Amazon EMR 7.14 is now available with new features across Amazon EMR on EC2, Amazon EMR on EKS, and Amazon EMR Serverless, along with version upgrades for additional applications. This release also upgrades Apache Spark to 3.5.8 and Apache Iceberg to 1.10.1. Apache Iceberg materialized views now refresh faster on tables with updates and deletes, using change data capture to read only the data files affected by a change. On Amazon EMR on EKS, clusters now support Spark Connect endpoints for interactive Spark sessions with token-based authentication, and can run workloads on IPv6 Amazon EKS clusters. On Amazon EMR Serverless, the storage limit for Spark jobs increases from 200 GiB to 1 TiB, giving more room for shuffle data. Amazon EMR 7.14 is available in all AWS Regions where Amazon EMR is available. To learn more, visit the Amazon EMR 7.14 Release Guide, or get started by creating a cluster from the Amazon EMR console.

Amazon EMR introduces Long Term Support (LTS) releases, starting with emr-spark-8.1.0 and Apache Spark 4.1. With LTS, designated versions of the AWS runtime for Apache Spark receive 36 months of support. Amazon EMR provides LTS releases with fixes for critical and high severity security, bug, and data-corruption issues, subject to availability. LTS helps you run production Spark workloads on one release longer and upgrade on your own schedule, at no additional cost. This release adds full support for Apache Iceberg v3, bringing new geospatial, high-precision timestamp, and schema-evolution capabilities to your tables. Spark SQL queries can reference catalogs by name, including cross-account and Amazon S3 Tables catalogs, and automatically detect Apache Iceberg, Delta Lake, and Apache Hudi table formats, without registering each catalog in your Spark configuration. Fine-grained access control now covers more Apache Iceberg operations and the Delta Lake VACUUM operation, so you can apply column-level and row-level permissions to a wider set of jobs. Amazon EMR on EKS clusters now support Spark Connect endpoints with token-based authentication. emr-spark-8.1.0 is available in all AWS Regions where Amazon EMR is available, across Amazon EMR on EC2, Amazon EMR on EKS, and Amazon EMR Serverless. To learn more, see the https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-spark810-release.html and the https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-standard-support.html policy. To get started, create an EMR cluster or application with emr-spark-8.1.0 from the AWS Management Console, or use the https://docs.aws.amazon.com/emr/latest/ReleaseGuide/spark-upgrades.html to move existing applications to the release.

Amazon EMR on EKS now supports interactive Apache Spark sessions with Spark Connect. Data engineers and data scientists can develop and debug Apache Spark applications interactively from managed notebooks in Amazon SageMaker Unified Studio and their own IDEs, such as Jupyter and Visual Studio Code, with Spark running on the Amazon EKS clusters they already operate.   An interactive session provides a persistent Spark context that spans across cells and scripts, letting you blend local Python code execution with remote Spark operations. Spark Connect's client-server architecture decouples your application client from the Spark driver and allows you to maintain your preferred development environment and tooling while Spark runs on your Amazon EKS cluster. This architecture supports workflows including ad hoc data exploration and incremental PySpark job development before deploying to production. Each session runs as pods on a virtual cluster, secured with your AWS Identity and Access Management (IAM) execution role and tagged by project and user. Spark Connect on Amazon EMR on EKS is available with EMR release 7.14 (Apache Spark 3.5) and emr-spark-8.1.0 (Apache Spark 4.1), in all AWS Commercial Regions. The Amazon SageMaker Unified Studio experience is available in supported AWS Regions. To get started, visit the Spark Connect on Amazon EMR on EKS documentation or the Amazon SageMaker Unified Studio Getting Started guide.

Amazon EMR on EKS now supports interactive Apache Spark sessions with Spark Connect. Data engineers and data scientists can develop and debug Apache Spark applications interactively from managed notebooks in Amazon SageMaker Unified Studio and their own IDEs, such as Jupyter and Visual Studio Code, with Spark running on the Amazon EKS clusters they already operate.   An interactive session provides a persistent Spark context that spans across cells and scripts, letting you blend local Python code execution with remote Spark operations. Spark Connect's client-server architecture decouples your application client from the Spark driver and allows you to maintain your preferred development environment and tooling while Spark runs on your Amazon EKS cluster. This architecture supports workflows including ad hoc data exploration and incremental PySpark job development before deploying to production. Each session runs as pods on a virtual cluster, secured with your AWS Identity and Access Management (IAM) execution role and tagged by project and user. Spark Connect on Amazon EMR on EKS is available with EMR release 7.14 (Apache Spark 3.5) and emr-spark-8.1.0 (Apache Spark 4.1), in all AWS Commercial Regions. The Amazon SageMaker Unified Studio experience is available in https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html. To get started, visit the https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/emr-eks-spark-connect.html or the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/notebooks-spark-connect.html#spark-connect-emr-eks

