-new/2025/08/amazon-sagemaker-unified-studio-s3-file-sharing-options/) [User Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/storage.html) and [Admin Guide](https://docs.aws.amazon.com/ssagemaker-unified-studio/latest/adminguide/smus-admin-storage-guide.html).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/07/amazon-sagemaker-streamlines-s3-tables-workflow/) and [User Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/s3-tables-integration.html).
### 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](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/s3-tables-integration.html).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/06/amazon-sagemaker-custom-transport-openlineage-community-lineage-capabilities/) and [User Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/datazone-support-matrix.html).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/07/amazon-sagemaker-integration-quicksight/), [User Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/quicksight-integration.html), [Admin Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/amazon-quicksight.html), [AWS News Blog](https://aws.amazon.com/blogs/aws/streamline-the-path-from-data-to-insights-with-new-amazon-sagemaker-capabilities/), and the [AWS Big Data Blog](https://aws.amazon.com/blogs/big-data/unifying-data-insights-with-amazon-quicksight-and-amazon-sagemaker/).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/07/amazon-sagemaker-data-management-lakehouse-onboarding/), [Admin Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/data-onboarding.html), and [AWS News Blog](https://aws.amazon.com/blogs/aws/streamline-the-path-from-data-to-insights-with-new-amazon-sagemaker-capabilities/).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/07/amazon-sagemaker-catalog-s3-general-purpose-buckets/) and [User Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/data-s3-publish.html).
### 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](https://aws.amazon.com/blogs/big-data/unifying-metadata-governance-across-amazon-sagemaker-and-collibra/).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/07/amazon-sagemaker-visual-workflows-builder/), [User Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/workflow-orchestration.html), and [Blog Post](https://aws.amazon.com/blogs/big-data/orchestrate-data-processing-jobs-querybooks-and-notebooks-using-visual-workflow-experience-in-amazon-sagemaker/).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/07/amazon-sagemaker-data-processing-jobs/), [User Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/sagemaker-unified-studio-jobs.html), and [Blog Post](https://aws.amazon.com/blogs/big-data/introducing-jobs-in-amazon-sagemaker/).
### 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](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/adding-existing-s3-data.html).
### 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](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/getting-started-sagemaker-gdc-s3.html).
### 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](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/using-project-tip.html) and [Admin Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/trusted-identity-propagation.html).
### 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](https://docs.aws.amazon.com/next-generation-sagemaker/latest/userguide/what-is-sagemaker.html).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/06/amazon-sagemaker-automatic-synchronization-git-s3/), [Admin Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/blueprints.html#manage-tooling-blueprint), and [AWS Big Data Blog](https://aws.amazon.com/blogs/big-data/unified-scheduling-for-visual-etl-flows-and-query-books-in-amazon-sagemaker-unified-studio/).
## 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](https://github.com/aws/Unified-Studio-for-Amazon-Sagemaker/tree/main/cloudformation) to setup and create a new Amazon SageMaker Unified domain and project.
### 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](https://aws.amazon.com/about-aws/whats-new/2025/06/amazon-datazone-upgrade-domain-sagemaker/) and [Upgrade Amazon DataZone domains to Amazon SageMaker unified domains](https://docs.aws.amazon.com/datazone/latest/userguide/upgrade-domain.html).
## 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](https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-790-release.html#emr-790-relnotes) and [Configure Amazon EMR cluster logging and debugging](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-debugging.html#emr-plan-debugging-logs-archive).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/05/code-editor-vs-code-open-source-sagemaker-unified-studio/) and [Using the Code Editor IDE in Amazon SageMaker Unified Studio](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/code-editor.html).
### 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](https://aws.amazon.com/about-aws/whats-new/2025/05/amazon-sagemaker-unified-studio-byoi/) and [Bring your own image (BYOI)](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/byoi.html).