Today, AWS announces that Amazon EMR on EKS now supports running workloads on IPv6 Amazon Elastic Kubernetes Service (Amazon EKS) clusters. Amazon EMR on EKS enables data platform and analytics teams to run open-source big data frameworks such as Apache Spark and Apache Flink on Amazon EKS. With IPv6 support, teams operating at scale can now run these workloads on IPv6 Amazon EKS clusters, giving them access to the vastly larger address space of IPv6 as their analytics workloads grow. You can now scale large Spark and Flink workloads using the expanded IPv6 address space, which removes the need for IPv4 conservation workarounds such as secondary CIDR ranges or prefix delegation. High-executor jobs, such as 500-executor Spark jobs, can run concurrently without planning around address limits. To submit workloads, use StartJobRun, Spark Connect Interactive Endpoints, and Amazon SageMaker Unified Studio with no additional configuration, while the Flink, Livy, and Spark Operators are also supported. This capability is available at no additional cost, starting with Amazon EMR releases emr-7.14.0 and emr-spark-8.0.0. IPv6 cluster support is available in all AWS Regions where Amazon EMR on EKS and IPv6 Amazon EKS clusters are available. Amazon EMR on EKS IPv6 cluster support includes detailed setup instructions and documentation on supported submission models. Visit the Amazon EMR on EKS product page and the IPv6 documentation to learn more.

Today, AWS announces that Amazon EMR on EKS now supports running workloads on IPv6 Amazon Elastic Kubernetes Service (Amazon EKS) clusters. Amazon EMR on EKS enables data platform and analytics teams to run open-source big data frameworks such as Apache Spark and Apache Flink on Amazon EKS. With IPv6 support, teams operating at scale can now run these workloads on IPv6 Amazon EKS clusters, giving them access to the vastly larger address space of IPv6 as their analytics workloads grow. You can now scale large Spark and Flink workloads using the expanded IPv6 address space, which removes the need for IPv4 conservation workarounds such as secondary CIDR ranges or prefix delegation. High-executor jobs, such as 500-executor Spark jobs, can run concurrently without planning around address limits. To submit workloads, use StartJobRun, Spark Connect Interactive Endpoints, and Amazon SageMaker Unified Studio with no additional configuration, while the Flink, Livy, and Spark Operators are also supported. This capability is available at no additional cost, starting with Amazon EMR releases emr-7.14.0 and emr-spark-8.0.0. IPv6 cluster support is available in all AWS Regions where Amazon EMR on EKS and IPv6 Amazon EKS clusters are available. Amazon EMR on EKS IPv6 cluster support includes detailed setup instructions and documentation on supported submission models. Visit the https://aws.amazon.com/emr/features/eks/ and the https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/emr-eks-ipv6.html to learn more. 

Amazon EMR introduces Long Term Support (LTS) releases, starting with emr-spark-8.1.0 and Apache Spark 4.1. With LTS, designated versions of the AWS runtime for Apache Spark receive 36 months of support. Amazon EMR provides LTS releases with fixes for critical and high severity security, bug, and data-corruption issues, subject to availability. LTS helps you run production Spark workloads on one release longer and upgrade on your own schedule, at no additional cost. This release adds full support for Apache Iceberg v3, bringing new geospatial, high-precision timestamp, and schema-evolution capabilities to your tables. Spark SQL queries can reference catalogs by name, including cross-account and Amazon S3 Tables catalogs, and automatically detect Apache Iceberg, Delta Lake, and Apache Hudi table formats, without registering each catalog in your Spark configuration. Fine-grained access control now covers more Apache Iceberg operations and the Delta Lake VACUUM operation, so you can apply column-level and row-level permissions to a wider set of jobs. Amazon EMR on EKS clusters now support Spark Connect endpoints with token-based authentication. emr-spark-8.1.0 is available in all AWS Regions where Amazon EMR is available, across Amazon EMR on EC2, Amazon EMR on EKS, and Amazon EMR Serverless. To learn more, see the emr-spark-8.1.0 release notes and the Amazon EMR standard support policy. To get started, create an EMR cluster or application with emr-spark-8.1.0 from the AWS Management Console, or use the Apache Spark Upgrade Agent for Amazon EMR to move existing applications to the release.

Amazon EMR 7.14 is now available with new features across Amazon EMR on EC2, Amazon EMR on EKS, and Amazon EMR Serverless, along with version upgrades for additional applications. This release also upgrades Apache Spark to 3.5.8 and Apache Iceberg to 1.10.1. Apache Iceberg materialized views now refresh faster on tables with updates and deletes, using change data capture to read only the data files affected by a change. On Amazon EMR on EKS, clusters now support Spark Connect endpoints for interactive Spark sessions with token-based authentication, and can run workloads on IPv6 Amazon EKS clusters. On Amazon EMR Serverless, the storage limit for Spark jobs increases from 200 GiB to 1 TiB, giving more room for shuffle data. Amazon EMR 7.14 is available in all AWS Regions where Amazon EMR is available. To learn more, visit the https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-7140-release.html, or get started by creating a cluster from the https://console.aws.amazon.com/emr.

New capabilities help optimize application performance, analyze unlimited prefixes, and simplify metrics analysis through S3 Tables integration.

Amazon EMR on EKS now supports the Apache Spark troubleshooting agent. Data engineers can now diagnose EMR on EKS job failures through natural language, receiving automated root cause analysis and PySpark code recommendations without manually navigating distributed logs and Spark History Server data. The agent analyzes Spark History Server data, distributed executor logs, and cluster configurations to identify issues such as memory errors, data skew, resource contention, and connectivity failures. With this launch, the Spark troubleshooting agent now covers all EMR deployment options: EMR on EC2, EMR Serverless, and EMR on EKS. The agent is accessible directly from the EMR on EKS console through a "Troubleshoot with AI" option on failed jobs. Additionally, the agent is available through MCP (Model Context Protocol) using any compatible AI coding agent, including Kiro, Claude Code, and Cursor. All operations are read-only, authenticated with IAM roles, and logged in AWS CloudTrail. The Spark troubleshooting agent for Amazon EMR on EKS is available in all AWS Regions where the SageMaker Unified Studio is available. To get started, go to EMR on EKS console, or set up the MCP server in your preferred AI coding agent. For detailed guidance, see the EMR troubleshooting agent documentation.

Amazon EMR now supports Apache Spark 4.0.2 across all three deployment models. With Spark 4.0.2, you can build and maintain data pipelines more easily with ANSI SQL and VARIANT data types, enforce fine-grained access control (FGAC) at the row level or column level, strengthen compliance and governance frameworks with Apache Iceberg v3 table format, and deploy new real-time applications faster with enhanced streaming capabilities. With Spark 4.0.2, you can build data pipelines, making data engineering accessible to a broader range of users through standard ANSI SQL support, eliminating the need to learn Spark-specific syntax. Spark 4.0.2 natively supports JSON and semi-structured data through VARIANT data types, providing flexibility for handling diverse data formats. You can enforce fine-grained access control (FGAC) on both read and write operations for AWS Lake Formation registered tables in your Apache Spark jobs. Building on these security capabilities, Apache Iceberg v3 table format provides stronger transaction guarantees and tracks data lineage, creating the audit trails required for regulatory compliance. Enhanced streaming controls simplify management of complex stateful operations and improve monitoring, enabling you to deploy real-time applications for fraud detection, personalization, and other time-sensitive use cases faster. Apache Spark 4.0.2 is available in all regions where EMR is available. If you are upgrading your existing EMR application, you can use Apache Spark upgrade agent to accelerate your upgrades. To learn more about Apache Spark 4.0.2 on Amazon EMR, visit the Amazon EMR release notes, or get started by creating an EMR application with Spark 4.0.2 from the AWS Management Console.

Amazon EMR Serverless is now a FedRAMP High authorized service in the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. Federal agencies, public sector organizations and other enterprises with FedRAMP High compliance requirements can now leverage EMR Serverless to run Apache Spark and Hive workloads. Amazon EMR Serverless is a serverless option that makes it simple for data analysts and engineers to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers. The Federal Risk and Authorization Management Program (FedRAMP) is a US government-wide program that delivers a standard approach to the security assessment, authorization, and continuous monitoring for cloud products and services. To get started with Amazon EMR Serverless, visit the User Guide.

AWS now supports deletion vectors and row lineage as defined in the Apache Iceberg Version 3 (V3) specification. These new features are available with Apache Spark on Amazon EMR 7.12, AWS Glue, Amazon SageMaker notebooks, Amazon S3 Tables, and the AWS Glue Data Catalog. These Iceberg V3 capabilities help customers build petabyte-scale data lakes with improved performance for data modifications and functionality to easily track changed records. Deletion vectors write optimized delete files that speed up data pipelines and reduce data compaction costs. Row lineage provides metadata fields on each record to track changes with a simple SQL query, eliminating the computational expense of finding small changes in large tables. Get started creating V3 tables by setting the table property to 'format-version = 3' in the CREATE TABLE command in Spark or a SageMaker notebook. To upgrade existing tables, simply update the table property in metadata with the new format version. When you do this, AWS query engines that support V3 will automatically begin to use deletion vectors and row lineage. Iceberg V3 deletion vectors and row lineage are now available in all AWS Regions where each respective service/feature—Amazon EMR, AWS Glue, SageMaker notebooks, S3 Tables, and AWS Glue Data Catalog—is supported. To learn more about AWS support for Iceberg V3, visit Apache Iceberg V3 on AWS, and read the blog post.

Amazon EMR Serverless now supports Apache Spark 4.0.1 (preview). With Spark 4.0.1, you can build and maintain data pipelines more easily with ANSI SQL and VARIANT data types, strengthen compliance and governance frameworks with Apache Iceberg v3 table format, and deploy new real-time applications faster with enhanced streaming capabilities. This enables your teams to reduce technical debt and iterate more quickly, while ensuring data accuracy and consistency. With Spark 4.0.1, you can build data pipelines with standard ANSI SQL, making it accessible to a larger set of users who don't know programming languages like Python or Scala. Spark 4.0.1 natively supports JSON and semi-structured data through VARIANT data types, providing flexibility for handling diverse data formats. You can strengthen compliance and governance through Apache Iceberg v3 table format, which provides transaction guarantees and tracks how your data changes over time, creating the audit trails you need for regulatory requirements. You can deploy real-time applications faster with improved streaming controls that let you manage complex stateful operations and monitor streaming jobs more easily. With this capability, you can support use cases like fraud detection and real-time personalization. Apache Spark 4.0.1 is available in preview in all regions where EMR Serverless is available, excluding China and AWS GovCloud (US) regions. To learn more about Apache Spark 4.0.1 on Amazon EMR, visit the Amazon EMR Serverless release notes, or get started by creating an EMR application with Spark 4.0.1 from the AWS Management Console.

Amazon EMR 7.12 is now available featuring the new Apache Iceberg v3 table format with Apache Iceberg 1.10. This release enables you to reduce costs when deleting data, strengthen governance and compliance through better tracking for row level changes, and enhance data security with more granular data access control. With Iceberg v3, you can delete data cost-effectively because Iceberg v3 marks deleted rows without rewriting entire files - speeding up your data pipelines while reducing storage costs. You get better governance and compliance capabilities through automatic tracking of every row’s creation and modification history, creating the audit trails needed for regulatory requirements and change data capture. You can enhance data security with table-level encryption, helping you meet privacy regulations for your most sensitive data. With Apache Spark 3.5.6 included in this release, you can leverage these Iceberg 1.10 capabilities for building robust data lakehouse architectures on Amazon S3. This release also includes support for data governance operations across your Iceberg tables using AWS Lake Formation. In addition, this release also includes Apache Trino 476. Amazon EMR 7.12 is available in all AWS Regions that support Amazon EMR. To learn more about Amazon EMR 7.12 release, visit the Amazon EMR 7.12 release documentation.

Amazon EMR on EC2 announces two significant enhancements for governance: Apache Spark native fine-grained access control (FGAC) via AWS Lake Formation, and support for AWS Glue Data Catalog views. These features allow organizations to improve data security, simplify access management, and enhance data sharing capabilities across their analytics environments. The Apache Spark native FGAC implementation allows customers to define granular access policies once in AWS Lake Formation and apply them consistently across EMR clusters. This reduces security risks and administrative overhead while providing a unified approach to data governance. Customers can now use familiar Lake Formation grant and revoke statements to manage access controls for their Spark jobs and interactive sessions on EMR on EC2, similar to how this works for other AWS analytics services. AWS Glue Data Catalog views enables customers to create, manage, and query multi-engine SQL views across AWS regions, accounts, and organizations. This feature allows administrators to create views from Spark jobs that can be queried from multiple engines, while controlling data access through Lake Formation permissions. These permissions include named resource grants, data filters, and tags, with all access requests automatically logged in AWS CloudTrail for comprehensive auditing. Apache Spark native FGAC and Glue Data Catalog view features are available with Amazon EMR release 7.10 in all AWS Regions where EMR on EC2 is available. To learn more, visit Using AWS Lake Formation with Amazon EMR and Working with AWS Glue Data Catalog Views in the Amazon EMR documentation.

AWS announces Amazon EMR S3A, a new Amazon S3 connector that optimizes performance for Apache Hadoop, Apache Spark, and Apache Hive workloads on Amazon EMR. This new connector enhances the open source S3A architecture with AWS-specific optimizations to help organizations process large-scale data more efficiently. With direct integration support for S3 Express One Zone, S3 Glacier, and AWS Outposts, EMR S3A helps customers leverage different storage options in AWS to optimize both data access speed and storage cost on their EMR workloads. Additionally, the EMR S3A connector delivers advanced security features and performance capabilities that extend beyond open source S3A. Key improvements include Apache Spark built-in fine-grained access control support, enhanced S3A credentials resolver, MagicCommitter V2 for optimized file writes, and accelerated S3 prefix listing for columnar file formats. These enhancements are available starting with EMR release 7.10 and maintain compatibility with existing applications. The Amazon EMR S3A connector is available in all AWS Regions where Amazon EMR is available and comes pre-configured with Amazon EMR release version 7.10 and later. To learn more about Amazon EMR S3A, see the Amazon EMR documentation.

Today, Amazon EMR on EKS announces support for Service Quotas, improving visibility and control over EMR on EKS quotas. Previously, to request an increase for EMR on EKS quotas, such as maximum number of StartJobRun API calls per second, customers had to open a support ticket and wait for the support team to process the increase. Now, customers can view and manage their EMR on EKS quota limits directly in the https://us-east-1.console.aws.amazon.com/servicequotas/home/services/emr-containers/quotas. This enables automated limit increase approvals for eligible requests, improving response times and reducing the number of support tickets. Customers can also set up Amazon CloudWatch alarms to get automatically notified when their usage reaches a certain percentage of a maximum quota. Amazon EMR on EKS support for Service Quotas is available in all Regions where Amazon EMR on EKS is currently available. To get started, visit the https://docs.aws.amazon.com/servicequotas/latest/userguide/intro.html.

Amazon EMR Serverless is now a https://aws.amazon.com/compliance/fedramp/ High authorized service in the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. Federal agencies, public sector organizations and other enterprises with FedRAMP High compliance requirements can now leverage EMR Serverless to run Apache Spark and Hive workloads. Amazon EMR Serverless is a serverless option that makes it simple for data analysts and engineers to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers. The Federal Risk and Authorization Management Program (FedRAMP) is a US government-wide program that delivers a standard approach to the security assessment, authorization, and continuous monitoring for cloud products and services. To get started with Amazon EMR Serverless, visit the https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/getting-started.html.  

Amazon EMR 7.12 is now available featuring the new Apache Iceberg v3 table format with Apache Iceberg 1.10. This release enables you to reduce costs when deleting data, strengthen governance and compliance through better tracking for row level changes, and enhance data security with more granular data access control. With Iceberg v3, you can delete data cost-effectively because Iceberg v3 marks deleted rows without rewriting entire files - speeding up your data pipelines while reducing storage costs. You get better governance and compliance capabilities through automatic tracking of every row’s creation and modification history, creating the audit trails needed for regulatory requirements and change data capture. You can enhance data security with table-level encryption, helping you meet privacy regulations for your most sensitive data. With Apache Spark 3.5.6 included in this release, you can leverage these Iceberg 1.10 capabilities for building robust data lakehouse architectures on Amazon S3. This release also includes support for data governance operations across your Iceberg tables using AWS Lake Formation. In addition, this release also includes Apache Trino 476. Amazon EMR 7.12 is available in all AWS Regions that support Amazon EMR. To learn more about Amazon EMR 7.12 release, visit the https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-7120-release.html. 

Amazon EMR and AWS Glue now provide comprehensive audit context support for AWS Lake Formation credential vending APIs and AWS Glue Data Catalog GetTable and GetTables API calls. This auditing capability helps you maintain compliance with regulatory frameworks, including the Digital Markets Act (DMA) and data protection regulations. The feature is enabled by default, offering seamless integration into existing workflows while strengthening security and compliance monitoring across your data lake infrastructure. You can view this audit context information in AWS CloudTrail logs, enabling enhanced security auditing, regulatory compliance, and improved troubleshooting for EMR for Apache Spark native fine-grained access control (FGAC) and full table access jobs. The audit logging feature automatically records the platform type (EMR-EC2, EMR on EKS, EMR Serverless, or AWS Glue) and its corresponding identifiers like such as Cluster ID, Step ID, Job Run ID, and Virtual Cluster ID. This enables security teams to track and correlate API calls from individual Spark jobs, streamline compliance reporting, and analyze historical data access patterns. Additionally, data engineers can quickly troubleshoot access-related issues by connecting them to specific job executions, resolve FGAC permission challenges, and monitor access patterns across different compute platforms. This feature is available in all AWS Regions that support Amazon EMR, AWS Glue, and AWS Lake Formation, requiring EMR version 7.12+ or AWS Glue version 5.1+.

AWS announces Amazon EMR S3A, a new Amazon S3 connector that optimizes performance for Apache Hadoop, Apache Spark, and Apache Hive workloads on https://aws.amazon.com/emr/. This new connector enhances the open source S3A architecture with AWS-specific optimizations to help organizations process large-scale data more efficiently. With direct integration support for S3 Express One Zone, S3 Glacier, and AWS Outposts, EMR S3A helps customers leverage different storage options in AWS to optimize both data access speed and storage cost on their EMR workloads. Additionally, the EMR S3A connector delivers advanced security features and performance capabilities that extend beyond open source S3A. Key improvements include Apache Spark built-in fine-grained access control support, enhanced S3A credentials resolver, MagicCommitter V2 for optimized file writes, and accelerated S3 prefix listing for columnar file formats. These enhancements are available starting with EMR release 7.10 and maintain compatibility with existing applications. The Amazon EMR S3A connector is available in all AWS Regions where Amazon EMR is available and comes pre-configured with Amazon EMR release version 7.10 and later. To learn more about Amazon EMR S3A, see the https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-s3a-file.html.

Amazon EMR on EC2 announces two significant enhancements for governance: Apache Spark native fine-grained access control (FGAC) via AWS Lake Formation, and support for AWS Glue Data Catalog views. These features allow organizations to improve data security, simplify access management, and enhance data sharing capabilities across their analytics environments. The Apache Spark native FGAC implementation allows customers to define granular access policies once in AWS Lake Formation and apply them consistently across EMR clusters. This reduces security risks and administrative overhead while providing a unified approach to data governance. Customers can now use familiar Lake Formation grant and revoke statements to manage access controls for their Spark jobs and interactive sessions on EMR on EC2, similar to how this works for other AWS analytics services. AWS Glue Data Catalog views enables customers to create, manage, and query multi-engine SQL views across AWS regions, accounts, and organizations. This feature allows administrators to create views from Spark jobs that can be queried from multiple engines, while controlling data access through Lake Formation permissions. These permissions include named resource grants, data filters, and tags, with all access requests automatically logged in AWS CloudTrail for comprehensive auditing. Apache Spark native FGAC and Glue Data Catalog view features are available with Amazon EMR release 7.10 in all AWS Regions where EMR on EC2 is available. To learn more, visit https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-lake-formation.html and https://docs.aws.amazon.com/emr/latest/ManagementGuide/SECTION-jobs-glue-data-catalog-views-ec2.htmlin the Amazon EMR documentation.